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With the development of deep learning technology, many deep learning methods have been applied to font recognition and generation. However, few studies focus on font inpainting problems. This paper is dedicated to repairing damaged fonts based on style to repair damaged fonts in a better way. In this paper, we propose a CGAN (Conditional Generative Adversarial Nets)-based font repair method. This paper uses the content accuracy and style similarity of the repaired image as an evaluation index to evaluate the accuracy of the restored style font. The font content proposed by the paper based on CGAN network repair style is similar with the correct content. [ABSTRACT FROM AUTHOR]

Conventional algorithms for blind image deblurring are often inaccurate at blur kernel estimation, and the recovery effect is far from perfect. To address this, we propose a single-image blind deblurring method based on local rank. For this, we first impose adaptive threshold segmentation on a conventional local rank transform, which is subsequently used to construct a novel model for blind image deblurring. Next, a half-quadratic splitting method is adopted to estimate the blur kernel and an intermediate latent image, in alternating iterations. Finally, the desired latent image is obtained by linear combination of the hyper-Laplacian model and the total-variation-l2 model, where the weights are calculated from the adaptive local ranks. Experimental results using public datasets show that the proposed approach can accurately estimate the blur kernel and effectively suppress ringing effects. [ABSTRACT FROM AUTHOR]

Universities may set similar courses due to disciplinary crossing. Facing the problem that undergraduates have numerous tasks and limited time, we designed a mass customization model which will automatically provide every student with a suitable curriculum to maximize their degree of satisfaction when the student enters a set of courses. Our calculating model of curriculum satisfaction is based on the assumptions that curriculum satisfaction is proportional to its similarity and that curriculum satisfaction is inversely proportional to credits. With regard to a student who chooses n courses, his satisfaction model is total degree of satisfaction divided by total credits. We manually screened out 84 pairs of courses about science and engineering, social science and humanities, and made use of their Chinese names and Chinese course description on the site of Office of Educational Administration, and segmented words based on statistical principles and calculated the course similarity by using vector space. In addition, we make supplement to curriculum similarity by the use of artificially-designed questionnaires. [ABSTRACT FROM AUTHOR]

VR sickness (Cybersickness) presents an important challenge in virtual reality environments. We present the results of a study on the effects of VR technology and VR video content type on VR sickness and on autonomous nervous system of the user. The participants watched two omnidirectional (360°) videos of different content types (neutral and action) on five distinct video display types (2D TV screen, three generations of Oculus Rift VR HMDs and on the mobile Samsung GearVR HMD). The Simulator Sickness Questionnaire (SSQ) in combination with the measurement of the physiological parameters (skin conductance and skin temperature, respiratory frequency and heart rate) were used to monitor the participants' physiology. The results show that video content significantly affects the SSQ grading and the skin conductance level. VR sickness effects were significantly reported less often with TV display type than with other VR HMDs. [ABSTRACT FROM AUTHOR]

Circulating tumor cells (CTCs) is a clinical biomarker for cancer metastasis. CTCs are cells circulating in the body of patients by being separated from primary cancer and entering into blood vessel. CTCs spread every positions in the body, and this is one of the cause of cancer metastasis. To analyze them, pathologists get information about metastasis without invasive test. CTCs test is conducted by analyzing the blood sample from patient. The fluorescence microscope generates a large number of images per each sample, and images contain a lot of cells. There are only a few CTCs in images and cells often have blurry boundaries. So CTCs identification is not an easy work for pathologists. In this paper, we develop an automatic CTCs identification method in fluorescence microscopy images. This proposed method has three section. In the first approach, we conduct the cell segmentation in images by using filtering methods. Next, we compute feature values from each CTC candidate region. Finally, we identify CTCs using artificial neural network algorithm. We apply the proposed method to 5895 microscopy images (7 samplesas), and evaluate the effectiveness of our proposed method by using leave-one-out cross validation. We achieve the result of performance tests, a true positive rate is 92.57% and false positive rate is 9.156%. [ABSTRACT FROM AUTHOR]

Human wearable helmet is a useful tool for monitoring the status of miners in the mining industry. However, there is little research regarding human emotion recognition in an extreme environment. To the best of our knowledge, this paper is the first to describe the human anxiety change rule and to propose a cloud computing platform for detecting human emotions using brain-computer interface (BCI) devices. In this paper, an emotional state evoked paradigm is designed to identify the brain area where the emotion feature is most evident. Next, the correct electrode position is determined for the collection of the negative emotion by the electroencephalograph (EEG) based on the international 10–20 system of electrode placement. Next, a fusion algorithm of the anxiety level is proposed to evaluate the person's mental state using the θ, α, and β rhythms of an EEG. Next, the human smart helmet system is designed to collect the human state, which includes the mental parameters of the anxiety level, the fatigue level, the concentration level, and the environmental parameters in the coal mine. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most visible EEG changes appear within the first 2 s following stimulation. The amplitudes of the θ rhythm increase most significantly in the negative emotional state. The fusion algorithm of the anxiety level accurately measures negative emotional change. [ABSTRACT FROM AUTHOR]

Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to recognize and to predict the behavior of organisms. In this paper, we first develop a method for underwater real-time recognition and tracking of multi-objects, which we call "You Only Look Once: YOLO". This method provides us with a very fast and accurate tracker. At first, we remove the haze, which is caused by the turbidity of the water from a captured image. After that, we apply YOLO to allow recognition and tracking of marine organisms, which include shrimp, squid, crab and shark. The experiments demonstrate that our developed system shows satisfactory performance. [ABSTRACT FROM AUTHOR]

