A Survey of Customer Service System Based on Learning

With the rapid development of artificial intelligence, people have moved from manual customer service to handling affairs, and now they are more inclined to use intelligent customer service systems. The intelligent customer service system is generally a chat robot based on natural language processing, and it is a dialogue system. Therefore, it plays a vital role in many fields, especially in the field of e-commerce. In this article, to help researchers further study the customer service system for e-commerce, we survey the learning-based methods in dialogue understanding, dialogue management and dialogue response generation in the customer service system. In particular, we compare the advantages and disadvantages of these methods and pointed out further research directions

A Methodological Process for the Design of Frameworks Oriented to Infotainment User Interfaces

The objective of this paper was to propose a methodological process for the design of frameworks oriented to infotainment user interfaces. Four stages comprise the proposed process, conceptualization, structuring, documentation, and evaluation; in addition, these stages include activities, tasks, and deliverables to guide a work team during the design of a framework. To determine the stages and their components, an analysis of 42 papers was carried out through a systematic literature review in search of similarities during the design process of frameworks related to user interfaces. The evaluation method by a panel of experts was used to determine the validity of the proposal; the conceptual proposal was provided to a panel of 10 experts for their analysis and later a questionnaire in the form of a Likert scale was used to collect the information on the validation of the proposal. The results of the evaluation indicated that the methodological process is valid to meet the objective of designing a framework oriented to infotainment user interfaces

Transdisciplinarity seen through Information, Communication, Computation, (Inter-)Action and Cognition

Similar to oil that acted as a basic raw material and key driving force of industrial society, information acts as a raw material and principal mover of knowledge society in the knowledge production, propagation and application. New developments in information processing and information communication technologies allow increasingly complex and accurate descriptions, representations and models, which are often multi-parameter, multi-perspective, multi-level and multidimensional. This leads to the necessity of collaborative work between different domains with corresponding specialist competences, sciences and research traditions. We present several major transdisciplinary unification projects for information and knowledge, which proceed on the descriptive, logical and the level of generative mechanisms. Parallel process of boundary crossing and transdisciplinary activity is going on in the applied domains. Technological artifacts are becoming increasingly complex and their design is strongly user-centered, which brings in not only the function and various technological qualities but also other aspects including esthetic, user experience, ethics and sustainability with social and environmental dimensions. When integrating knowledge from a variety of fields, with contributions from different groups of stakeholders, numerous challenges are met in establishing common view and common course of action. In this context, information is our environment, and informational ecology determines both epistemology and spaces for action. We present some insights into the current state of the art of transdisciplinary theory and practice of information studies and informatics. We depict different facets of transdisciplinarity as we see it from our different research fields that include information studies, computability, human-computer interaction, multi-operating-systems environments and philosophy.Comment: Chapter in a forthcoming book: Information Studies and the Quest for Transdisciplinarity - Forthcoming book in World Scientific. Mark Burgin and Wolfgang Hofkirchner, Editor

A Comparative Analysis of Neural-Based Visual Recognisers for Speech Activity Detection

Recent advances in Neural network has offered great solutions to automation of various detections including speech activity detection (SAD). However, existing literature on SAD highlights different approaches within neural networks, but do not provide a comprehensive comparison of the approaches. This is important because such neural approaches often require hardware-intensive resources. As a result, the project provides a comparative analysis of three different approaches: classification with still images (CNN), classification based on previous images (CRNN) and classification based on a sequence of images (Seq2Seq). The project aims to find a modest approach-one that provides the highest accuracy but yet does not require expensive computation whilst providing the quickest output prediction times. Such approach can then be adapted for real-time application such as activation of infotainment systems or interactive robots etc. Results show that within the problem domain (dataset, resources etc.) the use of still images can achieve an accuracy of 97% for SAD. With the addition of RNN, the classification accuracy is increased further by 2%, as both architectures (classification based on previous images and classification of a sequence of images) achieve 99% classification accuracy. These results show that the use of history/previous images improves accuracy compared to the use of still images. Furthermore, with the RNNs ability of memory, the network can be defined smaller which results in quicker training and prediction times. Experiments also showed that CRNN is almost as accurate as the Seq2Seq architecture (99.1% vs 99.6% classification accuracy, respectively) but faster to train (326s vs 761s per epoch) and 28% faster output predictions (3.7s vs 5.19s per prediction). These results indicate that the CRNN can be a suitable choice for real-time application such as activation of infotainment systems based on classification accuracy, training and prediction times