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For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks.

We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files’ timestamp information. In the last part, we try to improve one promising looking model.

We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries.

Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results.

In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.

The purpose of this paper is to advocate a “social representations” approach to the study of socio‐cognitive processes during information systems (IS) implementation as an alternative to the technological frames framework.

The paper demonstrates how social representations theory can improve research outcomes by applying it to three recent studies that employed the technological frames framework.

It is found that because the technological frames framework is overly technologically centered, temporally bounded, and individually focused, it may lead to symptomatic explanations of IS implementation. Alternatively, using the theory of social representations can offer more fundamental causal explanations of IS implementation processes.

IS researchers are encouraged to use a social representations approach to study IS implementation as the theory provides a rich vocabulary to examine the formation, change, and content of representations of IS, and their relationship to people's actions toward IS.

The paper introduces a new theoretical perspective into the IS research discipline, which can be applied to provide better research results concerning IS implementation.

Data mining plays a major role in forecasting the open price details of the stock market. However, it fails to address the dimensionality and expectancy of a naive investor. Hence, this paper aims to study a future prediction model named time series model is implemented.

In this model, the stock market data are fed to the proposed deep neural networks (DBN), and the number of hidden neurons is optimized by the modified JAYA Algorithm (JA), based on the fitness function. Hence, the algorithm is termed as fitness-oriented JA (FJA), and the proposed model is termed as FJA-DBN. The primary objective of this open price forecasting model is the minimization of the error function between the modeled and actual output.

The performance analysis demonstrates that the deviation of FJA–DBN in predicting the open price details of the Tata Motors, Reliance Power and Infosys data shows better performance in terms of mean error percentage, symmetric mean absolute percentage error, mean absolute scaled error, mean absolute error, root mean square error, L1-norm, L2-Norm and Infinity-Norm (least infinity error).

The proposed model can be used to forecast the open price details.

The investors are constantly reviewing past pricing history and using it to influence their future investment decisions. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends

This paper presents a technique for time series modeling using JA. This is the first work that uses FJA-based optimization for stock market open price prediction.

Access to daily store level scanner data has been increasingly easier in recent years in Japan and time series analysis based on a sales response model is becoming realistic. Introduces a new method of combining time series analysis and regression analysis on the price promotion effect, which enables simultaneous decomposition of store level scanner sales into trend (including seasonality), day‐of‐the‐week effect and explanatory variable effect due to price promotion. The method was applied to daily store level scanner sales of milk, showing evidence of the existence of day‐of‐the‐week effect. Further, a method of incorporating several kinds of price‐cut variables in regression analysis and the analyzed results were presented.

This paper aims to present a case study of virtual-reality-based product demonstrations featuring items of furniture. The results will be of use in further design and development of virtual-reality-based product demonstration systems and could also support effective student learning.

A new method was introduced to guide the experiment by confirming orthogonal arrays. User interactions were then planned, and a furniture demonstration system was implemented. The experiment comprised two stages. In the evaluation stage, participants were invited to experience the virtual-reality (VR)-based furniture demonstration system and complete a user experience (UX) survey. Taguchi-style robust design methods were used to design orthogonal table experiments and planning and design operation methods were used to implement an experimental display system in order to obtain optimized combinations of control factors and levels. The second stage involved a confirmatory test for the optimized combinations. A pilot questionnaire was first applied to survey demonstration scenarios that are important to customers.

The author found in terms of furniture products, product interactive display through VR can achieve good user satisfaction through quality design planning. VR can better grasp the characteristics of products than paper catalogs and website catalogs. And VR can better grasp the characteristics of products than online videos. For “interactive inspection”, “function simulation”, “style customization” and “set-out customization” were the most valuable demonstration scenarios for customers. The results of the experiment confirmed that the “overall rating”, “hedonic appeal” and “practical quality” were the three most important optimized operating methods, constituting a benchmark of user satisfaction.

The author found that it is possible to design and build a VR-based furniture demonstration system with a good level of usability when a suitable quality design method is applied. The optimized user interaction indicators and implementation experience for the VR-based product demonstration presented in this study will be of use in further design and development of similar systems.

This study aims to examine the effects of iPad distribution on all teachers in a university and its application in teaching and student learning at home via wireless network during the coronavirus (COVID-19) pandemic. The attitude towards the use of iPads, behavioural intentions and the impact on the quality of teaching were evaluated.

