Reviewerly/exHarmony huggingface dataset:
DE-CycleGAN: An Object Enhancement Network for Weak Vehicle Detection in Satellite Images

Vehicle detection is a very important application of remote sensing. However, suffering from the low acutance and insufficient color information, the detection of weak vehicles in satellite imagery still remains a challenge. Image enhancement can improve the visual effects of remote sensing images. Nevertheless, most existing image enhancement methods aim to improve the quality of the entire image without target guidance, which have ambiguous contributions to the detection performance. Methods based on generative adversarial networks (GANs) have realized image enhancement with target guidance by the addition of target-guided branches, but paired training data is not available in some scenarios. In this article, a novel model of detection-guided CycleGAN (DE-CycleGAN) is proposed to enhance the weak targets for the purpose of accurate vehicle detection, where a backbone GAN with a target-guided branch is learned in the absence of paired images. Specifically, enhancements of two levels are mutually executed. At the image level, the color information of the entire satellite image is enriched by refined CycleGAN, and its sharpness is enhanced by the gradient enhancement model. At the object level, the target-guided branch for detection is added to enhance features of the target. The experimental results validate that the detection performance has been significantly improved on the images enhanced by the proposed DE-CycleGAN model, which shows a positive effect on weak target detection.
Data hiding in encryption–compression domain

Abstract This paper introduces a robust and secure data hiding scheme to transmit grayscale image in encryption-then-compression domain. First, host image is transformed using lifting wavelet transform, Hessenberg decomposition and redundant singular value decomposition. Then, we use appropriate scaling factor to invisibly embed the singular value of watermark data into the lower frequency sub-band of the host image. We also use suitable encryption-then-compression scheme to improve the security of the image. Additionally, de-noising convolutional neural network is performed at extracted mark data to enhance the robustness of the scheme. Experimental results verify the effectiveness of our scheme, including embedding capacity, robustness, invisibility, and security. Further, it is established that our scheme has a better ability to recover concealed mark than conventional ones at low cost.
TLSAN: Time-aware long- and short-term attention network for next-item recommendation

Recently, deep neural networks are widely applied in recommender systems for their effectiveness in capturing/modeling users’ preferences. Especially, the attention mechanism in deep learning enables recommender systems to incorporate various features in an adaptive way. Specifically, as for the next item recommendation task, we have the following three observations: 1) users’ sequential behavior records aggregate at time positions (“time-aggregation”), 2) users have personalized taste that is related to the “time-aggregation” phenomenon (“personalized time-aggregation”), and 3) users’ short-term interests play an important role in the next item prediction/recommendation. In this paper, we propose a new Time-aware Long- and Short-term Attention Network (TLSAN) to address those observations mentioned above. Specifically, TLSAN consists of two main components. Firstly, TLSAN models “personalized time-aggregation” and learn user-specific temporal taste via trainable personalized time position embeddings with category-aware correlations in long-term behaviors. Secondly, long- and short-term feature-wise attention layers are proposed to effectively capture users’ long- and short-term preferences for accurate recommendation. Especially, the attention mechanism enables TLSAN to utilize users’ preferences in an adaptive way, and its usage in long- and short-term layers enhances TLSAN’s ability of dealing with sparse interaction data. Extensive experiments are conducted on Amazon datasets from different fields (also with different size), and the results show that TLSAN outperforms state-of-the-art baselines in both capturing users’ preferences and performing time-sensitive next-item recommendation.
Protecting Personal Healthcare Record Using Blockchain &amp; Federated Learning Technologies

For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and realtime usage.
Refugees Welcome? Online Hate Speech and Sentiments in Twitter in Spain during the Reception of the Boat Aquarius

High-profile events can trigger expressions of hate speech online, which in turn modifies attitudes and offline behavior towards stigmatized groups. This paper addresses the first path of this process using manual and computational methods to analyze the stream of Twitter messages in Spanish around the boat Aquarius (n = 24,254) before and after the announcement of the Spanish government to welcome the boat in June 2018, a milestone for asylum seekers acceptance in the EU and an event that was highly covered by media. It was observed that most of the messages were related to a few topics and had a generally positive sentiment, although a significant part of messages expressed rejection or hate—often supported by stereotypes and lies—towards refugees and migrants and towards politicians. These expressions grew after the announcement of hosting the boat, although the general sentiment of the messages became more positive. We discuss the theoretical, practical, and methodological implications of the study, and acknowledge limitations referred to the examined timeframe and to the preliminary condition of the conclusions.
An Approach on Image Processing of Deep Learning Based on Improved SSD

