Innovation and knowledge trends through standardisation of IT applications

The paper presents part of a study on collective knowledge and innovations in IT as well as an extract segment from a comparative statistical analysis of trends in the global–local standardisation of the pathways of knowledge and IT innovations in IT applications. The aim of the paper is to provide and promote educational and financial resources for the quality of knowledge in IT application. ISO (global) and SRPS (local) documents on IT and IT applications have been extracted from this statistical sample and analysed.The main results of the research are presented with phases of the PDCA methodology     

Stabilization of persistently excited linear systems by delayed feedback laws

This paper considers the stabilization to the origin of a persistently excited linear system by means of a linear state feedback, where we suppose that the feedback law is not applied instantaneously, but after a certain positive delay (not necessarily constant). The main result is that, under certain spectral hypotheses on the linear system, stabilization by means of a linear delayed feedback is indeed possible, generalizing a previous result already known for non-delayed feedback laws.     

Quantitative cross impact analysis with latent semantic indexing

Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology.     

Intelligent software product line configurations: A literature review

A software product line (SPL) is a set of industrial software-intensive systems for configuring similar software products in which personalized feature sets are configured by different business teams. The integration of these feature sets can generate inconsistencies that are typically resolved through manual deliberation. This is a time-consuming process and leads to a potential loss of business resources. Artificial intelligence (AI) techniques can provide the best solution to address this issue autonomously through more efficient configurations, lesser inconsistencies and optimized resources. This paper presents the first literature review of both research and industrial AI applications to SPL configuration issues. Our results reveal only 19 relevant research works which employ traditional AI techniques on small feature sets with no real-life testing or application in industry. We categorize these works in a typology by identifying 8 perspectives of SPL. We also show that only 2 standard industrial SPL tools employ AI in a limited way to resolve inconsistencies. To inject more interest and application in this domain, we motivate and present future research directions. Particularly, using real-world SPL data, we demonstrate how predictive analytics (a state of the art AI technique) can separately model inconsistent and consistent patterns, and then predict inconsistencies in advance to help SPL designers during the configuration of a product.     

The impact of electronic environmental knowledge on the environmental behaviors of people

Nowadays, we examined the connection between individuals' relationships with the natural environment, their environmental behaviors, and their environmental knowledge with a focus on environmental attitudes, environmental awareness, environmental values, public information, environmental skills, and environmental responsibility is very interesting. Environmental knowledge involves human discourse about interrelationships with the environment. The impact of people to perceive and interpret the relative health of environmental systems and to take appropriate action to maintain, restore, or improve the health of those systems is very important. Therefore, environmental teaching and learning is very necessary and can help to improve the environment behaviors of the people. The purpose of this paper is to investigate the impact of e-learning system the environmental of the behavior. Data were collected from 330 students of Tabriz Branch, Islamic Azad University. After performing satisfactory reliability and validity checks, the structural model was tested with the use of smart PLS 2.0. Results indicate that electronic environmental knowledge learning has a direct effect on person's environmental behaviors.     

Understanding online regret experience in Facebook use – Effects of brand participation,accessibility & problematic use

The past few years have witnessed the emergence of research examining online regret experience. The presence of online regret generates negative use experience and even leads to service switching and discontinuity. However, to date, only limited research has examined the conceptualization of this new yet very important phenomenon in the field of technology use. To address this research gap, the present study has examined the relative influence of SNS brand participation, technology accessibility attributes (including Facebook usage parameters) and problematic Facebook use in predicting regret experience regarding Facebook use. A pen-and-paper cross-sectional survey was administered to 804 adolescent Facebook users (aged 13–18 years). The study results suggest that adolescent users with varying technology accessibility did not differ in their online regret experience, but excessive Facebook users and those actively participating in brand communities tended to experience higher regret. Other findings suggest that two variables, namely parents’ perceptions of problematic Facebook use and conflict with friends due to Facebook use, were significant predictors of online regret experience. This study presents different theoretical and practical implications for both research and practice.     

Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction

We suggest an optimization approach of cluster-based undersampling to select appropriate instances. This approach can solve the data imbalance problem, which can lead to knowledge extraction for improving the performance of existing data mining techniques. Although data mining techniques among various big data analytics technologies have been successfully applied and proven in terms of classification performance in various domains, such as marketing, accounting and finance areas, the data imbalance problem has been regarded as one of the most important issues to be considered.We examined the effectiveness of a hybrid method using a clustering technique and genetic algorithms based on the artificial neural networks model to balance the proportion between the minority class and majority class. The objective of this paper is to constitute the best suitable training dataset for both decreasing data imbalance and improving the classification accuracy. We extracted the properly balanced dataset composed of optimal or near-optimal instances for the artificial neural networks model. The main contribution of the proposed method is that we extract explorative knowledge based on recognition of the data structure and categorize instances through the clustering technique while performing simultaneous optimization for the artificial neural networks modeling. In addition, we can easily understand why the instances are selected by the rule-format knowledge representation increasing the expressive power of the criteria of selecting instances. The proposed method is successfully applied to the bankruptcy prediction problem using financial data for which the proportion of small- and medium-sized bankruptcy firms in the manufacturing industry is extremely small compared to that of non-bankruptcy firms.     

Semi-supervised model-based clustering with controlled clusters leakage

In this paper, we focus on finding clusters in partially categorized data sets. We propose a semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural subgroups of given categories. In contrast to other semi-supervised models, C3L is parametrized by user-defined leakage level, which controls maximal inconsistency between initial categorization and resulting clustering. Our method can be implemented as a module in practical expert systems to detect clusters, which combine expert knowledge with true distribution of data. Moreover, it can be used for improving the results of less flexible clustering techniques, such as projection pursuit clustering. The paper presents extensive theoretical analysis of the model and fast algorithm for its efficient optimization. Experimental results show that C3L finds high quality clustering model, which can be applied in discovering meaningful groups in partially classified data.     

A 3D-DCT video encoder using advanced coding techniques for low power mobile device

The three-dimensional discrete cosine transform (3D-DCT) has been researched as an alternative to existing dominant video standards based on motion estimation and compensation. Since it does not need to search macro block for inter/intra prediction, 3D-DCT has great advantages for complexity. However, it has not been developed well because of poor video quality while video standards such as H.263(+) and HEVC have been blooming. In this paper, we propose a new 3D-DCT video coding as a new video solution for low power mobile technologies such as Internet of Things (IoT) and Drone. We focus on overcoming drawbacks reported in previous research. We build a complete 3D-DCT video coding system by adopting existing advanced techniques and devising new coding algorithms to improve overall performance of 3D-DCT. Experimental results show proposed 3D-DCT outperforms H.264 low power profiles while offering less complexity. From GBD-PSNR, proposed 3D-DCT provides better performance by average 4.6 dB.