Reviewerly/exHarmony huggingface dataset:
Intelligent Path Planning of Underwater Robot Based on Reinforcement Learning

As one of the commonly used vehicles for underwater detection, underwater robots are facing a series of problems. The real underwater environment is large-scale, complex, real-time and dynamic, and many unknown obstacles may exist in the underwater environment. Under such complex conditions and lack of prior knowledge, the existing path planning methods are difficult to plan, therefore they cannot effectively meet the actual demands. In response to these problems, a three-dimensional marine environment including multiple obstacles is established with the real ocean current data in this paper, which is consistent with the actual application scenarios. Then, we propose an N-step Priority Double DQN (NPDDQN) path planning algorithm, which potently realizes obstacle avoidance in the complex environment. In addition, this study proposes an experience screening mechanism, which screens the explored positive experience and improves its reuse rate, thus efficiently improving the algorithm stability in the dynamic environment. This paper verifies the better performance of reinforcement learning compared with a variety of traditional methods in three-dimensional underwater path planning. Underwater robots based on the proposed method have good autonomy and stability, which provides a new method for path planning of underwater robots. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The goal of this study is to provide a new solution for obstacle avoidance in path planning of underwater robots, which is consistent with the dynamic and real-time demands of the real environment. Existing underwater path planning researches lack a consistent environment with the actual application, and therefore we firstly construct a three-dimensional ocean environment with real ocean current data to provide support for the algorithms. Additionally, most of the algorithms are pre-planning methods or require long-time calculation, and there is little research on obstacle avoidance. In the face of obstacle changes, underwater robots with poor adaptability will cause performance decline and even economic losses. The proposed algorithm learns through interaction with the environment, and therefore it does not require any prior experience, and has good adaptability as well as fast inference speed. Especially, in the dynamic environment, algorithm performance is difficult to guarantee due to less positive experience in exploration. The proposed experience screening mechanism improves the stability of the algorithm, so that the underwater robot maintains stable performance in different dynamic environments.
An Efficient Certificate-Based Aggregate Signature Scheme With Provable Security for Industrial Internet of Things

In Industrial Internet of Things (IIoT), the certificate-based aggregate signature (CBAS) scheme was used to ensure privacy and security of IIoT, thus, the concrete construction of CBAS scheme without bilinear pairing has proposed, which was employed as an underlying building block to create an efficient data aggregation protocol. In this article, we first point out that the previous CBAS scheme cannot keep its claimed security. Particularly, a malicious KGC can compute a valid forgery signature, because it has the knowledge of the master secret key. After that, we discussed how specific forgery attacks could be made to against the existing CBAS schemes, which demonstrates that the previous related constructions cannot achieve its claimed security. In order to further provide a secure CBAS scheme for IIoT, we suggest a method to improve the CBAS scheme and propose a concrete construction of it. In the random oracle model, based on the hardness of discrete logarithm problem, the security of our new proposal can be proved. Compared with the previous CBAS schemes, our construction has better security, which is efficient with respect to computation and communication.
Fingerprinting Technique for YouTube Videos Identification in Network Traffic

Recently, many video streaming services, such as YouTube, Twitch, and Facebook, have contributed to video streaming traffic, leading to the possibility of streaming unwanted and inappropriate content to minors or individuals at workplaces. Therefore, monitoring such content is necessary. Although the video traffic is encrypted, several studies have proposed techniques using traffic data to decipher users&#x2019; activity on the web. Dynamic Adaptive Streaming over HTTP (DASH) uses Variable Bit-Rate (VBR) - the most widely adopted video streaming technology, to ensure smooth streaming. VBR causes inconsistencies in video identification in most research. This research proposes a fingerprinting method to accommodate for VBR inconsistencies. First, bytes per second (BPS) are extracted from the YouTube video stream. Bytes per Period (BPP) are generated from the BPS, and then fingerprints are generated from these BPPs. Furthermore, a Convolutional Neural Network (CNN) is optimized through experiments. The resulting CNN is used to detect YouTube streams over VPN, Non-VPN, and a combination of both VPN and Non-VPN network traffic.
FISS: A Trajectory Planning Framework Using Fast Iterative Search and Sampling Strategy for Autonomous Driving

