Edge computing has recently emerged as a promising paradigm to support mobile access in Internet of Things (IoT) multinetworks, where heterogeneous wireless communication solutions coexist. Meanwhile, software-defined networking (SDN) presents a potential infrastructure to monitor and manage mobile edge computing. However, resilient access in the integrated IoT-Edge-SDN environment is a key challenge. In this article, we present location-aware spatio-temporal solution (LASTS) as an edge computing-empowered software-defined system to scalably control mobile IoT access and detect sequential anomaly. LASTS utilizes a Personal access point protocol to enable switching between multiple networks. In addition, it supports efficient control and transfer of the mobile device’s spatio-temporal context. This context information plays an important role in a deep learning model employed for sequential anomaly detection in the LASTS system. Realistic testbed experiments confirm that LASTS can successfully achieve scalable access control and sequential anomaly detection in mobile IoT.

Micro Advice is a mobile platform for democratic governance in China, allowing access to voice social issues and advice to the government with the aim of improving people's livelihood. However, due to the lack of first-hand experience, the current understanding of how end-users utilize Micro Advice to participate in democratic governance is incomplete. We interviewed 12 users to understand their practices and challenges in using the platform. Specifically, we illustrate the user's experience, introduce what difficulties they encountered, and how they strategically use the platform to improve people's livelihood. We also investigate the socio-technical aspects of Micro Advice within the Chinese political context, discussing how to accept and utilize Micro Advice in China's social environment, and develop technological solutions adapted to these backgrounds. Finally, we propose some design implications for civic technology participation platforms. Micro Advice provides a novel, open, and real-time channel for civic engagement, showcasing the practical effects and impact of digitized civic engagement in China. It offers researchers a new perspective for expressing and addressing societal issues. We believe that the innovation and insights of Micro Advice can extend to other types of digitized civic engagement initiatives. We will continue to explore the interactive processes between the government and the public, along with innovative technological approaches.

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely StyleWe, employing federated learning to aid in sketch design. StyleWe is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through StyleWe, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations and user studies indicate that our StyleWe system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.

Quantized neural networks (QNNs) have become a standard operation for efficiently deploying deep learning models on hardware platforms in real application scenarios. An empirical study on German traffic sign recognition benchmark (GTSRB) dataset shows that under the three white-box adversarial attacks of fast gradient sign method, random + fast gradient sign method and basic iterative method, the accuracy of the full quantization model was only 55%, much lower than that of the full precision model (73%). This indicates the adversarial robustness of the full quantization model is much worse than that of the full precision model. To improve the adversarial robustness of the full quantization model, we have designed an adversarial attack defense platform based on field-programmable gate array (FPGA) to jointly optimize the efficiency and robustness of QNNs. Various hardware-friendly techniques such as adversarial training and feature squeezing were studied and transferred to the FPGA platform based on the designed accelerator of QNN. Experiments on the GTSRB dataset show that the adversarial training embedded on FPGA can increase the model's average accuracy by 2.5% on clean data, 15% under white-box attacks, and 4% under black-box attacks, respectively, demonstrating our methodology can improve the robustness of the full quantization model under different adversarial attacks.

The “mind-controlling” capability has always been in mankind's fantasy. With the recent advancements in electroencephalograph (EEG) techniques, brain-computer interface (BCI) researchers have explored some solutions to allow individuals to perform various tasks using their minds. However, the commercial off-the-shelf devices to run accurate EEG signal collection are usually expensive and the comparably cheaper devices can only present coarse results, which prevents the practical application of these devices in domestic services. To tackle this challenge, we propose and develop an end-to-end solution that enables fine brain-robot interaction (BRI) through embedded learning of coarse EEG signals from low-cost devices, namely PerBCI, so that people having difficulty moving, such as the elderly, can mind command and control a robot to perform some basic household tasks. Our contributions are three folds: 1) We present a stacked long short-term memory (BiLSTM) structure, along with specific pre-processing techniques to handle the time-dependency of EEG signals and their classification. 2) We propose a personalized design to adaptively capture multiple features and achieve accurate recognition of individual EEG signals by enhancing the signal interpretation of BiLSTM with an attention mechanism. 3) We develop a low-cost, real-time and end-to-end BRI system that can run our PerBCI models and algorithms in the embedded robot platform to perform more than one type of domestic task based on the users' EEG signal inputs. Our real-world experiments with elderly participants of diverse backgrounds in a home setting and system comparison with other approaches show that the proposed end-to-end solution with low cost can achieve satisfactory run-time speed, accuracy and energy-efficiency.

