Neuromorphic computing with a spiking neural network (SNN) is expected to provide a complement or alternative to deep learning in the future. The challenge is to develop optimal SNN models, algorithms, and engineering technologies for real use cases. As a potential use cases for neuromorphic computing, we have investigated a person monitoring and worker support with a video surveillance system, given its status as a proven deep neural network (DNN) use case. In the future, to increase the number of cameras in such a system, we will need a scalable approach that embeds only a few neuromorphic devices in a camera. Specifically, this will require a shallow SNN model that can be implemented in a few neuromorphic devices while providing a high recognition accuracy comparable to a DNN with the same configuration. A shallow SNN was built by converting ResNet, a proven DNN for image recognition, and a new configuration of the shallow SNN model was developed to improve its accuracy. The proposed shallow SNN model was evaluated with a few neuromorphic devices, and it achieved a recognition accuracy of more than 80% with about 1/130 less energy consumption than that of a GPU with the same configuration of DNN as that of SNN.

In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.

Ontologies are regarded as the solution to data heterogeneity on the Semantic Web (SW), but they also suffer from the heterogeneity problem, which leads to the ambiguity of data information. Ontology Meta-Matching technique (OMM) is able to solve the ontology heterogeneity problem through aggregating various similarity measures to find the heterogeneous entities. Inspired by the success of Reinforcement Learning (RL) in solving complex optimization problems, this work proposes a RL-based OMM technique to address the ontology heterogeneity problem. First, we propose a novel RL-based OMM framework, and then, a neural network that is called evaluated network is proposed to replace the Q table when we choose the next action of the agent, which is able to reduce memory consumption and computing time. After that, to better guide the training of neural network and improve the accuracy of RL agent, we establish a memory bank to mine depth information during the evaluated network's training procedure, and we use another neural network that is called target network to save the historical parameters. The experiment uses the famous benchmark in ontology matching domain to test our approach's performance, and the comparisons among Deep Reinforcement Learning(DRL), RL and state-of-the-art ontology matching systems show that our approach is able to effectively determine high-quality alignments.

An attention residual network with triple feature as input is proposed to predict the remaining useful life (RUL) of bearings. First, the channel attention and spatial attention are connected in series into the residual connection of the residual neural network to obtain a new attention residual module, so that the newly constructed deep learning network can better pay attention to the weak changes of the bearing state. Secondly, the “triple feature” is used as the input of the attention residual network, so that the deep learning network can better grasp the change trend of bearing running state, and better realize the prediction of the RUL of bearing. Finally, The method is verified by a set of experimental data. The results show the method is simple and effective, has high prediction accuracy, and reduces manual intervention in RUL prediction.

In this paper, we propose improved Generative Adversarial Networks with attention module in Generator, which can enhance the effectiveness of Generator. Furthermore, recent work has shown that Generator conditioning affects GAN performance. Leveraging this insight, we explored the effect of different normalization (spectral normalization, instance normalization) on Generator and Discriminator. Moreover, an enhanced loss function called Wasserstein Divergence distance, can alleviate the problem of difficult to train module in practice.

Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.

Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.

Epileptic seizure prediction is an important research topic in the clinical epilepsy treatment, which can provide opportunities to take precautionary measures for epilepsy patients and medical staff. EEG is an commonly used tool for studying brain activity, which records the electrical discharge of brain. Many studies based on machine learning algorithms have been proposed to solve the task using EEG signal. In this study, we propose a novel seizure prediction models based on convolutional neural networks and scalp EEG for a binary classification between preictal and interictal states. The short-time Fourier transform has been used to translate raw EEG signals into STFT sepctrums, which is applied as input of the models. The fusion features have been obtained through the side-output constructions and used to train and test our models. The test results show that our models can achieve comparable results in both sensitivity and FPR upon fusion features. The proposed patient-specific model can be used in seizure prediction system for EEG classification.

