Online learning resource recommendation method based on multi-similarity metric optimization under the COVID-19 epidemic.

With the continuous COVID-19 pneumonia epidemic, online learning has become a normal choice for many learners. However, the problems of information overload and knowledge maze have been aggravated in the process of online learning. A learning resource recommendation method based on multi similarity measure optimization is proposed in this paper. We optimize the user score similarity by introducing information entropy, and use particle swarm optimization algorithm to determine the comprehensive similarity weight, and determine the nearest neighbor user with both score similarity and interest similarity through secondary screening in this method. The ultimate goal is to improve the accuracy of recommendation results, and help learners learn more effectively. We conduct experiments on public data sets. The experimental results show that the algorithm in this paper can significantly improve the recommendation accuracy on the basis of maintaining a stable recommendation coverage.

Risk assessment using information entropy

Risk assessment, particularly when using simulations, requires that the analyst develops estimates of expected, low, and high values for inputs. Mean and standard deviation are often used to assess the variability of metrics, assuming that the underlying distribution is normal. However, it is increasingly realized that non-normal distributions are common and important. If data are available, it is simple and straightforward to check this assumption by computing higher order moments.Claude Shannon [1], [2] proposed that the information entropy for a set of N discrete events can be measured by (Formula Presented) E. T. Jaynes [3] proposed that, if data is available, information entropy can be maximized using Lagrangian multipliers and that the resulting probability distribution maximizes the uncertainty of that distribution given the data.In order to use entropy maximization, it is required to define constraints such that Σpi = 1, plus constraints on the mean, variance, skew, kurtosis, and other moments. This problem does not have a closed form solution but can be solved iteratively in a spreadsheet.The problem can be set up as follows for mean bar x and variance s2: (Formula Presented) This basic formulation models the normal distribution. The importance of non-normality can be estimated by adding higher order moments as desired. For n ≥ 3, constraints can be added using: (Formula Presented) where Mn is the computed nth moment of the data set.Differentiating ∂H/∂pi = 0 maximizes information entropy, and the resulting probability distribution has the most uncertainty given the observed data.This suggests that it is possible to develop an estimate of the distribution where some values are underrepresented in the sample. It further suggests that unusual or atypical results can be better estimated.This paper uses the method of maximizing entropy to model observed data and will study two time series applications. One problem of interest is sequential acquisition of data. For example, time to failure for a device may be a metric of concern. A maximum entropy model provides an empirical estimate of the distribution of this metric. A second problem of interest is forecasting the distribution of a metric at some point in the future. This applies to supply chain management. Project sponsors prepare cost and schedule estimates well in advance of placing the orders for the materials used in those projects. Management reserves for cost and schedule are typically set by subject matter experts, and recent experience (e.g., supply chain disruptions due to the COVID19 pandemic) may overemphasize current data when developing risk assessments. This approach offers a datadriven way to empirically develop risk assessments. © 2023 IEEE.

Gta-Ids: Game Theoretic Approach to Enhance Ids Detection in Cloud Environment

The Internet of Things (IoT) industry is growing with the high-quality collaboration with Cloud Computing. The data generated by the IoT devices is quite large which can be efficiently stored and processed by the cloud. Further, the scenario like COVID-19 led to an unexpected flood of IoT devices on enabling networks to facilitate online services, which increases the potential threats to the companies fighting to remain operational during the crises. Still, the problem with the IoT devices is their weak security implications because vendors prioritize other factors like energy-saving and efficiency at the cost of security. The Attacker can send malicious requests through the vulnerable IoT device to the network and exploit the cloud in various ways. So, to address this issue, a Game Theoretic Approach to enhance IDS detection (GTA-IDS) in Cloud Environment has been devised that helps the Defender system to be more efficient, accurate in decision-making and save energy. The algorithm based on relative information entropy has been developed to defend against such attacks. The Bayesian Nash Equilibrium (BNE) has been used to make the Defender's strategies and perform actions to maximize its payoffs. The model has been tested on the NSL-KDD dataset and the results have been compared to the existing techniques. The results show that despite efforts made by the Attacker, the Defender always gets a better gain and ultimately eliminates the attack.

