Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline in cognitive functions and eventually results in death. An early AD diagnosis is important for taking active measures to slow its deterioration. Traditional diagnoses are usually based on clinical experience, which is limited by several realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as a typical eye-tracking behavior, is of great clinical value in detecting cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conducted a 3D comprehensive visual task on a noninvasive eye-tracking system to collect visual attention heatmaps. Then a multilayered comparison convolutional neural network (MC-CNN) is proposed to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multilayered feature representations of heatmaps were obtained by hierarchical residual blocks to better encode eye movement behaviors, which were further integrated into a distance vector to benefit the comprehensive visual task. From evaluation, MC-CNN can distinguish AD patients from normals with 0.84 accuracy, 0.86 recall, 0.82 precision, 0.83 F1-score and 0.90 area under the curve (AUC). The above results demonstrate the effectiveness of the proposed MC-CNN in AD diagnosis based on the comprehensive 3D visual task.

Alzheimer’s disease (AD) is a degenerative neurological disease that occurs in the elderly with typical symptoms of decline in cognition, manifested by eye movement behaviors. The key to AD treatment requires early detection of cognitive impairment, which relies on frequent medical screening. This article proposes an Internet of Things (IoT) architecture constructed with eye-tracker (ET) nodes and cloud-based diagnosis enabled by machine learning (ML), which can provide convenient screening of oculomotor abnormalities and automatic identification of early-stage AD. The bespoke ET nodes collect 3-D oculomotor responses from diverse stereo video stimulation trials and transmit data into a dedicated multimodal ML (MMML) algorithm in the cloud. The algorithm incorporates multimodal features extracted from diverse oculomotor types to enhance the classification accuracy and optimized data dimension reduction in feature fusion to improve the classifier’s performance. From evaluation, the proposed method can distinguish AD patients from the control group with 86% accuracy (ACC), 78% true positive rate (TPR), and 90% positive predictive value (PPV). The results confirm the effectiveness of our MMML algorithm in AD diagnosis with the fusion of multimodal oculomotor features and prove the feasibility of our IoT-powered eye-tracking solution for AD screening.

Field Programmable Gate Array (FPGA) has become an excellent hardware accelerator solution for convolutional neural networks (CNN). Meanwhile, optimizing methods such as model compression have been proposed. As most CNN accelerators focus on dense neural networks, to solve the problem of difficult hardware deployment due to irregular networks, we propose a method for sparse neural networks in our work. The storage and coding format of sparse data obtained by the block pruning method is designed to make it friendly to implement on FPGA. Besides, we also propose an efficient and simple data flow by the planarization of the whole convolution calculation process. The experimental result demonstrates that our implementation can achieve clock frequency of 190MHz, power consumption of 13.32W and inferencing speed of 16.37ms. Compared with some typical Mobilenet implementation schemes, our method has been proven to achieve a better balance between frequency, accuracy, power consumption and speed.

Recent progress in artificial intelligence (AI) have broadened various intelligent application scenarios on the Internet of Multimedia Things (IoMT). Due to the urgent demands for intelligence in the online fashion industry, using AI techniques to explore user’s clothing collocations from fashion data generated by the IoMT system is a challenging task. Under the background, fashion compatibility modeling (FCM), which aims to estimate the matching degree of a given outfit, has attracted great attention in the multimedia analysis field. However, most of the studies often fail to fully leverage multimodal content or ignore the sparse associations between fashion items. In this paper, we propose a novel multimodal high-order relationship inference network (MHRIN) for fashion compatibility modeling task. In MHRIN, we focus on enriching multimodal representations of fashion items by means of incorporating the category correlations and injecting high-order item-item connectivity. Concretely, considering that fashion collocations depend on the semantic relevance patterns between categories, we design a category correlations learning module to adaptively learn category representations. On this basis, multiple modality representations are aggregated by a hierarchical multimodal fusion module to generate visual-semantic embeddings. To address the item-item matching interactions issue, we further refine the final representations by a high-order message propagation module to absorb rich connection information. Experiments on the publicly available dataset demonstrate the superiority of our MHRIN over state-of-the-art methods.

As the microblogging service (such as Weibo) is becoming popular, spam becomes a serious problem of affecting the credibility and readability of Online Social Networks. Most existing studies took use of a set of features to identify spam, but without the consideration of the overlap and dependency among different features. In this study, we investigate the problem of spam detection by analyzing real spam dataset collections of Weibo and propose a novel hybrid model of spammer detection, called SDHM, which utilizing significant features, i.e. user behavior information, online social network attributes and text content characteristics, in an organic way. Experiments on real Weibo dataset demonstrate the power of the proposed hybrid model and the promising performance.