This letter proposes a novel lightweight deep learning object detector named LW-YOLOv4-tiny, which incorporates the convolution block feature addition module (CBFAM). The novelty of LW-YOLOv4-tiny is the use of channel-wise convolution and element-wise addition in the CBFAM instead of utilizing the concatenation of different feature maps. The model size and computation requirement are reduced by up to 16.9 Mbytes, 5.4 billion FLOPs (BFLOPS), and 11.3 FPS, which is 31.9%, 22.8%, and 30% smaller and faster than the most recent version of YOLOv4-tiny. From the MSCOCO2017 and PASCAL VOC2012 benchmarks, LW-YOLOv4-tiny achieved 40.2% and 69.3% mAP, respectively.

The original single-shot multibox detector (SSD) algorithm has good detection accuracy and speed for regular object recognition. However, the SSD is not suitable for detecting small objects for two reasons: 1) the relationships among different feature layers with various scales are not considered, 2) the predicted results are solely determined by several independent feature layers. To enhance its detection capability for small objects, this study proposes an improved SSD-based algorithm called proportional channels' fusion SSD (PCF-SSD). Three enhancements are provided by this novel PCF-SSD algorithm. First, a fusion feature pyramid model is proposed by concatenating channels of certain key feature layers in a given proportion for object detection. Second, the default box sizes are adjusted properly for small object detection. Third, an improved loss function is suggested to train the above-proposed fusion model, which can further improve object detection performance. A series of experiments are conducted on the public database Pascal VOC to validate the PCF-SSD. On comparing with the original SSD algorithm, our algorithm improves the mean average precision and detection accuracy for small objects by 3.3% and 3.9%, respectively, with a detection speed of 40FPS. Furthermore, the proposed PCF-SSD can achieve a better balance of detection accuracy and efficiency than the original SSD algorithm, as demonstrated by a series of experimental results.

A generative model is presented for two types of person image generation in this paper. First, this model is applied to pose-guided person image generation, i.e., converting the pose of a source person image to the target pose while preserving the texture of that source person image. Second, this model is also used for clothing-guided person image generation, i.e., changing the clothing texture of a source person image to the desired clothing texture. The core idea of the proposed model is to establish the multi-scale correspondence, which can effectively address the misalignment introduced by transferring pose, thereby preserving richer information on appearance. Specifically, the proposed model consists of two stages: 1) It first generates the target semantic map imposed on the target pose to provide more accurate guidance during the generation process. 2) After obtaining the multi-scale feature map by the encoder, the multi-scale correspondence is established, which is useful for a fine-grained generation. Experimental results show the proposed method is superior to state-of-the-art methods in pose-guided person image generation and show its effectiveness in clothing-guided person image generation.

The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.

Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.

The data augmentation method is known as a helpful technique to generate a dataset with a large number of images from one with a small number of images for supervised training in deep learning. However, a low validity augmentation method for image recognition was reported in a recent study on artificial intelligence (AI). This study aimed to clarify the optimal data augmentation method in deep learning model generation for the recognition of white blood cells (WBCs). Study Design: We conducted three different data augmentation methods (rotation, scaling, and distortion) on original WBC images, with each AI model for WBC recognition generated by supervised training. The subjects of the clinical assessment were 51 healthy persons. Thin-layer blood smears were prepared from peripheral blood and subjected to May-Grünwald-Giemsa staining. Results: The only significantly effective technique among the AI models for WBC recognition was data augmentation with rotation. By contrast, the effectiveness of both image distortion and image scaling was poor, and improved accuracy was limited to a specific WBC subcategory. Conclusion: Although data augmentation methods are often used for achieving high accuracy in AI generation with supervised training, we consider that it is necessary to select the optimal data augmentation method for medical AI generation based on the characteristics of medical images.

