This study introduces a blazingly fast, no-loss expert system for tic-tac-toe using decision trees called T3DT, which tries to emulate human gameplay as closely as possible. It does not make use of any brute force, minimax, or evolutionary techniques, but is still always unbeatable. To make the gameplay more human-like, randomisation is prioritised and T3DT randomly chooses one of the multiple optimal moves at each step. Since it does not need to analyse the complete game tree at any point, T3DT is exceptionally faster than any brute force or minimax algorithm, this has been shown theoretically as well as empirically from clock-time analyses in this study. T3DT also does not need the data sets or the time to train an evolutionary model, making it a practical no-loss approach to play tic-tac-toe.

Human motion prediction is an important and challenging task in computer vision with various applications. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been proposed to address this challenging task. However, RNNs exhibit their limitations on long-term temporal modelling and spatial modelling of motion signals. CNNs show their inflexible spatial and temporal modelling capability that mainly depends on a large convolutional kernel and the stride of convolutional operation. Moreover, those methods predict multiple future poses recursively, which easily suffer from noise accumulation. The authors present a new encoder–decoder framework based on the residual convolutional block with a small filter to predict future human poses, which can flexibly capture the hierarchical spatial and temporal representation of the human motion signals from the motion capture sensor. Specifically, the encoder is stacked by multiple residual convolutional blocks to hierarchically encode the spatio-temporal features of previous poses. The decoder is built with two fully connected layers to automatically reconstruct the spatial and temporal information of future poses in a non-recursive manner, which can avoid noise accumulation that differs from prior works. Experimental results show that the proposed method outperforms baselines on the Human3.6M dataset, which shows the effectiveness of the proposed method. The code is available at https://github.com/lily2lab/residual_prediction_network.

Here, the authors present a novel interactive prototype system that enhances the effectiveness and ingenuity for sound designers to explore the sound effect library created by layering in multi-methods. They combine the explored methods of semantic keyword, acoustic feature, and layer relationship. In particular, the system visualises the layer relationship via circle pack, which facilitates the sound designers’ understanding on the components of the mixed sound effect by the designed layer and sourced layer. In order to evaluate the proposed method, they conduct a timing experiment along with a five-point Likert scale survey to analyse the searching efficiency, the user experience, and the interactive user behaviours. The studies performed by the authors show that the proposed system is capable of enhancing the sound designers’ ability for sound effects searching, thus creating new possible combination and design.

On the issue of low utilisation and acceptance of current adaptive cruise control (ACC), a multi-objective adaptive cruise control (MO-ACC) algorithm is developed in this study. Based on model predictive control theory, comprehensively considering the coordination among various conflicting objectives, the decision of desired longitudinal acceleration is transformed into online quadratic programming (QP) problem. In order to compensate for prediction error caused by modelling mismatch, the robustness of control system is improved by introducing an error feedback correction mechanism. Meanwhile, vector management method is adopted to deal with the non-feasible solution owing to hard constraints during the process of optimisation. Further, under different work conditions, the focusing performance index along with constraint space varies, and therefore different ACC modes are established to meet the demand of skilled driving groups by means of slightly adjusting performance index, constraint space as well as slack relaxation. The simulations show that under the combined work conditions of the preceding vehicle, the following vehicle can realise seamless switching among various working modes, and also is able to achieve the good expectation during vehicle following, which will help to enhance the adaptability of the ACC system to the complex road traffic environment.

Tensor computation evolves fast towards a prosperous existence in recent years, e.g. PyTorch. An immediate advantage of using tensor computation is that one does not need to implement low-level parallelism to attain efficient computation, which is of simplicity for both research and application development. The authors began with discovering that a simple manoeuvre ‘tensor shift’ could perform neighbourhood manipulation in very efficient parallel manner. Based on ‘tensor shift’, they derive the tensor version of a renowned correspondence search algorithm: semi-global matching (SGM), which they prefix the name as tensor-SGM. To evaluate their idea, they build-up a novel and practical one-shot structured light 3D acquisition system, which yields state-of-art reconstruction results using off-the-shelf hardware. This is the first fully tensorised 3D reconstruction system published to the authors’ best knowledge, and it opens new possibilities. A major one is, in the same tensorised framework, they solved the pattern interfering problem which hinders multi-structured light systems from working together. This part is marked as ‘beyond’ in this study to avoid confusing the readers the spotlight: the fully tensorised 3D structured light framework.