We have witnessed a rapid boom of data in recent years from various fields such as infrastructure, transport, energy, health, education, telecommunications, and finance. Together with the dramatic advances in Machine Learning (ML) and Visual Analytics (VA), getting insights from these ``big data'' and data analytics-driven solutions are increasingly in demand for different purposes. While we continuously find ourselves coming across ML-based Artificial Intelligence (AI) and VA systems that seem to work or have worked surprisingly well in practical scenarios, these technologies still face prolonged challenges with low user acceptance of delivered solutions as well as seeing system misuse, disuse, or even failure. These fundamental challenges can be attributed to the nature of ``black-box'' of ML methods for domain experts and a lack of consideration of the human user aspects when offering ML and VA based solutions.
This special issue targets on novel principles, analytics techniques and evaluation methodologies that are to address issues surrounding trustworthy AI and VA especially from human user’s perspectives of visible, explainable, trustworthy and transparent. The primary objective is to foster focused attention in this emerging area and to serve as forum for researchers and professionals all over the world to exchange and discuss the latest advances.
Papers to be submitted to this special issue must focus on trustworthy AI (e.g. visualizations, evaluations, human responses, etc.) and visual analytics for big data. All submitted papers will be peer-reviewed and papers will be selected based on their quality and relevance to the theme of this special issue. Topics considered for this special issue include, but are not limited to, the following:
Innovative algorithms, visual processing, analytics systems and techniques for trustworthy AI
Case studies, user studies, and application systems of trustworthy AI and visual analytics
Cognitive aspects of trustworthy AI
Human-machine interfaces, frameworks, architectures, tools and systems for trustworthy AI and visual analytics
Visualization of computational processes in AI
Applied Soft Computing
Special Issue on Cognitive Computing for Collaborative Robotics
Cognitive Computing breaks the boundary between two separate fields, neuroscience and computer science. It paves the way for machines to have reasoning abilities which is analogous to human. The research field of cognitive computing is interdisciplinary, and uses knowledge and methods from many areas such as psychology, biology, signal processing, physics, information theory, mathematics, and statistics. The development of cognitive computing will keep cross-fertilizing these research areas. However, in collaborative robotics applications there still remain many open problems for using cognitive computing theories. Technologies like Computational Cognition and Perception (CCP) and Computational Neuroscience (CN) are driving as the best tools for upgrading the robots with near human intelligence, which can be intended to physically interact with humans in a shared workspace.
The overall aim of this special issue is to collect the state-of-the-art contributions on the Computational Neuroscience, Computational Cognition and Perception, Computer Vision, Natural Language Processing, Human Action Analysis, and related applications in robotics.
The journal invites submissions for a special issue on "Cognitive Computing for Collaborative Robotics" that aims to attract high-quality papers that describe state-of-the-art technologies and new findings both in soft computing and robotics research fields. Some of the most important areas include, but are not limited to:
1. New Theories and Methods of Cognitive Computing
Human Brain Mapping Approaches
Computational Modelling (CNN, RNN, ANN etc)
Sensory Perception Methods
Memory and Imagination Models
Action Prediction Approaches
Soft Computing Models
2. Applications of Cognitive Computing in Robotics
Cognitive Mixed Reality
Neuroscience and Behavior Analysis in Robotics
Cognitive Imaging and Processing
Electro-Encephalography (EEG) Analysis
Transcranial Magnetic Stimulation (TMS) Analysis
Bayesian Program Learning
Applied Soft Computing
Emerging Soft Computing Methodologies in Deep Learning and Applications
Machine learning is to design and analyze algorithms that allow computers to "learn" automatically, and allows machines to establish rules from automatically analyzing data and using them to predict unknown data. Traditional machine learning approach is difficult to meet the needs of Internet of Things (IoT) only through its outdated process starting from problem definition, appropriate information collection, and ending with model development and results verification. But however, recent scenario has dramatically changed due to the development of artificial intelligence (AI) and high-speed computing performance. Therefore, deep learning is a good example that breaks the limits of machine learning through feature engineering and gives astonishingly superior performance. It makes a number of extremely complex applications possible.