Pose estimation is a research hot spot in computer vision tasks and the key to computer perception of human activities. The core concept of human pose estimation involves describing the motion of the human body through major joint points. Large receptive fields and rich spatial information facilitate the keypoint localization task, and how to capture features on a larger scale and reintegrate them into the feature space is a challenge for pose estimation. To address this problem, we propose a multi-scale convergence network (MSCNet) with a large receptive field and rich spatial information. The structure of the MSCNet is based on an hourglass network that captures information at different scales to present a consistent understanding of the whole body. The multi-scale receptive field (MSRF) units provide a large receptive field to obtain rich contextual information, which is then selectively enhanced or suppressed by the Squeeze-Excitation (SE) attention mechanism to flexibly perform the pose estimation task. Experimental results show that MSCNet scores 73.1% AP on the COCO dataset, an 8.8% improvement compared to the mainstream CMUPose method. Compared to the advanced CPN, the MSCNet has 68.2% of the computational complexity and only 55.4% of the number of parameters.

In the pork fat content detection task, traditional physical or chemical methods are strongly destructive, have substantial technical requirements and cannot achieve nondestructive detection without slaughtering. To solve these problems, we propose a novel, convenient and economical method for detecting the fat content of pig B-ultrasound images based on hybrid attention and multiscale fusion learning, which extracts and fuses shallow detail information and deep semantic information at multiple scales. First, a deep learning network is constructed to learn the salient features of fat images through a hybrid attention mechanism. Then, the information describing pork fat is extracted at multiple scales, and the detailed information expressed in the shallow layer and the semantic information expressed in the deep layer are fused later. Finally, a deep convolution network is used to predict the fat content compared with the real label. The experimental results show that the determination coefficient is greater than 0.95 on the 130 groups of pork B-ultrasound image data sets, which is 2.90, 6.10 and 5.13 percentage points higher than that of VGGNet, ResNet and DenseNet, respectively. It indicats that the model could effectively identify the B-ultrasound image of pigs and predict the fat content with high accuracy.

Online social networks have increased their impact on the real world, which motivates information senders to control the propagation process of information to promote particular actions of online users. However, the existing works on information provisioning seem to oversimplify the users' decision-making process that involves information reception, internal actions of social networks, and external actions of social networks. In particular, characterizing the best practices of information provisioning that promotes the users' external actions is a complex task due to the complexity of the propagation process in OSNs, even when the variation of information is limited. Therefore, we propose a new information diffusion model that distinguishes user behaviors inside and outside of OSNs, and formulate an optimization problem to maximize the number of users who take the external actions by providing information over multiple rounds. Also, we define a robust provisioning policy for the problem, which selects a message sequence to maximize the expected number of desired users under the probabilistic uncertainty of OSN settings. Our experiment results infer that there could exist an information provisioning policy that achieves nearly-optimal solutions in different types of OSNs. Furthermore, we empirically demonstrate that the proposed robust policy can be such a universally optimal solution.

In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (GeoI), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, GeoI is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GeoGI. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms GeoI mechanisms, including optimal GeoI mechanism, with respect to the tradeoff.

Multi-band transmission technologies promise to cost-effectively expand the capacity of optical networks by exploiting low-loss spectrum windows beyond the conventional band used in already-deployed fibers. While such technologies offer a high potential for capacity upgrades, available capacity is seriously restricted not only by the wavelength-continuity constraint but also by the signal-to-noise ratio (SNR) constraint. In fact, exploiting more bands can cause higher SNR imbalance over multiple bands, which is mainly due to stimulated Raman scattering. To relax these constraints, we propose wavelength-selective band switching-enabled networks (BSNs), where each wavelength channel can be freely switched to any band and in any direction at any optical node on the route. We also present two typical optical node configurations utilizing all-optical wavelength converters, which can realize the switching proposal. Moreover, numerical analyses clarify that our BSN can reduce the fiber resource requirements by more than 20% compared to a conventional multi-band network under realistic conditions. We also discuss the impact of physical-layer performance of band switching operations on available benefits to investigate the feasibility of BSNs. In addition, we report on a proof-of-concept demonstration of a BSN with a prototype node, where C+L-band wavelength-division-multiplexed 112-Gb/s dual-polarization quadrature phase-shift keying signals are successfully transmitted while the bands of individual channels are switched node-by-node for up to 4 cascaded nodes.

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server; the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively studied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum unavailability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of proposed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the proposed model reduces the maximum unavailability by an average of 29% in our examined cases.