Cognitive Computation and Systems最新文献

I am Echoborg is promoted as ‘a show created afresh each time by the audience in conversation with an artificial intelligence (AI)’. The show demonstrates how AI in a creative and performance context can raise questions about the technology’s ethical use for persuasion and compliance, and how humans can reclaim agency. This audience study focuses on a consecutive three-night run in Bristol, UK in October 2019. The different outcomes of each show illustrate the unpredictability of audience interactions with conversational AI and how the collective dynamic of audience members shapes each performance. This study analyses (1) how I am Echoborg facilitates audience cocreation in a live performance context, (2) the show’s capacity to provoke nuanced understandings of the potential for AI and (3) the ability for intelligent technology to facilitate social interaction and group collaboration. This audience study demonstrates how the show inspires debate beyond binary conclusions (i.e. AI as good or bad) and how audiences can understand potential creative uses of AI, including as a tool for cocreating entertainment with (not just for) them.

Existing bearing fault diagnosis methods have some disadvantages, one being that most methods cannot completely consider all specific fault attributes. Another disadvantage is that the qualitative diagnosis method considers different fault types as a whole, and qualitative diagnosis of a single fault attribute is complicated. A convolutional neural network is proposed for application in the multi-attribute quantitative bearing fault diagnosis. Multiple combinations of convolutional layers are adopted to directly extract features from one-dimensional vibration signals. In addition, a softmax layer is designed to realise the simultaneous recognition of different fault attributes. The advantage of this approach is that it can realise diagnostic results for any combination of fault attributes and corresponding types, which overcomes the disadvantage of single attribute recognition in the traditional method. The method is simple but has strong generalisation ability with average diagnostic accuracy of more than 95%. According to bearing data from Case Western Reserve University and laboratory experiments by the authors, the results verify that the method can accurately and quantitatively diagnose bearing faults.

With the development of the wireless network, from 4G network to 5G network, people's communication quality has improved significantly and the processing requirements of operators' customer service systems will ameliorate, whereas the business undertaken by the intelligent network becomes more difficult. Customer service system, which can convey files and video, has evolved from manual to intelligent. At the same time, this system establishes a knowledge base based on the process of solving problems with customers. The customer service system can also undertake the task of process control within the enterprise. The ultimate goal is to understand the needs of customers through the knowledge base and develop corporate products based on customer data. Furthermore, this study proposes a network architecture of an intelligent customer service system to provide a reference for the construction.

The body area network (BAN) is composed of every wearable device network on the body to share information and data, which is applied in medical and health, especially in the direction of intelligent clothing. A wearable device is an integrated body of multi-sensor fusion. At the same time, the multi-dimensional needs of users and the unique problems of sensors appear. How to solve the problems of wearable sensors and sustainable design is the research focus. Based on the wearable sensor in the critical factor of wearable device fusion, this paper analyses the classification, technology, and current situation of a wearable sensor, discusses the problems of a wearable sensor for BAN from the aspects of human–computer interaction experience, data accuracy, multiple interaction modes, and battery power supply, and summarizes the direction of multi-sensor fusion, compatible biosensor materials, and low power consumption and high sensitivity. The sustainable design direction of visibility design, identification of use scenarios, short-term human–computer interaction, interaction process reduction, and integration invisibility are introduced. The integration research of wearable sensors is the future trend, and it has been widely used in medical and health, intelligent clothing, wireless communication, military, automobile, and other fields.

For detection of dense small-target organisms with indistinct features in complex background, the efficiency and accuracy of traditional target detection methods are low. Multi-sensor fusion oriented human-robot interaction (HRI) system has facilitated biologists to process and analyse data. For this, several deep learning models based on convolutional neural network (CNN) are improved and compared to study the species and density of dense organisms in deep-sea hydrothermal vent, which are fused it with related environmental information given by position sensors and conductivity-temperature-depth (CTD) sensors, so as to perfect multi-sensor fusion oriented HRI system. Firstly, the authors combined different meta-architectures and different feature extractors, and obtained five object identification algorithms based on CNN. Then, they compared computational cost of feature extractors and weighed the pros and cons of each algorithm from mean detection speed, correlation coefficient and mean class-specific confidence score to confirm that Faster Region-based CNN (R-CNN)_InceptionNet is the best algorithm applicable to hydrothermal vent biological dataset. Finally, they calculated the cognitive accuracy of rimicaris exoculata in dense and sparse areas, which were 88.3% and 95.9% respectively, to analyse the performance of the Faster R-CNN_InceptionNet. Results show that the proposed method can be used in the multi-sensor fusion oriented HRI system for the statistics of dense organisms in complex environments.