This study used the technology acceptance model to explore the use of iPad smart mobile devices in multimedia teaching applications by university teachers. Furthermore, it used the structural equation modelling (SEM) for data analysis to explore the causal relationship between model variables, and it aimed to examine the causal relationship between variables to verify the theory. The SEM analysis included the following two stages: measurement model analysis and structural model analysis.

The “Internet information environment” had a significant positive impact on “perceived usefulness” and “perceived ease of use”. Amongst them, perceived usefulness had a significant positive effect on the use attitude, and use attitude had a significant positive effect on behaviour intention.

The findings confirmed that a good information network environment will directly and positively affect the perceived usefulness and the ease of use of iPad smart devices, of which the perceived usefulness will further positively affect teachers' perception of iPad smart devices. The attitude and behaviour of using such devices will in turn positively affect the quality of teaching. The results of the quality performance evaluation can be referenced further by manufacturers and scholars regarding the use of iPad smart devices for work at home during the COVID-19 pandemic.

This study has two objectives: to explore the factors that influence student self-efficacy regarding engagement and learning outcomes in a business simulation game course and to compare the difference between hierarchical and general teaching methods.

From September 2021 to May 2022, a questionnaire was administered to 126 students in a business simulation game course at the Zhongshan Institute, University of Electronic Science and Technology of China. Data were analyzed using nonparametric paired samples tests and linear regression.

The results showed that student self-efficacy, engagement and learning outcomes were significantly higher with the hierarchical teaching method than with the general teaching method. There were also differences in the factors that influenced self-efficacy regarding learning outcomes between the two teaching methods. With the general teaching method, student self-efficacy did not directly affect learning outcomes, but did so indirectly by mediating the effect of engagement. However, with the hierarchical teaching method, self-efficacy directly and significantly affected learning outcomes, in addition to indirectly affecting learning outcomes through student engagement.

Compared with the control group experimental research method, the quasi-experimental research method can eliminate the influence of sample heterogeneity itself, but the state of the same sample may change at different times, which is not necessarily caused by the hierarchical teaching design.

Based on the results of this study, teachers can apply hierarchical teaching according to student ability levels when integrating business simulation games. The results of this study can inspire teachers to protect student self-confidence and make teaching objectives and specific requirements clear in the beginning of the course, and also provide an important practical suggestion for students on how to improve their course performance.

The research results can be extended to other courses. Teachers can improve students' self-efficacy through hierarchical teaching design, thus improving students' learning performance and also provide reference value for students to improve their learning performance.

This study built a model based on self-system model of motivational development (SSMMD) theory, comparing factors that affect student self-efficacy regarding learning outcomes under different teaching methods. The model enriches the literature on SSMMD theory as applied to business simulation game courses and adds to our understanding of hierarchical teaching methods in this field. The results provide a valuable reference for teachers that can improve teaching methods and learning outcomes.

Popularly, metaphysics is considered to be the antonym for physics. Argues that this attitude is a hangover from outdated forms of empiricism and positivism. In the light of cybernetic epistemology, scientific theories are linguistic structures which help to produce predictions of events. These structures are not directly deduced from experience, but guessed and then justified a posteriori. Metaphysics provides a basis for such structures. Proposes the principle that the ultimate reality we find in the physical world is that of action. Modifies Schopenhauer's formula as the world is action and representation, with action taking ontological precedence, and not to the space‐time picture of the world. For a picture is only a picture, a representation which changes from one subject to another, from one theory to another; while action is an irrefutable reality. Thus the concept of action in abstracto is taken and on this basis an attempt is made to interpret the fundamental concepts of knowledge: what are objects, what is objective description of the world, what is space and time?

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

To provide an overview of how a number of frequently used smoothing‐based forecasting techniques can be modelled for use in dynamic analysis of production‐inventory systems.

The smoothing techniques are modelled using transfer functions and state space representation. Basic control theory is used for analysing the dynamic properties.

A set of expressions are derived for the smoothing techniques and dynamic properties are identified.

Dynamic properties are important in many applications. It is shown that the different smoothing techniques can have very different influences on the dynamic behaviour and therefore should be considered as a factor when smoothing parameters are decided on.

Dynamic behaviour of production‐inventory systems can be analysed using control theory based on, e.g. transfer functions or state space models. In this paper a set of models for five common smoothing techniques are analysed and their respective dynamic properties are highlighted.