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.
URCA-GAN: UpSample Residual Channel-wise Attention Generative Adversarial Network for image-to-image translation

Multimodal image-to-image translation is a challenging topic in computer vision. In image-to-image translation, an image is translated from a source domain to different target domains. For many translation tasks, the difference between the source image and the target image is only in the foreground. In this paper, we propose a novel deep-learning-based method for image-to-image translation. Our method, named URCA-GAN, is based on a generative adversarial network and it can generate images of higher quality and diversity than existing methods. We introduce Upsample Residual Channel-wise Attention Blocks (URCABs), based on ResNet and softmax channel-wise attention, to extract features associated with the foreground. The URCABs form a parallel architecture module named Upsample Residual Channel-wise Attention Module (URCAM) to merge features from the URCABs. URCAM is embedded after the decoder in the generator to regulate image generation. Experimental results and quantitative evaluations showed that our model has better performance than current state-of-the-art methods in both quality and diversity. Especially, the LPIPS, PSNR, and SSIM of URCA-GAN on CelebA dataset increase by 1.31%,1.66%, and 4.74% respectively, the PSNR and SSIM on RaFD dataset increase by 1.35% and 6.71% respectively. In addition, visualization of the features from URCABs demonstrates that our model puts emphasis on the foreground features.
Extracting Events and Their Relations from Texts: A Survey on Recent Research Progress and Challenges

Event is a common but non-negligible knowledge type. How to identify events from texts, extract their arguments, even analyze the relations between different events are important for many applications. This paper summaries some constructed event-centric knowledge graphs and the recent typical approaches for event and event relation extraction, besides task description, widely used evaluation datasets, and challenges. Specifically, in the event extraction task, we mainly focus on three recent important research problems: 1) how to learn the textual semantic representations for events in sentence-level event extraction; 2) how to extract relations across sentences or in a document level; 3) how to acquire or augment labeled instances for model training. In event relation extraction, we focus on the extraction approaches for three typical event relation types, including coreference, causal and temporal relations, respectively. Finally, we give out our conclusion and potential research issues in the future.
Virtual Surfaces and Attitude Aware Planning and Behaviours for Negative Obstacle Navigation

This letter presents an autonomous navigation system for ground robots traversing aggressive unstructured terrain through a cohesive arrangement of mapping, deliberative planning and reactive behaviour modules. All systems are aware of terrain slope, visibility and vehicle orientation, enabling robots to recognize, plan and react around unobserved areas and overcome negative obstacles, slopes, steps, overhangs and narrow passageways. This is one of pioneer works to explicitly and simultaneously couple mapping, planning and reactive components in dealing with negative obstacles. The system was deployed on three heterogeneous ground robots for the DARPA Subterranean Challenge, and we present results in Urban and Cave environments, along with simulated scenarios, that demonstrate this approach.
An Introduction to Practical Quantum Key Distribution

Quantum technology, and especially quantum computing, is advancing rapidly. Our every-day secure communication infrastructure relies heavily on public key cryptography; unfortunately, many public key schemes are in fact insecure against quantum algorithms. Furthermore, adversaries who, today, capture information encrypted using classical key distribution systems, can wait and, as soon as quantum computers of sufficient power become available, they may decipher all previously captured information. While some new “post-quantum” public key systems are assumed to be secure against quantum computers, this is only an assumption and, even if the assumption remains valid, still results in systems that are less efficient for end-users than the more commonly used public key systems employed today. While the advent of quantum technology may seem detrimental to security, in fact, one may harness the power of quantum through quantum key distribution (QKD) for stronger security guarantees. Such QKD protocols allow two (or more) parties to establish a shared secret key, secure against an all-powerful adversary. Perhaps surprising is that QKD technology is already here both experimentally and commercially. This tutorial serves as an introduction to basic QKD along with QKD technology from a practical perspective. Quantum communication is a highly interdisciplinary field of research and one of the goals of this article is to introduce a larger set of researchers and practitioners to its study in the hopes of furthering its progress and eventual wide-scale adoption.
Joint Clustering and Blockchain for Real-Time Information Security Transmission at the Crossroads in C-V2X Networks