In microservice systems, availability can be ensured through a variety of measures, such as fault tolerance and flow limiting, which are collectively called the flow control. In the current mainstream system design, the flow control rules are usually fixed and set manually, which cannot be dynamically adjusted according to the flow shape. The performance of the system is thus not fully explored. To mitigate this problem, an adaptive dynamic flow control algorithm is proposed. Based on the system's monitoring data and current flow, the algorithm calculates the flow-limiting threshold in real time, and then it implements fine-grained service adaptive flow control to improve the resource utilization. Experimental results show that the performance of the adaptive automatic flow control is better than that of the traditional static method on resource utilization.

Machine-based language translation has been certainly picking up. Still, machines lag behind the cognitive powers of human beings. Neural Machine Translation (NMT) methods require huge datasets and computational power for high-quality translation. A novel Sequential Adaptive Memory (SAM) cognitive model-based machine translation system for English to Hindi translation, was proposed. This model is an augmented version of the Cortical Learning Algorithm (CLA). The SAM is based on the architecture of the neocortex region of the brain, where speech and language comprehension and production take place. The proposed model is capable of learning with smaller datasets. This model employs the sequence to sequence learning approach, which provides better quality translation. It enables the creation of word pairs, dictionaries, and rules for translation. The results of the proposed approach are compared with the traditional phrase-based SMT approach as well as with the state-of-the-art NMT approach. The results are comparable with the results of the conventional approaches. We illustrate that the limitations of the approaches are won over by the proposed SAM approach. It is observed that SAM is capable of exhibiting satisfactory quality translation for low resource languages as well.

Soft grippers are compliant and self-adaptive, and can be highly compatible with the surrounding environment in grasping tasks. Currently, most soft pneumatic grippers are developed with a single grasping configuration, which leads to poor universality for different objects. Additionally, the oscillation caused by actuator's elastic bodies will result in poor stability during grasping and transportation, which can be improved by stiffness enhancement. A four-fingered soft pneumatic gripper is proposed by integrating multi-configuration and variable-stiffness functionality. The multi-configuration was realised by using the motion characteristics of a tangent mechanism. Meanwhile, a damping method based on electrorheological fluids was applied on a pneumatic actuator to improve the grasping stability. Besides, a machine vision technique was adopted to automatically adjust the grasping posture during manipulation. As a result, the proposed multi-configuration gripper can self-adaptively grasp different shapes of objects, especially two classical types, a pen canister as the flat cylinder and a cuboid box as the long cylinder. In addition, the electrorheological variable-stiffness method was manifested to be applicable for reducing pneumatic finger vibration. This research is expected to improve the versatility and grasping stability of soft pneumatic grippers.

Cancer is a group of related diseases with high mortality rate characterized by abnormal cell growth which attacks the body tissues. Microarray cancer data is a prominent research topic across many disciplines focused to address problems related to the higher curse of dimensionality, a small number of samples, noisy data and imbalance class. A random forest (RF) tree-based feature selection and ensemble learning based on hard voting and soft voting is proposed to classify microarray cancer data using six different base classifiers. The selected features due to RF tree are submitted to the base classifiers as the training set. Then, an ensemble learning method is applied to the base classifiers in which case each base classifier predicts class label individually. The final prediction is carried out hard and soft voting techniques that use majority voting and weighted probability on the test set. The proposed ensemble learning method is validated on eight different standard microarray cancer datasets, of which three of the datasets are binary class and the remaining five datasets are multi-class datasets. Experimental results of the proposed method show 1.00 classification accuracy on six of the datasets and 0.96 on two of the datasets.