Aiming at improving the performance of the endmember extraction problem in hyperspectral images, a new extraction method based on discrete hybrid artificial bee colony algorithm and genetic algorithm (DABC_GA) is proposed. By analyzing the characteristic of the problem, each dimension of candidate solution is a discrete and exclusive integer. Then we employ an optimization method with integral coding. By inheriting the strong exploration ability of the traditional artificial bee colony algorithm (ABC), we propose a discrete ABC which could quickly obtain more valuable endmembers combinations in the early stage. Then we select some outstanding results of DABC as the potential solutions of GA, which is adopted as another optimization tool in the later stage of iteration. The concept of complementary sets is proposed in the cross and mutation operators to guarantee the diversity and completeness of solutions. Meanwhile, the greedy strategy is adopted to ensure that the favorable potential solutions are not discarded. Compared with conventional extraction algorithms in simulated and real hyperspectral remote sensing data, the experimental results show the validity of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to reduce artifacts on temporal difference images by improving the conventional method using a non-rigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. In this method, global matching, local matching, and 3D elastic matching are performed on the current image and past image, and an initial temporal difference image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and perform segmentation using SuperVoxel and Graph Cuts in which a superpixel is extended to three dimensions for each region of interest. For each extracted region, a support vector machine (SVM) is used to reduce the over-segmentation. Finally, in the method that greatly reduces artifacts other than the remaining GGO candidate regions, Voxel Matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact. The resulting ratio of artifacts to lung volume is 0.101 with an FWHM of 28.3, which is an improvement over the conventional method and shows the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan’s economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots. [ABSTRACT FROM AUTHOR]

Optical underwater images often demonstrate low contrast, heavy scatter, and color distortion. Contrast enhancement methods have been proposed to solve these issues. However, such methods typically do not consider high-level inhomogeneous scatter removal and do not focus on real-scene color restoration. We proposed a hierarchical transmission fusion method and a color-line ambient light estimation method for image de-scattering from a single input image. Our proposed method can be summarized into three steps. Firstly, we take the dark channel as prior information to estimating the preliminary transmission and ambient light. In the second step, we then use color lines to estimate the refined ambient light in selected patches. The refined transmission is obtained by hierarchical transmission maps using maximum local energy-based fusion at different turbidity levels. We then use a joint normalized filter to obtain the final transmission. Finally, a chromatic color correction method and de-blurring algorithm are used to recover the scene color. Experimental results demonstrate that the accurate estimation of the depth map and ambient light by the proposed method can recover visually appealing images with sharp details. [ABSTRACT FROM AUTHOR]

Miner’s wearable robot is an important mobile terminal of the monitoring network for the coal mine production safety. However, it is difficult to find the study on the miner’s emotion change using brain-computer interface (BCI) for miner’s wearable robot, especially for the smart helmet. This paper explores the anxiety change rule and the detection method using BCI of miner’s smart helmet. There are three contributions in this paper. Firstly, an emotional state evoked paradigm is designed to find the brain area where the emotion feature is most obvious. Then, the accurate electrode position is determined for the electroencephalograph (EEG) collection of the negative emotion on the basis of the international 10-20 systems. Secondly, a fusion algorithm of the anxiety level is proposed to evaluate the miner’s mental state by using the θ, α, and β rhythms of EEG. Thirdly, the miner’s smart helmet system is built to collect the human state which includes the mental parameters of the anxiety level, the fatigue level, the concentration level, and the environmental parameter in coal mine. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most obvious EEG changes appear within the first 2 s after the stimulator works. The amplitudes of the θ rhythm increase most obviously in the negative emotion. In addition, the fusion algorithm of the anxiety level parameter has a good following function to the negative emotion change. This method can reflect quantitatively the anxiety level of a human. So it is avoided for the miner to operate improperly, and then the operation safety can be improved. [ABSTRACT FROM AUTHOR]

Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles (AUVs), unmanned underwater vehicles (UUVs), and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of light's transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater imaging problems. This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers. We present an overview of various underwater image-processing approaches, such as underwater image de-scattering, underwater image color restoration, and underwater image quality assessments. Finally, we summarize the future trends and challenges in designing and processing underwater imaging sensors. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including the integration of cognitive computing and intelligent industrial internet of things (IoT) in indoor farming, the social group device-to-device (2D) sharing, and the health recommendation system.

Cognitive Computing breaks the boundary between two separate fields, neuroscience and computer science. Regarding the issue, Jiang et al. [[3]] consider the communication cost of virtual machine (VM) migration and proposes a VM Migration Algorithm based on Gene Aggregation Genetic Algorithm (VMM-GAGA). Mobile Netw Appl. 10.1007/s11036-019-01375-8 3 Jiang Y, Wang J, Shi J et al (2020) Network-aware virtual machine migration based on gene aggregation genetic algorithm. [Extracted from the article]

The integration of cognitive science and AI offers with a deep understanding of human cognition and communication. This special issue is forming connections between cognitive science and AI that examines human performance and design of engineering psychology. Thus, this special issue focusing on convergence of cognitive science and AI principles and methods would be helpful to improve the thinking ability skills of the humans in engineering psychology. [Extracted from the article]

The original version of this article unfortunately contained a mistake in the Affiliation section. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including the tracking of amateur unmanned aerial vehicles (UAV) using artificial intelligence, the detection of anxiety using miner's smart helmet, and the algorithm to get the social satisfaction of voters.