The cellular vehicle-to-everything (C-V2X) networks support diverse kinds of services, such as traffic management, road safety, and sharing data. However, the safety issues cannot be ignored in the process of information transmission. In this article, a joint clustering and blockchain scheme is proposed for real-time information security transmission to prevent some vehicles from sending malicious messages to disrupt the traffic order at the crossroads in C-V2X networks. In this scheme, the dynamic stability of the cluster is maintained by updating the trust value of the vehicle nodes, which can improve the real time and accuracy of the information transmission. The modified Webster algorithm is presented to divert the traffic flow so as to reduce the traffic jams at the crossroads. Meanwhile, the blockchain technology is utilized to establish a vehicle trust management mechanism in C-V2X, which can avoid malicious tampering of vehicle information during information sharing and ensure the safety of vehicle information communication. The simulation results of the Veins simulation platform are provided to demonstrate the effectiveness of the proposed algorithm and verify that the proposed scheme can guarantee the security of real-time information transmission.
Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.
Performance Analysis of Daily Global Solar Radiation Models in Peru by Regression Analysis

Solar radiation (Rs) is one of the main parameters controlling the energy balance at the Earth’s surface and plays a major role in evapotranspiration and plant growth, snow melting, and environmental studies. This work aimed at evaluating the performance of seven empirical models in estimating daily solar radiation over 1990–2004 (calibration) and 2004–2010 (validation) at 13 Peruvian meteorological stations. With the same variables used in empirical models (temperature) as well as two other parameters, namely precipitation and relative humidity, new models were developed by multiple linear regression analysis (proposed models). In calibration of empirical models with the same variables, the lowest estimation errors were 227.1 and 236.3 J·cm−2·day−1 at Tacna and Puno stations, and the highest errors were 3958.4 and 3005.7 at San Ramon and Junin stations, respectively. The poorest-performing empirical models greatly overestimated Rs at most stations. The best performance of a proposed model (in terms of percentage of error reduction) was 73% compared to the average of all empirical models and 93% relative to the poorest result of empirical models, both at San Ramon station. According to root mean square errors (RMSEs) of proposed models, the worst and the best results are achieved at San Martin station (RMSE = 508.8 J·cm−2·day−1) and Tacna station (RMSE = 223.2 J·cm−2·day−1), respectively.
Education enrolment rate vs employment rate: Implications for sustainable human capital development in Nigeria

The study examines the dynamic interrelationships among the school enrolment rates and the rate of employment (via unemployment rates) in Nigeria. The study employed Autoregressive estimates and an unrestricted VAR approach to analyze these relationships. The study lends credence to the new-growth theory (i.e. endogenous models) that more investments in human capital, through education especially at higher levels, will allow human capital to evolve dynamically to increase long-run growth positively in Nigeria. This tendency engenders multiplier effects in stimulating sustainable human capital development given that education-driven growth facilitates employment in the short-run. However, to sufficiently sustain human capital development to generate employment in the long-run, it appears there is need to combine education with other cooperant factors such as social safety nets, good governance, private sector development and efficient utilization of human and physical resources. The growth literature has been definitive on the role of human capital in achieving long-run economic growth. Therefore, investments in education have been identified as a vital channel for building human capital and achieving long run development objectives. Thus, in the nascent quest for sustainable development, this study takes the new growth theory a step higher by examining the modulating effects of education-driven growth (i.e. via school enrolments rates) in setting the pace for employment patterns in Nigeria.
Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a "near"-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional-integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.
Augmenting Ontology Alignment by Semantic Embedding and Distant Supervision

Ontology alignment plays a critical role in knowledge integration and has been widely investigated in the past decades. State of the art systems, however, still have considerable room for performance improvement especially in dealing with new (industrial) alignment tasks. In this paper we present a machine learning based extension to traditional ontology alignment systems, using distant supervision for training, ontology embedding and Siamese Neural Networks for incorporating richer semantics. We have used the extension together with traditional systems such as LogMap and AML to align two food ontologies, HeLiS and FoodOn, and we found that the extension recalls many additional valid mappings and also avoids some false positive mappings. This is also verified by an evaluation on alignment tasks from the OAEI conference track.
Synthesis of a Path-Planning Algorithm for Autonomous Robots Moving in a Game Environment during Collision Avoidance

This paper describes and illustrates the optimization of a safe mobile robot control process in collision situations using the model of a multistep matrix game of many participants in the form of a dual linear programming problem. The synthesis of non-cooperative and cooperative game control software was performed in Matlab/Simulink software to determine the safe path of the robot when passing a greater number of other robots and obstacles. The operation of the game motion control algorithm of a mobile robot is illustrated by computer simulations made in the Matlab/Simulink program of two real previously recorded navigation situations while passing dozens of other autonomous mobile robots.