A survey on video content rating: taxonomy,challenges and open issues

Multimedia Tools and Applications - Rating a video based on its content is one of the most important solutions to classify videos for audience age groups. In this regard, Film content rating and TV...     

Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

     

A systematic literature review on intelligent automation: Aligning concepts from theory,practice, and future perspectives

With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA’s benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.     

Enhancing power transfer capability through flexible AC transmission system devices:a review

Global demand for power has significantly increased, but power generation and transmission capacities have not increased proportionally with this demand. As a result, power consumers suffer from variou...     

EFFECT OF TOOL WEAR ON MICROSTRUCTURE, MECHANICAL PROPERTIES AND ACOUSTIC EMISSION OF FRICTION STIR WELDED 6061 Al ALLOY

Tool condition is one of the main concerns in friction stir welding （FSW）, because the geometrical condition of the tool pin including size and shape is strongly connected to the microstrueture and mechanical performance of the weld. Tool wear occurs during FSW, especially for welding metal matrix composites with large amounts of abrasive particles, and high melting point materials, which significantly expedite tool wear and deteriorate the mechanical performance of welds. Tools with different pin-wear levels are used to weld 6061 Al alloy, while acoustic emission （AE） sensing, metallographic sectioning, and tensile testing are employed to evaluate the weld quality in various tool wear conditions. Structural characterization shows that the tool wear interferes with the weld quality and accounts for the formation of voids in the nugget zone. Tensile test analysis of samples verifies that both the ultimate tensile strength and the yield strength are adversely affected by the formation of voids in the nugget due to the tool wear. The failure location during tensile test clearly depends on the state of the tool wear, which led to the analysis of the relationships between the structure of the nugget and tool wear. AE signatures recorded during welding reveal that the AE hits concentrate on the higher amplitudes with increasing tool wear. The results show that the AE sensing provides a potentially effective method for the on-line manitoring of tool wear.     

A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

Constructing online switching barriers: examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers

Developing switching barriers to retain customers has become a critical marketing strategy for online retailers. However, research on the role of switching barriers in e-retailing is still limited. Recent trends show that when competitors are just one click away, it is questionable if customer loyalty can be achieved at all in online environments. This leads to the research question on whether switching barriers have any impact on e-loyalty in pure-play retailers. The paper examines the influence of switching barriers on customer retention (i.e., e-store loyalty) and further investigates the moderating effects of switching costs and alternative attractiveness. Data were gathered via a survey of 590 shoppers of online pure-play retailers in the UK. Findings show that customer satisfaction and the two dimensions of switching barriers (perceived switching costs and perceived attractiveness of alternatives) significantly influence customer loyalty. Contrary to findings in earlier studies, it was found that switching costs did not moderate the relationships between satisfaction and loyalty nor between perceived attractiveness of alternatives and loyalty. The paper makes imperative recommendations to develop switching barriers and to foster loyalty along with suggestions for future research.     

Incorporating user preferences in ant colony optimization for the next release problem

The selection of which requirements should be implemented in the next software release is an important and complex task in the software development process, considering the presence of budget constraints and other conflicting aspects. In this context, search based software engineering, has the main objective of applying automatic search methods to solve complex software engineering problems. However, most of these methods do not consider human expertise during the search, especially due to the difficulty in mathematically modeling the user's preferences. Consequently, the user can present some resistance or place little confidence in the final results, given that his/her knowledge and domain expertise was not properly considered in the solution construction. This paper aims at proposing an interactive model for the next release problem using ant colony optimization, where the user can define which requirements he/she would like to include or not in the next release. Employing humans and a simulator, an empirical study was performed that considers real-world and artificial instances. The achieved results demonstrate that the loss of score was, on average, 12% when it was compared with a solution with no human intervention. On the other hand, the algorithm generates solutions that have more than 80% of the met preferences, as defined by the users. Furthermore, the results showed that ACO can be an interesting choice as an interactive search engine, given the low quantity of interactions that are required to reach good solutions.     

Optimal design of a 3D-printed scaffold using intelligent evolutionary algorithms

Fabrication of three-dimensional structures has gained increasing importance in the bone tissue engineering (BTE) field. Mechanical properties and permeability are two important requirement for BTE scaffolds. The mechanical properties of the scaffolds are highly dependent on the processing parameters. Layer thickness, delay time between spreading each powder layer, and printing orientation are the major factors that determine the porosity and compression strength of the 3D printed scaffold.In this study, the aggregated artificial neural network (AANN) was used to investigate the simultaneous effects of layer thickness, delay time between spreading each layer, and print orientation of porous structures on the compressive strength and porosity of scaffolds. Two optimization methods were applied to obtain the optimal 3D parameter settings for printing tiny porous structures as a real BTE problem. First, particle swarm optimization algorithm was implemented to obtain the optimum topology of the AANN. Then, Pareto front optimization was used to determine the optimal setting parameters for the fabrication of the scaffolds with required compressive strength and porosity. The results indicate the acceptable potential of the evolutionary strategies for the controlling and optimization of the 3DP process as a complicated engineering problem.