Trajectory planning is a critical component in autonomous vehicles directly responsible for driving safety and efficiency during deployment. The ability to find the optimal trajectory in real-time is critical for autonomous driving. This paper presents a novel general framework using the Fast Iterative Search and Sampling (FISS) strategy for sampling-based trajectory planning, which can find the optimal trajectory from an enormous number of candidates with high efficiency in real-time. Specifically, before generating any trajectories, the proposed method utilizes historical planning results as prior information in heuristics to estimate the cost distribution over the sampling space. On this basis, the Fast Iterative Search and Sampling strategy is employed to explore the sampling space for possible candidates and generate trajectories for verification during the search process. Experimental results show that our method can significantly outperform existing frameworks by order of magnitude in planning efficiency while ensuring safety and maintaining high accuracy.
Towards Generalized Welding Ontology in Line with ISO and Knowledge Graph Construction

Motivation. Industry 4.0 [1, 2] comes with unprecedented amounts of heterogeneous industrial data [3–5].
DIM-DS: Dynamic Incentive Model for Data Sharing in Federated Learning Based on Smart Contracts and Evolutionary Game Theory

With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data. First, we integrate the incentives based on reputation and payment and introduce “credibility coins” as a cryptocurrency for data-sharing transactions, to encourage users to participate honestly in the data-sharing process based on federated learning. Second, we propose a dynamic incentive model based on the evolutionary game theory to model the game process of users in data sharing and analyze the stability of their strategies. Finally, based on the results of this analysis, we use the blockchain-based smart contract technology to dynamically adjust the participation benefits of users under different conditions in order to promote users to join consortium blockchains more often and steadily to participate in model training for federated learning and obtain better model accuracy. Our work is the first to apply the evolutionary game theory to the study of incentives in federated learning, and plays a leading role in the study of incentives in federated learning. Experimental simulation validation shows that our DIM-DS model can adequately motivate users to participate in the collaborative task of data sharing and maintain stability. The model can maximize the effectiveness of the federated learning model.
HiGAN+: Handwriting Imitation GAN with Disentangled Representations

Humans remain far better than machines at learning, where humans require fewer examples to learn new concepts and can use those concepts in richer ways. Take handwriting as an example, after learning from very limited handwriting scripts, a person can easily imagine what the handwritten texts would like with other arbitrary textual contents (even for unseen words or texts). Moreover, humans can also hallucinate to imitate calligraphic styles from just a single reference handwriting sample (that even have never seen before). Humans can do such hallucinations, perhaps because they can learn to disentangle the textual contents and calligraphic styles from handwriting images. Inspired by this, we propose a novel handwriting imitation generative adversarial network (HiGAN+) for realistic handwritten text synthesis based on disentangled representations. The proposed HiGAN+ can achieve a precise one-shot handwriting style transfer by introducing the writer-specific auxiliary loss and contextual loss, and it also attains a good global &amp; local consistency by refining local details of synthetic handwriting images. Extensive experiments, including human evaluations, on the benchmark dataset validate our superiority in terms of visual quality, scalability, compactness, and style transferability compared with the state-of-the-art GANs for handwritten text synthesis.
Based on Quantum Topological Stabilizer Color Code Morphism Neural Network Decoder

Solving for quantum error correction remains one of the key challenges of quantum computing. Traditional decoding methods are limited by computing power and data scale, which restrict the decoding efficiency of color codes. There are many decoding methods that have been suggested to solve this problem. Machine learning is considered one of the most suitable solutions for decoding task of color code. We project the color code onto the surface code, use the deep Q network to iteratively train the decoding process of the color code and obtain the relationship between the inversion error rate and the logical error rate of the trained model and the performance of error correction. Our results show that through unsupervised learning, when iterative training is at least 300 times, a self-trained model can improve the error correction accuracy to 96.5%, and the error correction speed is about 13.8% higher than that of the traditional algorithm. We numerically show that our decoding method can achieve a fast prediction speed after training and a better error correction threshold.
Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach

Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.
High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm

