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In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues.

This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods.

The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments.

Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.

To combine the forecasting by single method using influence information fully, other than regular combined methods only focusing on historical forecasting errors.

To combine the single methods based on the analysis of improved gray correlation, with more related information being considered to enhance the price forecasting precision, such as the trend of the prices, the historical forecasting errors, and the temporal influence factors on prices.

A case of PJM market of USA shows that the proposed method has better performance than any other combined methods, and all single models as well.

The combined performance depends on the forecasting precision of single methods, and the correlation between the single methods, as well as the number of single method that to be combined.

It is a novel idea for combined method to forecasting the time series data, such as electricity prices, electric power loads.

The proposed method considers all the following factors: the similarity between the trends of the single forecasting, the errors of the single models and the temporal influence.

The purpose of this paper is to focus on the suitability of the multitrait-multimethod (MTMM) matrix approach for examining construct validity in human resource management (HRM) research. The authors also provide a number of suggestions on how to use MTMM more effectively in HRM research.

The authors start by presenting a basic introduction to MTMM approach. Next the authors briefly review the limitations of MTMM approach and suggested improvements. The authors elaborate on these limitations by providing HRM examples. To further illustrate these issues, the authors review employment interview research.

The construct validity analysis in HRM research suffers from three problematic assumptions of the classical MTMM approach: uncorrelated trait-method units, uncorrelated methods, and uncorrelated traits. The review of interview research shows that classical MTMM approach is by far the most popular approach given its relative simplicity and modest sample size requirements. This popularity stresses the significance of the review in highlighting these issues.

Several improvements to quantify the interpretations of MTMM analysis are available to researchers. This review closely examines how these limitations and proposed improvements influence HRM research, thereby making the methodological advances concerning the MTMM approach more accessible to HRM researchers and practitioners.

The implementation of the innovation-driven development strategy is of practical significance for improving the quality and efficiency of economic growth and accelerating the transformation of economic development mode. The purpose of this paper is to study the impact of innovation-driven strategies on marine industry agglomeration and industrial transformation.

In traditional grey correlation analysis, when the positive and negative areas cancel each other out during the integration process, the calculation result of the correlation degree is often inconsistent with the qualitative analysis. For this reason, from the perspective of curve similarity, this paper constructs two response curves through the relative change area of the two curves and the relative area change ratio of similar degree, thus constructing an improved grey relational model.

The authors find that the innovation investment has a better correlation with marine industrial agglomeration. It also found that Guangdong Province has the highest degree of correlation between innovation indicators and marine industrial agglomeration. Much beyond the authors’ expectation, in the areas where marine industrial agglomeration is high, the synergistic effect is not obvious by using the location entropy method.

The improved grey correlation analysis method can effectively overcome the phenomenon that the positive and negative areas cancel each other in the integration process of the original algorithm, and it can also effectively measure the negative correlation between variables. This paper explores the impact of innovation drive on the agglomeration of marine industries, which is of great significance to the sustainable development of marine economy.

Affective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.

A new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.

The proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.

The proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.

The purpose of this paper is to examine the relationships between project delivery success factors, project management tools, software, and methods.

A statistical analysis was undertaken using data from a survey from a purposive sample of 150 participants across three countries (Australia, Canada and the UK). The findings were used to consider the relationships between project success factors, project management tools, software, and methods.

The findings reveal certain insights in the use of tools and methodologies. Of all the variables measured, the number of project management tools used and the number of risk tools used showed the highest direct correlation. It was therefore surmised that the use of tools from one of these categories is often coincident with the use of tools from the other category. Also, the use of project management tools exhibited less variability as compared to use of information communication technology support tools and risk management tools. In addition, use of formal project management methods exhibited less variability than use of formal decision‐making methods. Therefore, it is suggested that use of project management tools and methods is more consistent across the organizations studied, as compared to other tools and methods.

This paper extends the survey findings of an international 2011 study and sheds light on the use of project management and related tools and methods.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Using six fabrics which are very different in structure and composition, comparisons have been made between British, French and American standards, and between IWS and Renault methods for seaming properties determination. According to measuring principles they could be classified into two groups. The calculated correlations within groups were acceptable, while correlations between groups were low. The Renault method is situated between the two groups, because one parameter given by this method showed a good correlation with the British standard, while the other had a good correlation with the French standard.

The aim is to apply pseudo‐random correlation method to detect very weak electrical signals because of various natural and artificial electron‐magnetic interferences in electrical prospecting.

Electrical prospecting is an important method of geophysical exploration and the electrical prospecting instruments are required to detect very weak electrical signals against strong interferences. Recently, pseudo‐random correlation coding has been widely applied in telecommunications and measurement and test systems to improve the signal noise ratio with great success, but has not been used in electric prospecting. This paper theoretically investigated the application model of pseudo‐random correlation techniques in electrical prospecting.

The model of pseudo‐random correlation techniques in electrical prospecting, including its principle, detailed protocol and parameter selection, is established.

With the continuing improvement in the capacity of electrical prospecting transmitters, the pseudo‐random correlation method will be widely used in electrical prospecting.

The pseudo‐random correlation techniques is originally investigated for its application in electrical prospecting. This paper is aimed at researchers and engineers in geophysical exploration.

This article deals with how to test for and evaluate interdependence among control practices in a management control system using structural equation modeling. Empirical research on the levers of control (LOC) framework is used as an example. In LOC research, a path model approach to interdependence has been developed. The appropriateness of this approach is evaluated, developed, and compared with the correlation of residuals approach (seemingly unrelated regression) implemented in the wider complementarity literature. Empirical examples of the different models are shown and compared by using a data set on LOC of 120 SBUs in Sweden. The empirical results show that modeling interdependence among control practices in a management control system as non-recursive (bi-directional) paths or as residual correlations evidently affects the conclusions drawn about interdependence in terms of both presence and magnitude. The two models imply different views on how to conceptualize interdependence and are not statistically and empirically comparable. If using non-recursive path models, several model specification issues appear. To be able to identify such models, this needs to be carefully considered in the theory and research design prior to data collection.