Social Media Big Data Analytics for Demand Forecasting

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

A Cognitive Stylistic Analysis of Catharsis Used by Husseini Preachers

This study deals with catharsis as a cognitive stylistic device used to expel fear and anxiety for the sake of changing the audience toward better by preachers in Husseini discourse – Hussein is a grand Shiite Muslim leader. It aims to explicate the exploitation of catharsis by Husseini preachers and the conceptualization of such phenomenon by the audience. The study adapts the emotion model developed by Kovecses (2000); five stages are utilized: cause of the emotion, emotion, control, loss of control, and behavioral response. Twenty Husseini sermons are analyzed according to the stages of the model. Consequently, the study has come up with considerable conclusions. Chief among them are: Husseini preachers pragmatically use prosodic features to convey catharsis. A further conclusion is that catharsis is utilized by Husseini preachers as a strategy to teach the audience all the objectives of the Husseini revolution and to connect the objectives to this age for the sake of reform, using the fear that can modulate the human behavior.

An Affective Computing Multilayer Cognitive Architecture for Evolutionary Cognitive Robotics

<p>Robots are entering our daily lives from self-driving cars to health-care robots. Historically, pre-programmed robots were vulnerable to changing conditions in daily life, primarily because of a lack of ability to generate novel, non-preset flexible solutions. Thus there is a need for robotics to incorporate adaptation, which is a trait of higher order natural species. This adaptation allows higher-order natural species to change their behaviours and internal mechanisms based on experience with often dynamic environment. The ability to adapt emerged through evolutionary processes. Evolutionary Robotics is an approach to create autonomous robots that are capable of automatically generating artificial behaviors and morphologies to achieve adaptation. Evolutionary robotics has the potential to automatically synthesize controllers for real autonomous robots and generate solutions to complete tasks in the uncertain real-world. Compared to the inflexibility of pre-programmed robots, evolutionary robots are able to learn flexible solutions to given tasks through evolutionary methods. Cognitive robotics, a branch of artificial cognitive systems research, is such an attempt to create autonomous robots by applying bio-inspired methods. As the robot interacts with environment, an underlying cognitive system can learn its own solutions toward task completion. This learning-solution-from-interaction approach, also termed as a Reinforcement Learning (RL) approach, is widely applied in cognitive robotics to learn the solutions automatically. Ideally, the solutions can emerge in the cognitive system through the trial-and-error process of the RL approach without introducing human bias. This thesis aims to develop an evolutionary cognitive architecture (system) for a robot that can learn adaptive solutions to complete tasks. Inspired by emotion theories, this work proposes Affective Computing Multilayer Cognitive Architecture (ACMCA), a universal cognitive architecture, which is able to learn diverse solutions. Extending from previous work, ACMCA has a five-layer structure, where each layer aims to achieve different components of the solutions. The position of this thesis is that introducing a novel emotion inspired multilayer architecture that produces task solutions through subsumption operations and underlying appropriate machine learning algorithms will allow a robot to complete admissible tasks. ACMCA’s five layers are: primary reinforcer layer, secondary reinforcer layer, core affect state layer, strategy layer, and behaviour layer. This five-layer decomposition also meets the traditional decomposition of a mobile control system into functional modules (e.g. perception, modelling, planning, task execution, and motor control). Each layer contains computing nodes as functional modules that process various Stimuli, Actions, and their consequential Outcomes of the cognitive system. In this work, 17 computing nodes and their connections in ACMCA represent the solutions that a mobile robot has learned to complete navigation tasks in complex scenarios. Inspired by the Constructive Theory 1 and the robotic subsumption system, this work proposes a contingency-based subsumption approach to construct ACMCA. This contingency is termed Stimuli-Action-Outcome Contingency (SAOC), which is extended from the Action-Outcome (AO) contingency of Construction Theory. SAOCs are represented by “if-then” rules, termed SAOC rules, which encapsulate Stimuli, Actions, and their consequential Outcomes, providing clear symbolic interpretations. That is, the symbolic meaning of a SAOC rule can be interpreted as: if the input stimulus is perceived, the output action will be advocated as a cognitive response, expecting the outcome of the action with an estimation of relevance. As low-level computing nodes encapsulate Stimulus, Actions, and Outcomes, high-level computing nodes can subsume these low-level ones through the form of SAOC rules. Therefore, the proposed ACMCA can be constructed by subsumption layers of Stimuli-Action-Outcome Contingency (SAOC) rules. This work applies machine learning techniques to facilitate ACMCA’s real-world robotic implementation. This work selects Accuracy-based Learning Classifier Systems (XCS) algorithms as the underlying machine learning techniques that are deployed at computing nodes for the contingency-based subsumption operations. The mitosis approach of XCS and the XCS with a Combined Reward method (XCSCR) are two novel variants of XCS algorithm. They are proposed to amend two challenges that occur when the standard XCS approaches are applied for robotic applications. The mitosis approach introduces an accuracy pressure into the algorithm’s evolutionary process, improving the algorithms’ performance in robotic applications where noisy interferences exist. The XCSCR enables the policy to emerge earlier and more frequently than the existing benchmark approaches in multistep problems. Therefore, a robot with the XCSCR can handle a multistep scenario more effectively than those with the benchmarked algorithms. This work conducts five experiments to test the capability of ACMCA and its underlying algorithms in learning solutions for robotic navigation tasks. The five experiments are conducted as follows: reflex-learning, IR-tuning, deliberation-establishing, emotion model, and combined reward assignment. As the results of the experiments, three different affective patterns have emerged in the first three experiments, an emotion model has emerged in the fourth experiments, and the fifth experiment explores ACMCA’s potential implementation in the life-long learning scenario. These results demonstrate that ACMCA, a novel emotion inspired multilayer architecture, can produce task solutions through contingency-based subsumption operations and underlying appropriate machine learning algorithms, allowing a robot to complete admissible tasks through evolutionary processes. The contingency-based subsumption operations can establish three contingencies and one emotion model between the subsumed components by multiple RL agents which deploy the proposed mitosis approach of XCS algorithms. These three emotion patterns and emotion model can consistently improve the robot’s navigation performance with interpretable explanations. These two variants of XCS algorithms can amend shortfalls of the standard XCS approach in real-world robotic implementations. It has been demonstrated that the diverse solutions learned by ACMCA improve the navigation performance of the robot in terms of higher flexibility, reduction in continuous collisions and shorter navigation time consumption.</p>