This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (High-Efficiency Video Coding) codec. The aim of the article is to present a method of embedding a watermark in a video with HEVC codec compression by making changes in a video in a way that is not noticeable to the naked eye. The method presented here is characterised by focusing on ensuring the accuracy of the original image in relation to the watermarked image, providing the transparency of the embedded watermark, while ensuring its survival after compression by the HEVC codec. The article includes a presentation of the practical results of watermark embedding with a built-in variation mechanism of its capacity and resistance, thanks to the adjustable subsquares properties algorithm. The obtained PSNR (peak signal-to-noise ratio) results are at the level of 40 dB or better. There is the possibility of the complete recovery of a watermark from a single frame compressed in the CRF (constant rate factor) range of up to 16, resulting in a BER (bit error rate) equal to 0 for the received watermark.
HD maps: Exploiting OpenDRIVE potential for Path Planning and Map Monitoring

Autonomous vehicle (AV) is one of the most challenging engineering tasks of our era. High-Definition (HD) maps are a fundamental tool in the development of AVs, being considered as pseudo sensors that provide a trusted baseline that other sensors cannot. Our approach is focused on the use of OpenDRIVE standard based HD maps in order to conduct the different mapping and planning tasks involved in Autonomous Driving (AD). In this paper we present a method for exploiting the HD map potential for two specific purposes: i) Global Path Planning and ii) Monitoring the relevant lanes and regulatory elements around the ego-vehicle to support the perception module. Mapping and planning modules are connected to the other modules of the AV stack by using ROS (Robot Operating System). Our AD architecture has been validated both in local and CARLA Autonomous Driving Leaderboard cloud, where we can appreciate a considerable improvement in the metrics by incorporating information from the HD map, not only used to conduct the Global Path Planning task but also providing prior information to the Perception module. Code is available in https://github.com/AlejandroDiazD/opendrive-mapping-planning.
Where Should I Look at? Recommending Lines that Reviewers Should Pay Attention To

Code review is an effective quality assurance practice, yet can be time-consuming since reviewers have to carefully review all new added lines in a patch. Our analysis shows that at the median, patch authors often waited 15&#x2013;64 hours to receive initial feedback from reviewers, which accounts for 16&#x0025;-26&#x0025; of the whole review time of a patch. Importantly, we also found that large patches tend to receive initial feedback from reviewers slower than smaller patches. Hence, it would be beneficial to reviewers to reduce their effort with an approach to pinpoint the lines that they should pay attention to. In this paper, we proposed REVSPOT-a machine learning-based approach to predict problematic lines (i.e., lines that will receive a comment and lines that will be revised). Through a case study of three open-source projects (i.e., Openstack Nova, Openstack Ironic, and Qt Base), Revspot can accurately predict lines that will receive comments and will be revised (with a Top-10 Accuracy of 81&#x0025; and 93&#x0025;, which is 56&#x0025; and 15&#x0025; better than the baseline approach), and these correctly predicted problematic lines are related to logic defects, which could impact the functionality of the system. Based on these findings, our Revspot could help reviewers to reduce their reviewing effort by reviewing a smaller set of lines and increasing code review speed and reviewers&#x0027; productivity.
A Hierarchical Deep Reinforcement Learning Framework With High Efficiency and Generalization for Fast and Safe Navigation

We present a hierarchical deep reinforcement learning (DRL) framework with prominent sampling efficiency and sim-to-real transfer ability for fast and safe navigation: the low-level DRL policy enables the robot to move towards the target position and keep a safe distance to obstacles simultaneously; the high-level DRL policy is supplemented to further enhance the navigation safety. We select a waypoint located on the path from the robot to the ultimate goal as the sub-goal to reduce the state space and avoid sparse reward. Moreover, the path is generated based on either a local or a global map, which can significantly improve the sampling efficiency, safety, and generalization ability of the proposed DRL framework. Additionally, a target-directed representation for the action space can be derived based on the sub-goal to improve the motion efficiency and reduce the action space. In order to demonstrate the eminent sampling efficiency, motion performance, obstacle avoidance, and generalization ability of the proposed framework, we implement sufficient comparisons with the non-learning navigation methods and DRL-based baselines, with videos, data, code, and other supplemental material shown on our website<xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label> https://github.com/zw199502/RL <inline-formula><tex-math notation="LaTeX">$\_$</tex-math></inline-formula> navigation </fn>.
Mayfly Sparrow Search Hybrid Algorithm for RFID Network Planning