Evaluation and prediction of high-quality development in China: A time-spatial analysis from Hubei province

High-quality sustainable development is the common goal pursued by all countries in the world. China's high-quality development (HQD) includes five concepts of "innovation, coordination, green, opening-up, and sharing ". In this context, we established an evaluation system that included these five fundamental characteristics, used the comprehensive entropy method and BP neural network to evaluate and predict the high-quality development of Hubei Province in China, and conducted a spatiotemporal deductive analysis. The study found that: 1) Economic growth still has an important impact on HQD, for all the five main indicators, "opening-up " and "innovation " have the highest impact weights, which are 0.379 and 0.278, respectively, while the proportions of coordination and sharing are both less than 0.1. 2) There are huge differences in the level of high-quality development between regions in Hubei Province. From 2010 to 2020, the average comprehensive index of Wuhan City was greater than 0.5, which is 7 times that of the second Xiangyang City, and 46 times that of the last Shennongjia district. 3) In the past few years, the overall high-quality development of Hubei Province has shown a fluctuating upward trend. However, due to the impact of COVID-19, during the following years, its comprehensive development index will decline by an average of 5% annually, but starting from 2022, it will gradually increase. As a result, tailored and coordinated sustainable environmental policies of integrating institutional and open-market measures should be provided.

Hospitality education evolution observed in online learning dataset.

The research article "Realtime Online Courses Mutated amid the COVID-19 Pandemic: Empirical Study in Hospitality Program" aims to explore the education evolution amid the pandemic [1]. Data were collected by recruiting 956 respondents; 926 responses were adopted after the valid screening through a cooperative survey company. A random sampling of targeted groups was required when outsourcing the data collection to the survey company Wenjuanxing, a platform with a majority population database providing functions equivalent to Amazon Mechanical Turk [2]. We asked the company to deliver the designed questionnaire to teachers and students in hospitality programs. The reliability and validity of all constructs showed that the questionnaire is proper for measurement [3]. Data analysis applied the structural equation model with Mplus to examine the CFA model and research hypothesis. Structural equation modeling was applied to conduct the hypotheses test and model fitness through the statistical tool Mplus. Results imply that the data is suitable for conducting replication studies.

Realtime online courses mutated amid the COVID-19 pandemic: Empirical study in hospitality program.

Real-time online courses (RTOCs), a new online learning mode, have been developed because of a longitudinal suspension of classes amid the COVID-19 pandemic worldwide. We explore an information model to review the learning process and outcomes of RTOCs, which conducted educational activities via social media. Results show that social media can be a potent mediation factor with the moderation of structural differentiation to facilitate online learning outcomes. Conclusions imply that the life-changing impact of COVID-19 has caused an evolutionary online education mode that can be hybridized with face-to-face education and massive open online courses to flourish education approaches and pedagogies.

An Evaluation of the Petroleum Investment Environment in African Oil-Producing Countries Based on Combination Weighting and Uncertainty Measure Theory

Recognizing that the evaluation of the overseas petroleum investment environment is affected by many uncertain factors and that there are problems with current evaluation methods, this paper proposes a mathematical evaluation model of an overseas oil resources investment environment, based on a combination of the weighting and uncertainty measure theory. Combining international investment environment theory with the characteristics of the petroleum industry, this paper establishes an evaluation index system for the overseas petroleum investment environment and the linear uncertainty measure function of each index. Using the subjective weight obtained using an analytic hierarchy process together with the objective weight obtained using the entropy weight method, the optimal weight of each evaluation index was obtained using minimum relative information entropy. A multi-index evaluation matrix of the top 12 oil-producing countries in Africa was calculated. Finally, the credible degree recognition criterion was used to judge the order and level of the oil investment environment. This model provides an effective method for the evaluation of the overseas petroleum investment environment. The results show that Nigeria and Angola have the best investment climate, followed by Algeria, Egypt, and Libya. In general, Africa is an important strategic partner of China and is rich in oil resources. Although Africa’s oil industry is fraught with complex challenges and headwinds, challenges also present opportunities.

