Most recent object detection methods have achieved growing performance on public datasets. However, enormous efforts are needed for these methods due to the extensive annotations of ground-truth boxes. Weakly Supervised Object Detection (WSOD) methods hence have been proposed to solve this problem as only image-level annotations are required and then output bounding boxes related to the objects. In order to further elevate the weakly supervised detection methods on the extraction of reasonable features, the training of potential positive proposals, and the generation of proposals before training, we propose a new Combined Backbone and Advanced Selection Heads (CBASH) method with the proposals generated from the object semantic information. Specifically, Combined Backbone will make the unobvious object features more noticeable, Advanced Selection Heads promote more potential positive proposals to get training, and the generated object semantic proposals elevate the quality and quantity of positive proposals. The proposed method is evaluated on the challenging PASCAL VOC 2007 and 2012 benchmark datasets. Experimental results show that our proposed method can achieve improved performance on both VOC 2007 and VOC 2012 datasets and outperforms the existing state-of-the-art methods.

Exchanging information and data within smart grids is crucial to improve interoperability among system users. Traditional cloud-based data exchange schemes are centralized on a single trusted third-party platform. The schemes consequently suffer from single-point failure, a lack of data protection, and uncontrolled access. Blockchain enables data exchange in a decentralised and secure manner. A new platform is proposed in this work for exchanging data within smart grids using blockchain. It allows users to securely exchange data without losing ownership. This platform provides solutions to three critical problems: privacy, scalability, and user ownership. Particularly, the blockchain-based smart contract technology gives participants the programmability to access data. All interactions are authenticated and recorded by the other participants in the tamper-resistant blockchain network. Furthermore, the performance of the proposed blockchain platform is enhanced by integrating it with an artificial neural network (ANN). The proposed method is used to predict the network’s throughput and latency, and the network administrator uses these predicted values to change the network’s settings for a high throughput and low latency. Throughout the results, the proposed model achieves performance improvements in blockchain-enabled information and data exchange and adapts well to the dynamics of smart grids.

Object counting aims to count the accurate number of object instances in images, and its operation efficiency is essential. However, most current CNN-based methods rely on complex network architectures, which results in them consuming a significant amount of memory, time, and other resources at runtime. This seriously limits their deployment in practical application scenarios, such as public safety and agriculture planting. Therefore, we propose a lightweight object counting method named EdgeCount to effectively balance inference speed and object counting accuracy. Specifically, we construct a network composed of a student model (EdgeCount) and a teacher model (EdgeCount-T) with the same encoder-decoder structure based on density map knowledge distillation (DMKD), allowing the EdgeCount to learn object density distribution from the EdgeCount-T. After that, we introduce spatial and channel reconstruction convolution (SCConv), composed of a spatial reconstruction unit (SRU) and a channel reconstruction unit (CRU), to decrease spatial and channel redundancy with lower computational costs. Moreover, a low parameter weighted multi-scale feature fusion module (LWMFFM) is designed to further improve the countering ability through segmenting minor structural discrepacies among multi-scale features. Extensive experiments conducted on challenging remote sensing and dense crowd object counting datasets demonstrate the effectiveness and superiority of our method. In particular, under the four NVIDIA Jetson devices, EdgeCount can accurately counter objects with only 0.12M parameters and 19.87M floating-point operations per second (FLOPs) in the size of 128, which achieves the lowest latency and fastest FPS compared with other state-of-the-art object counters.

Gene expression programming (GEP) is a data driven evolutionary technique that is well suited to correlation mining of system components. With the rapid development of industry 4.0, the number of components in a complex industrial system has increased significantly with a high complexity of correlations. As a result, a major challenge in employing GEP to solve system engineering problems lies in computation efficiency of the evolution process. To address this challenge, this paper presents EGEP, an event tracker enhanced GEP, which filters irrelevant system components to ensure the evolution process to converge quickly. Furthermore, we introduce three theorems to mathematically validate the effectiveness of EGEP based on a GEP schema theory. Experiment results also confirm that EGEP outperforms the GEP with a shorter computation time in an evolution.

At present, solid-state fermentation (SSF) is mainly controlled by artificial experience, and the product quality and yield are not stable. Therefore, predicting the quality and yield of SSF is of great significance for improving the utility of SSF. In this article, we propose a deep learning utility prediction (DLUP) scheme for the SSF in the Industrial Internet of Things, including parameters collection and utility prediction of the SSF process. Furthermore, we propose a novel edge-rewritable Petri net to model the parameters collection and utility prediction of the SSF process and further verify their soundness. More importantly, DLUP combines the generating ability of least squares generative adversarial network with the predicting ability of fully connected neural network to realize the utility prediction (usually use the alcohol concentration) of SSF. Experiments show that the proposed method predicts the alcohol concentration more accurately than the other joint prediction methods. In addition, the method in our article provides evidences for setting the ratio of raw materials and proper temperature through numerical analysis.

No doubt incorporating deep learning in security public transportation is an attractive and challenging research direction. In this article, we incorporate multiple attention mechanisms with high-resolution and high-semantic features into neural networks to further classify and localize threat items automatically. We propose our efficient model located in different network layers. To be specific, our model is involved in three modules called the spatial attention module (SAM), the channel attention module (CAM), and the high-resolution and high-semantic module (HRHSM). For shallow and deep layers, we adapt SAM and CAM to mine the long-range spatial and channel information of the network, respectively. With regard to the last few layers with different sizes, we leverage HRHSM to fuse their features so that the output contains high-resolution and high-semantic features. Due to the generalizability, our model can be broadly applied to many commonly used networks. Experimental results on the SIXray dataset that is consisted of six classes of threat item in X-ray images, CIFAR-10, and CIFAR-100 datasets demonstrate that our modules can improve the performance of networks in image classification and object localization without ground-truth bounding boxes.