Machine learning has been applied to solve complex problems in human society for years, and the success of machine learning is because of the support of computing capabilities as well as the sensing technology. An evolution of artificial intelligence and soft computing approaches will soon cause considerable impacts to the field. Search engines, image recognition, biometrics, speech and handwriting recognition, natural language processing, and even medical diagnostics and financial credit ratings are all common examples. It is clear that many challenges will be brought to publics as the artificial intelligence infiltrates into our world, and more specifically, our lives.
Deep learning has been more mature in the field of supervised learning, but other areas of machine learning have just started, especially for the areas of unsupervised learning and reinforcement learning with soft computing methodologies. Deep learning is a class ofmachine learningalgorithmsthat:
use a cascade of multiple layers ofnonlinear processingunits forfeature extractionand transformation. Each successive layer uses the output from the previous layer as input.
learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.
Due to the cascaded structure and the abstraction level of multiple representations, Deep Learning has very good performance in speech recognition and image recognition, especially when one aims to have different levels of resolution representations in signals and images with gaining automated features extracted from these. Two common models, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), are widely used architectures in the context of deep learning. In addition to the fact that most "deep learning" technologies are built on the concept of supervised learning to construct a set of classifiers to recognize things entering an information system, "soft computing and metaheuristic algorithms" are built on the concept of unsupervised learning to find out good solutions from a solution space, which can be regarded as an infinite space. The algorithms of these two research domains are the two promising technologies of AI that has been widely and successfully used in solving many complex and large-scale problems.
However, applying deep learning to solve problems will encounter some challenges. In order to have good performance, deep learning algorithms require a large and diverse range of data, and a large number of parameters need to be tuned. Furthermore, well-trained deep learning model tend to have overfitting problems, and not easily applied in other areas. In addition, the training process of deep learning is still a black box, and researchers have a hard time understanding how they learning and how they deduce conclusions. Therefore, in order to boost performance and transparency of deep learning models and to bring them actually to a level of high practical usage in real-world applications and facilities, this special issue places a special attention i.) on the (complexity) reduction of parameters with soft computing methodologies in deep-learning models, ii.) an enhanced interpretation and reasoning methods with soft computing methodologies for explaining hidden components in deep learning models as well as for gaining a better understanding of the outputs of deep learning models (=> increasing acceptability for company experts and users) and iii) on methods for incrementally self-adapting and evolving soft computing methodologies for deep learning models, where not only weight parameters may be recursively updated, but also internal structures may be evolved and pruned on the fly based on current changes and drift intensity present in the system. Furthermore, new deep learning methods in combination with renowned, widely-used architectures, but also developed for soft computing and artificial intelligence environments where it has been not considered so far (e.g., deep learning SVMs or deep learning bio-inspired systems are hardly existing) are also warmly welcomed. There are new emerging applications and new deep learning developments of established applications of soft computing methodologies and architectures, with specific emphasis in the fields of big data, internet of things, social media data mining, web applications.
Original contributions are solicited from, but are not limited, the following topics of interest:
Methodologies,and Techniques(but not necessarily restr. to):
New methods for Soft Computing in combination with Deep Learning
New learning methods with Soft Computing concepts for established deep learning architectures and structure
Faster and more robust Soft Computing methods for learning of deep models
Complexity Reduction with Soft Computing methods and Transformation of Deep Learning Models
Evolutionary and Soft Computing-based optimization and tuning of deep learning models
Evolving and Soft Computing techniques for deep learning systems (expanding and pruning layers, components etc. on the fly)
Metaheuristics aspects and Soft Computing algorithms in deep learning for improved convergence
Hybrid learning schemes with Soft Computing (deterministic with heuristics-based, memetic)
Interpretability Aspects with Soft Computing for a better Understanding of Deep Learning Models
Soft Computing Methods for non-established deep learning models (deep SVMs, deep fuzzy models, deep clustering techniques, ...)
Real-World Applicationsof deep learning techniques such as (but not necessarily restricted to):
Cloud and Fog Computing in AI
Big Data Analysis
Context-Awareness and Intelligent Environment Application
Financial Engineering and Time Series Forecasting and Analysis