An Affective Computing Multilayer Cognitive Architecture for Evolutionary Cognitive Robotics

<p>Robots are entering our daily lives from self-driving cars to health-care robots. Historically, pre-programmed robots were vulnerable to changing conditions in daily life, primarily because of a lack of ability to generate novel, non-preset flexible solutions. Thus there is a need for robotics to incorporate adaptation, which is a trait of higher order natural species. This adaptation allows higher-order natural species to change their behaviours and internal mechanisms based on experience with often dynamic environment. The ability to adapt emerged through evolutionary processes. Evolutionary Robotics is an approach to create autonomous robots that are capable of automatically generating artificial behaviors and morphologies to achieve adaptation. Evolutionary robotics has the potential to automatically synthesize controllers for real autonomous robots and generate solutions to complete tasks in the uncertain real-world. Compared to the inflexibility of pre-programmed robots, evolutionary robots are able to learn flexible solutions to given tasks through evolutionary methods. Cognitive robotics, a branch of artificial cognitive systems research, is such an attempt to create autonomous robots by applying bio-inspired methods. As the robot interacts with environment, an underlying cognitive system can learn its own solutions toward task completion. This learning-solution-from-interaction approach, also termed as a Reinforcement Learning (RL) approach, is widely applied in cognitive robotics to learn the solutions automatically. Ideally, the solutions can emerge in the cognitive system through the trial-and-error process of the RL approach without introducing human bias. This thesis aims to develop an evolutionary cognitive architecture (system) for a robot that can learn adaptive solutions to complete tasks. Inspired by emotion theories, this work proposes Affective Computing Multilayer Cognitive Architecture (ACMCA), a universal cognitive architecture, which is able to learn diverse solutions. Extending from previous work, ACMCA has a five-layer structure, where each layer aims to achieve different components of the solutions. The position of this thesis is that introducing a novel emotion inspired multilayer architecture that produces task solutions through subsumption operations and underlying appropriate machine learning algorithms will allow a robot to complete admissible tasks. ACMCA’s five layers are: primary reinforcer layer, secondary reinforcer layer, core affect state layer, strategy layer, and behaviour layer. This five-layer decomposition also meets the traditional decomposition of a mobile control system into functional modules (e.g. perception, modelling, planning, task execution, and motor control). Each layer contains computing nodes as functional modules that process various Stimuli, Actions, and their consequential Outcomes of the cognitive system. In this work, 17 computing nodes and their connections in ACMCA represent the solutions that a mobile robot has learned to complete navigation tasks in complex scenarios. Inspired by the Constructive Theory 1 and the robotic subsumption system, this work proposes a contingency-based subsumption approach to construct ACMCA. This contingency is termed Stimuli-Action-Outcome Contingency (SAOC), which is extended from the Action-Outcome (AO) contingency of Construction Theory. SAOCs are represented by “if-then” rules, termed SAOC rules, which encapsulate Stimuli, Actions, and their consequential Outcomes, providing clear symbolic interpretations. That is, the symbolic meaning of a SAOC rule can be interpreted as: if the input stimulus is perceived, the output action will be advocated as a cognitive response, expecting the outcome of the action with an estimation of relevance. As low-level computing nodes encapsulate Stimulus, Actions, and Outcomes, high-level computing nodes can subsume these low-level ones through the form of SAOC rules. Therefore, the proposed ACMCA can be constructed by subsumption layers of Stimuli-Action-Outcome Contingency (SAOC) rules. This work applies machine learning techniques to facilitate ACMCA’s real-world robotic implementation. This work selects Accuracy-based Learning Classifier Systems (XCS) algorithms as the underlying machine learning techniques that are deployed at computing nodes for the contingency-based subsumption operations. The mitosis approach of XCS and the XCS with a Combined Reward method (XCSCR) are two novel variants of XCS algorithm. They are proposed to amend two challenges that occur when the standard XCS approaches are applied for robotic applications. The mitosis approach introduces an accuracy pressure into the algorithm’s evolutionary process, improving the algorithms’ performance in robotic applications where noisy interferences exist. The XCSCR enables the policy to emerge earlier and more frequently than the existing benchmark approaches in multistep problems. Therefore, a robot with the XCSCR can handle a multistep scenario more effectively than those with the benchmarked algorithms. This work conducts five experiments to test the capability of ACMCA and its underlying algorithms in learning solutions for robotic navigation tasks. The five experiments are conducted as follows: reflex-learning, IR-tuning, deliberation-establishing, emotion model, and combined reward assignment. As the results of the experiments, three different affective patterns have emerged in the first three experiments, an emotion model has emerged in the fourth experiments, and the fifth experiment explores ACMCA’s potential implementation in the life-long learning scenario. These results demonstrate that ACMCA, a novel emotion inspired multilayer architecture, can produce task solutions through contingency-based subsumption operations and underlying appropriate machine learning algorithms, allowing a robot to complete admissible tasks through evolutionary processes. The contingency-based subsumption operations can establish three contingencies and one emotion model between the subsumed components by multiple RL agents which deploy the proposed mitosis approach of XCS algorithms. These three emotion patterns and emotion model can consistently improve the robot’s navigation performance with interpretable explanations. These two variants of XCS algorithms can amend shortfalls of the standard XCS approach in real-world robotic implementations. It has been demonstrated that the diverse solutions learned by ACMCA improve the navigation performance of the robot in terms of higher flexibility, reduction in continuous collisions and shorter navigation time consumption.</p>