Recent advancements in EEG-based BCI technologies have been explored to assist individuals in executing brain-to-robot tasks. In the future, using BCI systems in both personal and professional domains is anticipated in widespread adoption. One promising application is facing home life, to enable people to use commercial EEG equipment to implement daily tasks. However, current BCI studies mainly focus on single-brain-to-single-robot interaction, which has limitations in representing diverse human intentions. A generalized BCI system with multiple EEG devices allows users to more precisely or collaboratively control robots. Therefore, it is imperative to extend the BCI techniques to future collaboration scenarios. In this paper, we present a new system, NeuroBCI, for multi-brain-to-multi-robot interaction through the integration of sensing, computing, communication, and control. To improve sensing efficiency, NeuroBCI employs a sparse attention mechanism to extract joint features from heterogeneous EEG data. Parallel computation and transmission for multi-user multi-task scenarios are handled by semantic autoencoder and autodecoder communications. A code map is designed to ensure concurrent control and model compression methods are used on both transmitter and receiver sides. Our experiments in comparison with state-of-the-art works show the superior performance of NeuroBCI on sensing, computing, communication, and control as a holistic system.

Hand-drawn sketches and sketch colourization are the most laborious but necessary steps for fashion designers to design exquisite clothes, especially when the fashion design requires distinctive and personal characteristics from designer style. This paper presents an artificial intelligent aided fashion design system, namely StyleMe, to support the automatic generation of clothing sketches with designer style. Given the clothing pictures specified by the designer, StyleMe can use deep learning based generative model to generate clothing sketches that are consistent with the designer style. The system also supports intelligent colourization on clothing sketch by style transfer, according to specified styles from the real fashion images. Through a series of performance evaluations and user studies, we found that our system can generate effective clothing sketches as good as fashion designers’ human work, and significantly improve the efficiency of fashion design with its sketch colourization method.

Recent years have witnessed significant developments in point cloud processing, including classification and segmentation. However, supervised learning approaches need a lot of well-labeled data for training, and annotation is labor-and time-intensive. Self-supervised learning, on the other hand, uses unlabeled data, and pretrains a back-bone with a pretext task to extract latent representations to be used with the downstream tasks. Compared to 2D images, self-supervised learning of 3D point clouds is under-explored. Existing models, for self-supervised learning of 3D point clouds, rely on a large number of data samples, and require significant amount of computational re-sources and training time. To address this issue, we propose a novel contrastive learning approach, referred to as To ThePoint. Different from traditional contrastive learning methods, which maximize agreement between features obtained from a pair of point clouds formed only with different types of augmentation, ToThePoint also maximizes the agreement between the permutation invariant features and features discarded after max pooling. We first perform self-supervised learning on the ShapeNet dataset, and then evaluate the performance of the network on different downstream tasks. In the downstream task experiments, performed on the ModelNet40, ModelNet40C, ScanobjectNN and ShapeNet-Part datasets, our proposed ToThe-Point achieves competitive, if not better results compared to the state-of-the-art baselines, and does so with significantly less training time (200 times faster than baselines).

With the development of artificial intelligence and the improvement of hardware computing power, deep learning models have become widely used in the Internet of Things (IoT) field, especially for analyzing spatiotemporal data collected by wireless sensors. Recurrent neural networks (RNNs) such as long short-term memory (LSTM) network are generally used for these time-series data. Hyperparameter settings of model training are regarded as essential factors for the performance of deep learning models. Manually optimizing hyperparameters not only cost more resources but also be more likely to set hyperparameters that follow stereotypes, resulting in unreasonable hyperparameter settings and poor model performance. As one of the most important fields in automated machine learning research, automated hyperparameter optimization (HPO) mainly includes grid search, hyperparameter search based on genetic algorithm, etc. Whereas these methods have their own drawbacks. In this article, an automated HPO method based on parallel genetic algorithm (PGA) is proposed. According to the process of PGA, this article divided HPO into several stages, including population initialization, fitness function, tournament selection, crossover operators, mutation operators, subgroup exchange, and end of evolution. Then, the proposed HPO method is implemented in LSTM models and tested on two different time-series datasets collected by real-world IoT sensors. By comparing our proposed method with other mainstream HPO methods in different datasets, it is proved that our HPO method based on PGA shows a better performance on both time costs and prediction results.