The radio frequency identification (RFID) technology is frequently used for large-scale RFID network planning, which brings thought-provoking RFID network planning (RNP) problems such as the uncertain number of readers, high interference rate of readers, poor economic benefits, and weak robustness. In this paper, we propose a mayfly sparrow search hybrid algorithm with Circle chaotic map and Lévy flight, which is named MASSA. MASSA maps out the position of the readers by adding the idea of the mayfly algorithm (MA) into the optimization process of the sparrow search algorithm (SSA). Circle chaos mapping and Lévy flight are added in the SSA optimization process and the nonlinear inertia coefficients to balance the relationship between global search and local search. The MASSA is compared with SSA, Improved sparrow search algorithm (ISSA), MA, particle swarm optimization algorithm (PSO), and gray wolf algorithm (GWO). The first part of the experiment uses the CEC2017 test suite to evaluate algorithm performance and analysis strategy effectiveness. The results encourage the application of MASSA to solve RNP problems. In the second part, experiments were conducted with four objective functions of coverage, interference, power, and load balance. Two different experiments were carried out with fixed number of readers and flexible number of readers under random label conditions. Experimental results show that MASSA has good planning performance, better economy, and robustness.
Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase’s research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges.
A reinforced active learning approach for optimal sampling in aspect term extraction for sentiment analysis

Aspect level sentiment analysis is a fine grained task in sentiment analysis which identifies the product features from an opinionated piece of text and maps the sentiment towards each of them. Supervised ML algorithms have reported comparatively higher performance on aspect level sentiment analysis but at the cost of substantial qualitative labelled data. Data labelling for such fine grained tasks also demand domain knowledge and expertise. Hence a mechanism to extract a minimal informative subset which is almost representative of the entire data would be a breakthrough in bringing down the annotation costs to a large extent. The proposed methodology puts forward an active learning based sampling strategy for aspect term extraction, a subtask in aspect level sentiment analysis which identifies the product features. The sampling strategy is automated by reinforcement learning which extracts an optimal sample from the entire unlabelled training data and hence optimizes data annotation by reducing the time and effort linked to the labelling process. This work is of high importance in a data driven era where companies invest a lot in collecting and annotating huge volumes of data. The model has been experimented across the laptop and restaurant domains of SemEval(2014-2016) datasets. The experiments proved that a considerable reduction of the training data size is achieved across different datasets. The model trained on the data extracted by the proposed reinforced active learning model beats random sampling by 9 to 17 points when evaluated on the F-measure of the extracted aspect terms and is almost on par with the model trained on the entire training data by utilising hardly 9 to 13% of the entire training data across the datasets experimented.
Investigating the Effectiveness of Clustering for Story Point Estimation

Automated techniques to estimate Story Points (SP) for user stories in agile software development came to the fore a decade ago. Yet, the state-of-the-art estimation techniques&#x0027; accuracy has room for improvement. In this paper, we present a new approach for SP estimation, based on analysing textual features of software issues by employing latent Dirichlet allocation (LDA) and clustering. We first use LDA to represent issue reports in a new space of generated topics. We then use hierarchical clustering to agglomerate issues into clusters based on their topic similarities. Next, we build estimation models using the issues in each cluster. Then, we find the closest cluster to the new coming issue and use the model from that cluster to estimate the SP. Our approach is evaluated on a dataset of 26 open source projects with a total of 31,960 issues and compared against both baselines and state-of-the-art SP estimation techniques. The results show that the estimation performance of our proposed approach is as good as the state-of-the-art. However, none of these approaches is statistically significantly better than more naive estimators in all cases, which does not justify their additional complexity. We therefore encourage future work to develop alternative strategies for story points estimation. The experimental data and scripts we used in this work are publicly available to allow for replication and extension.