Classifying Emotions in Film Music—A Deep Learning Approach

The paper presents an application for automatically classifying emotions in film music. A model of emotions is proposed, which is also associated with colors. The model created has nine emotional states, to which colors are assigned according to the color theory in film. Subjective tests are carried out to check the correctness of the assumptions behind the adopted emotion model. For that purpose, a statistical analysis of the subjective test results is performed. The application employs a deep convolutional neural network (CNN), which classifies emotions based on 30 s excerpts of music works presented to the CNN input using mel-spectrograms. Examples of classification results of the selected neural networks used to create the system are shown.

Human inspired robotic path planning and heterogeneous robotic mapping

<p>One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains.</p>

Human inspired robotic path planning and heterogeneous robotic mapping

<p>One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains.</p>

Music Waveform Analysis Based on SOM Neural Network and Big Data

Music is an indispensable part of our life and study and is one of the most important forms of multimedia applications. With the development of deep learning and neural network in recent years, how to use cutting-edge technology to study and apply music has become a research hotspot. Music waveform is not only the main form of music frequency but also the basis of music feature extraction. This paper first designs a method of note extraction based on the fast Fourier transform principle of the audio signal packet route under the self-organizing map (SOM neural network) which can accurately extract the musical features of the note, such as amplitude, loudness, period, and so on. Secondly, the audio segments are divided into summary by adding window moving matching method, and the music features such as amplitude, loudness, and period of each bar are obtained according to the performance of audio signal in each bar. Finally, according to the similarity of the audio music theory of the adjacent summary of each bar, the audio segments are divided, and the music features of each segment are obtained. The traditional recurrent neural network (RNN) is improved, and the SOM neural network is used to recognize the audio emotion features. The final experimental results show that the proposed method based on SOM neural network and big data can effectively extract and analyze music waveform features. Compared with previous studies, this paper creatively proposed a new algorithm, which can more accurately and quickly extract and analyze the data sound waveform, and used SOM neural network to analyze the emotion model contained in music for the first time.