Reviewerly/exHarmony huggingface dataset:
Quantum evolutionary algorithm hybridized with Enhanced colliding bodies for optimization

New methods based on meta-heuristic approaches for solving hard problems have become efficient tools in many disciplines such as engineering, computer science, mathematics, and some other fields. Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. The original version of the quantum evolutionary algorithm (QEA) uses Q-bit individuals in binary code analogous to genes in the conventional genetic algorithm and the concept of quantum computing. Getting stuck in local optimum in some case studies is considered a weakness of primary QEA and makes access to the high standard deviation in statistical analysis. The idea of a combination of two or more methods can create a new approach with augmented features of all the combined methods. QECBO, as a high-level relay hybrid technique, is a new quantum version of the ECBO, which combines ECBO with the property of the quantum particle. QECBO gives quantum particle properties to colliding bodies of solution. The main idea of QECBO originates from the concept of quantum particle behavior in delta potential well and Schrödinger's uncertainty principle addition to Newtonian collision laws. It seems that the QECBO utilizes the rules of uncertainty inside classical mechanics. This paper is devoted to the assessment of the QECBO using some well-known benchmark optimization problems. In addition to less sensitive to population size, the quality of obtained results implies that QECBO utilizes an advanced approach to search solution space based on ECBO and balances exploration and exploitation concepts by using uncertainty laws. The inclination of the agents to the best solution in each iteration could provide a better chance to find an optimal solution. Also, efficient randomization due to uncertainty laws helps the algorithm to escape from the local optimum. Attained statistical results show the QECBO could be a new advanced method that would avoid local optimum and provide an excellent balance between intensification and diversification.
Analysis of publicly available anti-phishing webpages: contradicting information, lack of concrete advice and very narrow attack vector

Phishing is currently one of the biggest threats in cybersecurity for both the business and the private contexts. A large percentage of phishing attacks are blocked by automated technical solutions, but unfortunately there is often a delay between when phishing emails enter inboxes and when the technical solutions are able to detect and filter them out. To close this gap, it is common practice for companies to implement mandatory phishing awareness measures for their employees. But what about the private context? We aimed at answering that question by analysing 94 anti-phishing webpages from eight different countries and four organisation types. Our analysis revealed not only contradicting recommendations, but also that most of them are rather abstract (e.g. check the URL before clicking on the link without telling what to look for) and lack guidance on advanced phishing techniques (e.g. clone phishing). We discuss the problems faced by readers of these webpages and outline both immediate recommendations to the web designer and ways forward to improve the current situation as future work.
Deep learning-based super-resolution images for synchronous measurement of temperature and deformation at elevated temperature

Measuring temperature and deformation at elevated temperature has recently been a major concern for the engineering tests and the quality improvement of captured images is critical to it. Here, we propose a simple, high-precision and easy-to-implement technique combing the image capturing and processing methods to obtain the high-resolution images as well as the corresponding temperature and deformation fields. The super-resolution convolutional neural network (SRCNN) algorithm is used for the super-resolution reconstruction, while the deformation field is calculated by the DIC method and the temperature field is synchronous obtained by the improved two-color method. Flame heating experiment of the SiC material validated the applicability and the improvement of the proposed method, showing the improvement in image quality (with PSNR increased 16.7 %) and calculation accuracy of temperature and deformation (with error decreased 3.57 %). Meanwhile, the phenomenon of "feature point drift" is pointed out and the error of deformation measurement for high-resolution and low-resolution is analyzed. Furthermore, the sub-pixel edge detection can be achieved by the combination of the proposed method and the edge detection method, which shows a potential value in detecting of surface defects or cracks.
Mining information from sentences through Semantic Web data and Information Extraction tasks

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.
Incentive Assignment in PoW and PoS Hybrid Blockchain in Pervasive Edge Environments

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system in edge environments to enhance the security for transactions and determine the incentive for miners. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem that gives the corresponding PoW miner when a new block generates. We further formulate it into a two-stage Stackelberg game. We propose an algorithm and prove that it can obtain the global optimal results for the incentive that the miner will receive for a new block. Numerical simulation results show that our proposed algorithm can give reasonable incentive to miners under different system parameters in edge blockchain systems.
Efficient subpixel image registration algorithm for high precision visual vibrometry