Dynamic Crowd Accident-Risk Assessment Based on Internal Energy and Information Entropy for Large-Scale Crowd Flow Considering COVID-19 Epidemic

With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m-2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m-1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54%under this epidemic. IEEE

Analyzing Communication Broadcasting in the Digital Space

This paper aims to understand complex social events that arise when communicating general concepts in the digital space. Today, we get informed through many different channels, at different times of the day, in different contexts, and on many different devices. In addition to that, more complexity is added by the bidirectional nature of the communication itself. People today react very quickly to specific topics through various means such as rating, sharing, commenting, tagging, icons, tweeting, etc. Such activities generate additional metadata to the information itself which become part of the original message. When planning proper communication we should consider all this. In such a complicated environment, the likelihood of a message’s real meaning being received in a distorted or confused way is very high. However, as we have seen recently during the Covid-19 pandemic, at times, there is the need to communicate something, somewhat complicated in nature, while we need to make sure citizens fully understand the actual terms and meaning of the communication. This was the case faced by many governments worldwide when informing their population on the rules of conduct during the various lockdown periods. We analyzed trends and structure of social network data generated as a reaction to those official communications in Italy. Our goal is to derive a model to estimate whether the communication intended by the government was properly understood by the large population. We discovered some regularities in social media generated data related to “poorly” communicated issues. We believe it is possible to derive a model to measure how well the recipients grasp a specific topic. And this can be used to trigger real-time alerts when the need for clarification arises. © 2022, Springer Nature Switzerland AG.

Evolution of Urban Construction Land Structure Based on Information Entropy and Shift-Share Model: An Empirical Study on Beijing-Tianjin-Hebei Urban Agglomeration in China

Urban agglomerations are important carriers of the current world economic development and economic center of gravity shift, while urban construction land structure reflects and influences the functions and development directions of urban agglomerations and cities within them. It is significant to study the characteristics of urban construction land structure in urban agglomerations. Based on information entropy model and shift-share model, this study discusses and analyzes the evolution characteristics and spatial allocation differences of urban construction land structure in Beijing-Tianjin-Hebei urban agglomeration, and simulates the spatial allocation differences with the help of GIS technology. The empirical research results show that from, 2006 to 2017, the overall structure of urban construction land in Beijing-Tianjin-Hebei urban agglomeration changes alternately between “orderly” and “disorderly”, and finally the overall development was slightly disordered. Furthermore, there are significant differences in the competitiveness of different types of land in different cities. Among them, green land, public facilities land, and road traffic land show obvious replenishment effect, which are mainly distributed in Handan-Zhangjiakou northwestern Hebei, Tianjin-Cangzhou in the eastern coast, Baoding-Xingtai in central and southern Hebei, while industrial land and storage land, which are mainly distributed in Beijing-Tangshan-Langfang around the capital and Shijiazhuang-Handan-Hengshui in central and southern Hebei, show obvious crowding-out effect. In addition, the temporal changes and spatial allocation differences of urban construction land structure are influenced by many factors, such as economic development, industrial structure, population size, etc. Therefore, it is suggested that the coordinated development of urban agglomerations should adhere to the principle of “differentiated development before coordinated development, local coordinated development before overall coordinated development”.

Entropy and hierarchy of competition in the World semiconductor market during COVID-19 pandemics

Microelectronics is a highly capital-intensive industry sector in which the competition is based on constant innovations and large scale investments. The market of semiconductor components is a typical example of a complex and dynamic system and the study of its structure is of particular interest in theoretical and practical terms. This paper explores the evolution of the semiconductor market since the early 1990s with a special focus on the most recent developments during the COVID-19 pandemics and the economic crisis of 2020-21. The dynamics of market concentration and the specifics of competition interactions among the largest suppliers of semiconductor components were explored with the traditional Information Theory concept for entropy and a novel concept for hierarchy. The comparative analysis confirms that the novel hierarchy concept is a consistent method that provides credible results in a format that can be used as complement for improving the existing methods. © 2021 IEEE.

Novel Protein Sequence Comparison Method Based on Transition Probability Graph and Information Entropy.

AIM AND OBJECTIVE: Aim and Objective: Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the feature metrics hidden in the sequence. Otherwise, the graphical representation of the biologic sequence is an important tool for sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences. MATERIALS AND METHODS: The transition probability is used to describe amino acid combinations of protein sequences. The combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from the transition probability graph to transition probability vector by the k-order transition probability graph. Transition entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses. RESULTS: By constructing a phylogenetic tree, it was found that the results of each application are consistent with other studies. CONCLUSION: The graphical representation proposed in this article is a practical and correct method.