As one of the most reliable motion estimation algorithms that apply phase correlation methods, the single-step DFT (SSDFT) approach has superior characteristics, including the high accuracy and low complexity. However, this approach is limited by the accuracy of the initial estimation. Therefore, it is an enormous challenge to reduce the dimension of the searching area in the subsequent refinement step. As a result, this algorithm is inefficient with large upsampling factors. In order to overcome this problem, an improved two-step image registration algorithm is proposed in the present study. In the first step, the accuracy of the initial estimation is improved by using a motion amplified cross-correlation function. The improved initial estimation is then amended to ensure that retained error is eliminated. In the refinement step, the dimension of the searching area is effectively reduced in accordance with the improved initial estimation and upsampling factor. Obtained results show that for large upsampling factors, the modified SSDFT achieves the same subpixel accuracy as the original algorithm. Meanwhile, it is found that the modified scheme remarkably reduces the computational expense. Finally, conducted experiments on high-speed video sequences demonstrate that the proposed modifications significantly reduce the required time for high precision target tracking tasks.
VisConnect: Distributed Event Synchronization for Collaborative Visualization

Tools and interfaces are increasingly expected to be synchronous and distributed to accommodate remote collaboration. Yet, adoption of these techniques for data visualization is low partly because development is difficult: existing collaboration software systems either do not support simultaneous interaction or require expensive redevelopment of existing visualizations. We contribute VisConnect: a web-based synchronous distributed collaborative visualization system that supports most web-based SVG data visualizations, balances system safety with responsiveness, and supports simultaneous interaction from many collaborators. VisConnect works with existing visualization implementations with little-to-no code changes by synchronizing low-level JavaScript events across clients such that visualization updates proceed transparently across clients. This is accomplished via a peer-to-peer system that establishes consensus among clients on the per-element sequence of events, and uses a lock service to grant access over elements to clients. We contribute collaborative extensions of traditional visualization interaction techniques, such as drag, brush, and lasso, and discuss different strategies for collaborative visualization interactions. To demonstrate the utility of VisConnect, we present novel examples of collaborative visualizations in the healthcare domain, remote collaboration with annotation, and show in an education case study for e-learning with 22 participants that students found the ability to remotely collaborate on class activities helpful and enjoyable for understanding concepts. A free copy of this paper and source code are available on OSF at osf.io/ut7e6 and at visconnect.us.
Augmented Reality for Robot Control in Low-cost Automation Context and IoT

Augmented Reality (AR) is one of the key technology in the Industry 4.0 development. The robotic manufacturing is very useful because of their versatility and repeatability in industrial tasks. However, as technologies advance, complex and high-cost control systems are required, hence the need to create low-cost and efficient control systems, with low complexity when operating them. A virtual control proposed in Unity 3D allowing the application to be much more interactive and very easy for the user. The authors describe the working scenario, the overall architecture, communication protocol in the context of the Internet of Things (IoT) and give design and implementation details on the AR application.
Dynamic Modeling of a Transformable Quadrotor

This paper proposes a new design of the well-known quadrotor UAV. This design is based on a variable morphology that can be modified instantaneously during the flight, according to the tasks and the followed trajectory. It is able to change the rotation and the extension of its arms independently. This quadrotor, exploits simple mechanisms i.e. extendable arms and rotating arms. The modeling of this category of quadrotors is not obvious compared to the conventional ones because of its asymmetry. So, a detailed generic model that takes into account the variation of the center of gravity and the inertia is presented. The first prototype platform with rotating arms and some possible configurations will be shown.
SAFDet: A Semi-Anchor-Free Detector for Effective Detection of Oriented Objects in Aerial Images

An oriented bounding box (OBB) is preferable over a horizontal bounding box (HBB) in accurate object detection. Most of existing works utilize a two-stage detector for locating the HBB and OBB, respectively, which have suffered from the misaligned horizontal proposals and the interference from complex backgrounds. To tackle these issues, region of interest transformer and attention models were proposed, yet they are extremely computationally intensive. To this end, we propose a semi-anchor-free detector (SAFDet) for object detection in aerial images, where a rotation-anchor-free-branch (RAFB) is used to enhance the foreground features via precisely regressing the OBB. Meanwhile, a center-prediction-module (CPM) is introduced for enhancing object localization and suppressing the background noise. Both RAFB and CPM are deployed during training, avoiding increased computational cost of inference. By evaluating on DOTA and HRSC2016 datasets, the efficacy of our approach has been fully validated for a good balance between the accuracy and computational cost.
Simulating Difficulty Adjustment in Blockchain with SimBlock

A blockchain is a gradually increasing list of "blocks" containing information that are linked together using cryptographic hashes. It is a distributed, decentralized, and public digital ledger technology. Blockchains have caught not only the attention of researchers but also industries that are interested in implementing blockchains into existing products and services. However, performing studies on actual blockchain networks remain challenging as they may involve many nodes or these nodes could be placed in different geographical regions. Simulators for Proof-of-Work blockchain exists to facilitate this, but features such as difficulty adjustment or dynamic hash rate are not available. Using SimBlock, a blockchain network simulator as a base, difficulty adjustment algorithm and the capacity to increase or decrease hash rate dynamically were added to the simulator and the efficacy of the implementation was investigated for two scenarios; fixed hash rate and increasing hash rate periodically, over the length of the simulations. Based on the experiments, higher precision in terms of the average of actual time taken to mine a block were obtained, with a deviation of 0.68% compared to 5.73% in original SimBlock, and a deviation of 7.10% compared to 10.91% in original SimBlock, for fixed hash rate and dynamic hash rate adjustment respectively. Further studies were conducted for data from 2019 where the proposed implementation resulted in less than half the deviation achieved by the original implementation of SimBlock.
Herring: Rethinking the Parameter Server at Scale for the Cloud

Training large deep neural networks is time-consuming and may take days or even weeks to complete. Although parameter-server-based approaches were initially popular in distributed training, scalability issues led the field to move towards all-reduce-based approaches. Recent developments in cloud networking technologies, however, such as the Elastic Fabric Adapter (EFA) and Scalable Reliable Datagram (SRD), motivate a re-thinking of the parameter-server approach to address its fundamental inefficiencies. To this end, we introduce a novel communication library, Herring, which is designed to alleviate the performance bottlenecks in parameter-server-based training. We show that gradient reduction with Herring is twice as fast as all-reduce-based methods. We further demonstrate that training deep learning models like $\mathrm{B}\mathrm{E}\mathrm{R}\mathrm{T}_{\mathrm{l}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}}$ using Herring outperforms all-reduce-based training, achieving 85% scaling efficiency on large clusters with up to 2048 NVIDIA V100 GPUs without accuracy drop.
A new welding path planning method based on point cloud and deep learning

Scan the weldment by 3D vision sensors to obtain the 3D point cloud data that can accurately describe the spatial position information of the weldment to enable automatic welding. Accurate point cloud segmentation and optimized path generation are the two pivotal issues that need to be solved in robot welding path planning. To cope with the highly redundant and uneven dense point clouds, this paper proposed a deep learning based welding path planning method with a real missile air rudders welding case. According to the structural characteristics of air rudder products, a 3D point cloud segmentation method for air rudders is designed. A convolutional neural network segmentation model for air rudder 3D point clouds is proposed to accurately segment weldment. Then, an extraction method for the characteristic points of the air rudder welding seam and the curve fitting method for the irregular welding path are designed to generate an accurate air rudder welding path. Finally, the effectiveness of the proposed method is verified by eight types of air rudder products. The results show that the proposed method achieves competitive performance than other comparative methods.
AP-GAN: Predicting skeletal activity to improve early activity recognition

Early activity recognition is a classification task before the completion of activity. The study of early activity recognition is beneficial to avoid serious result. Previous studies have focused on extracting effective activity features and modeling for quick and accurate classification. It is challenging because of lack of available information. In order to get a firm basis for judgment, this paper adds an activity prediction module prior to recognition module. The main task of the module is to predict subsequent motions according to observed motions. To avoid motion blur, the structure of GAN (Generative Adversarial Networks) is used to generate the predicted motions. Compared with the traditional deep learning model, dilated neural network has advantages in large-span spatiotemporal feature modeling. The dilated RNN (Recurrent Neural Networks) and CNN (Convolutional Neural Networks) are introduced to the recognition module. In order to make the activity prediction and recognition modules work together, this paper designs and introduces a hard class mining mechanism to improve the learning ability of hard class samples. The proposed method is validated on four skeletal activity datasets and achieves state-of-the-art accuracy.
Feature Sparse Coding With CoordConv for Side Scan Sonar Image Enhancement

In this letter, we propose a learning-based compressive sensing (CS) algorithm for denoising side scan sonar (SSS) images. The proposed method is a deep learning-based CS method with enhanced nonlinearity based on an iterative shrinkage and thresholding algorithm (ISTA). Since noise intensity varies depending on the position within SSS images, the proposed method also incorporates CoordConv, which provides coordinate information to the network to help remove nonhomogeneous noise. Through end-to-end training, both the deep learning module and the CS characteristics can be jointly optimized. Representative experimental results show that the proposed method is better than state-of-art methods in terms of both noise removal and memory requirements.
Development of Sign Language using Flex Sensors

Sign Language is not familiar to those who are not deaf or dumb. When humans talk to each other, they convey their words through both speech and gestures. For a deaf and dumb individual, it is really difficult to lead a normal life with such a big communication barrier. This affects their social life as well as relationships. So, this framework is developed to assist them and help them in conveying their message easily. The creation of such a device involves both electronics and computer engineering knowledge. It will include flex sensors, Arduino, Bluetooth module, accelerometer, glove and an android application to display the results. The framework plans to reduce the communication barrier between a normal individual and a deaf/dumb individual. Also, the aim is to realize the gaps and options available in this field to further improve the framework.
Visual-Textual Sentiment Analysis Enhanced by Hierarchical Cross-Modality Interaction

Visual-textual sentiment analysis could benefit user understanding in online social networks and enable many useful applications like user profiling and recommendation. However, it faces a set of new challenges, i.e., exacerbated noise problem caused by irrelevant or redundant information in different modalities, and the gap in joint understanding for multimodal sentiment. In this article, we propose hierarchical cross-modality interaction model for visual-textual sentiment analysis. Our model emphasises the consistency and correlation across modalities, by extracting the semantic and sentiment interactions between image and text in a hierarchical way, which could cope with the noise and joint understanding issues, respectively. Hierarchical attention mechanism is first adopted to capture the semantic interaction and purify the information in one modality with the help of the other. Then, multimodal convolutional neural network, which could fully exploit cross-modality sentiment interaction is incorporated, and better joint visual-textual representation is generated. A transfer learning method is further designed to alleviate the impact of noises in real social data. Through extensive experiments on two datasets, we show that our proposed framework greatly surpasses the state-of-the-art approaches. Specifically, the phrase-level text fragment plays an important role in interacting with image regions for joint visual-textual sentiment analysis.
Bayesian robust multi-extreme learning machine

Outliers usually exist in the collected data due to human or instrumentation errors. Their existence makes the regression error obey a non-Gaussian distribution. So, people assume that the regression error obeys finite mixture distributions, such as mixture of Gaussians and mixture of Student’s t-distributions. This paper proposed two Bayesian robust regression models based on multiple extreme learning machines. First, the finite mixture error distributions are extended to their infinite counterparts according to the theory of stick-breaking construction, which can avoid selecting the optimal number of components in mixture models. Second, a multi-extreme learning machine, which combines many single extreme learning machines with different numbers of hidden nodes, is constructed, which can avoid the effects of different numbers of hidden nodes on the final regression performance. Besides, sparse Bayesian learning has also been derived to perform the sparse model weight and output weight inference automatically. The experimental results on artificial datasets and eight benchmark datasets show that the proposed two models outperform the other compared models under different rates of outliers. Also, they generate better multi-step ahead wind speed and traffic speed forecasts in real applications.
Wilderness Search and Rescue with Heterogeneous Multi-Robot Systems

Recent developments in autonomous and communication technologies led the use of cooperative aerial and ground vehicles in Wilderness Search and Rescue missions (WiSAR). The use of a heterogeneous Multi-Robot System (MRS) improves the robustness and efficiency in achieving these tasks, comparing to the homogeneous systems with vehicles of the same characteristics. From this point, this paper proposed a Multi-Robot Task Allocation (MRTA) algorithm for heterogeneous vehicles (UAVs and UGVs) using the Market-based Approach to optimize the mission resources. The algorithm checks the availability of the vehicles, the characteristics of each task, and the payload required as inputs, then it provides each vehicle with a plan of tasks and charging commands. The proposed algorithm has been validated by performing several missions in simulations of mountains terrain with real dimensions, and the obtained results show the robustness and efficiency of the proposed system.