Reviewerly/exHarmony huggingface dataset:
Research on UAV Path Planning Combined with Ant Colony and A*

UAV route planning is a complex problem of global optimum. Ant colony algorithm has robustness, easy to be combined with other optimization methods. To overcome the shortcomings of traditional ACO algorithm in UAV path planning, the convergence time of the algorithm is long, easy to be caught in local optimal, and the number of ants is large, so the algorithm cannot smoothly approach the target point, A*-ACO fusion algorithm was proposed by analyzing ACO improvement strategies. The planning environment was constructed in the form of grid graph, and the ACO algorithm generated several reference optimization routes, which were converted into the initial pheromone distribution characteristics, which alleviated the blindness of early ant colony search, avoided result in a locally optimal route, and accelerated the convergence speed of the algorithm. Simulation results indicate that the application of improved flight route can significantly increase the UAV flight security.
Path Planning for Maritime Drones in a Free Space Environment Based on Reinforcement Learning

In modern maritime reconnaissance tasks, realizing the adaptive path planning for the maritime drones has always been a challenging problem to overcome. Existing conventional methods for path planning focus on the mesh routing environment. However, the performance in a continuous free space environment may be unsatisfactory. Therefore, this paper proposes an improved Q learning method for the path planning of maritime drones on the surface of the ocean. This work discretes the flight environment of the maritime drones into a state space represented by several state variables, which greatly reduce the state dimension. During the training process, a state-aware guided learning strategy is proposed to accelerate the convergence. Numerical simulation experiments are performed to verify the generality, effectiveness and accuracy of the proposed algorithm with applications to different free space environments. The proposed learning algorithm is obviously superior compared with the traditional Q learning.
Autonomous Obstacle Avoidance Algorithm for UAVs Based on Obstacle Contour Detection

As a research hotspot in the UAV field, autonomous obstacle avoidance of UAVs in the unknown environment plays an important role in geographical reconnaissance, map drawing, field rescue, and other aspects. This paper designs an autonomous obstacle avoidance algorithm for UAVs based on obstacle contour detection. By analyzing the image features of unknown obstacles, the algorithm outputs the location coordinates and contour of obstacles in real-time. In this process, by reducing the time complexity of smooth filtering, the computational efficiency is improved. Meanwhile, converting the image to HSV color space effectively avoids the influence of light intensity, and the Otus threshold segmentation method is introduced to improve the real-time performance of the algorithm. The method is combined with the D* path search algorithm to realize autonomous obstacle avoidance of UAVs in an unknown environment. The algorithm is simulated in the Airsim environment, and compared with the existing obstacle avoidance methods, the effectiveness of the algorithm is verified.
Ship Trajectory Anomaly Detection Based on TCN Model

Ship trajectory data contains a lot of valuable information, but abnormal data is inevitably generated in the process of data acquisition. A Temporal Convolutional Network (TCN) model-based method for detecting ship trajectory anomalies is proposed in this paper to avoid adverse effects on subsequent data analysis. First, a sliding time window algorithm is designed to extract the trajectory-subsequence because ship trajectories are time-series data. Then input the normal trajectory to train the TCN model. The model will learn the kinematic characteristics of the normal trajectory. When inputting an abnormal trajectory, the model reconstructs the anomalous trajectory and detects the anomaly by the reconstruction error. In addition, the multi-dimensional features of the trajectory are analyzed, and the identification of anomalies is based on the reconstruction error of each feature. With a recall of 93.51 and an F1 score of 0.913, the algorithm has significantly improved its performance in comparison with the LSTM model.
Parameter Dynamic Selection Method of Multi-UAV Cooperative Search Based on Expert System

The cooperative search strategy is designed with the distributed model predictive method in this paper. For the rationality of decision-making of UAV swarm in different stages of the cooperative search, a dynamic parameter selection method based on the expert system is proposed. The weight of the objective function is adjusted online through the expert system so that the result of the search decision is more in line with the task requirements. Besides, a jump grid method is proposed for grid selection in cooperative search so that the UAV can select non-adjacent grids as the search point for the next time. The UAV's search frequency and trajectory maneuverability can be changed by adjusting the number of jump grids through the expert system. The simulation shows that the UAV with the expert system can dynamically adjust the search interval and search requirements, making the UAV swarm able to quickly discover targets, explore unknown areas, ensure collision avoidance, and meet the boundary constraints.
A Deep Reinforcement Learning Approach for Quadrotor Path Planning with Search-Based Planner Optimization

This paper proposes an end-to-end quadrotor path planning framework derived from deep reinforcement learning (DRL), which is sensor-based with Deep Deterministic Policy Gradient (DDPG) algorithm and search-based planner optimization. We use the current excellent search-based algorithm to demonstrate the path planning of the quadrotor to reach the goal and prioritize it into an experience buffer for replay. Semi-physical simulation experiments show that our framework can make the learning process converge quickly and realize the efficient use of information for exploration in the unknown environment, effectively improving the ability compare with the original DDPG algorithm in complex tasks.
Robust Underwater Direction-of-Arrival Tracking with Uncertain Measurement Noise

A robust underwater direction-of-arrival (DOA) tracking technique is presented by the paper. Firstly, considering the port and starboard ambiguity problem, the measurement model is founded as a non-linear sound pressure model based on a uniform circular array. Then, considering the unknown underwater environment can sometimes cause uncertain measurement noise, a robust DOA tracking technique based on variational Bayesian (VB) and extended Kalman filter (EKF) method is proposed. By utilizing proposed VB-EKF to estimate uncertain measurement noise online, DOA tracking technique can achieve a reliable tracking accuracy. Finally, the proposed VB-EKF is tested by simulations to verify its robustness and tracking performance.
Selecting reliable blockchain peers via hybrid blockchain reliability prediction

Abstract Blockchain and blockchain‐based decentralised applications have been attracting increasing attention recently. In public blockchain systems, users usually connect to third‐party peers or run a peer to join the P2P blockchain network. However, connecting to unreliable blockchain peers will lead to resource waste and even loss of cryptocurrencies by repeated transactions. In order to select reliable blockchain peers, it is urgently needed to evaluate and predict their reliability of them. Faced with this problem, we propose hybrid blockchain reliability prediction (H‐BRP), a Hybrid Blockchain Reliability Prediction model, to extract the blockchain reliability factors and then make the personalised prediction for each user. Comprehensive experiments conducted on 100 blockchain requesters and 200 blockchain peers demonstrate the effectiveness of the proposed H‐BRP model. Further, the implementation and dataset of 2,000,000 test cases are released.
Nonce‐misuse resilience of Romulus‐N and GIFT‐COFB

Abstract Nonce‐misuse resilience (NMRL) security of Romulus‐N and GIFT‐COFB is analysed, the two finalists of NIST Lightweight Cryptography project for standardising lightweight authenticated encryption. NMRL, introduced by Ashur et al. at CRYPTO 2017, is a relaxed security notion from a stronger, nonce‐misuse resistance notion. The authors have proved that Romulus‐N and GIFT‐ COFB have nonce‐misuse resilience. For Romulus‐N, the perfect privacy (NMRL‐PRIV) and n /2‐bit authenticity (NMRL‐AUTH) with graceful degradation with respect to nonce repetition are showed. For GIFT‐COFB, n /4‐bit security for both NMRL‐PRIV and NMRL‐AUTH notions is showed.
Towards understanding quality challenges of the federated learning for neural networks: a first look from the lens of robustness

Federated learning (FL) is a distributed learning paradigm that preserves users' data privacy while leveraging the entire dataset of all participants. In FL, multiple models are trained independently on the clients and aggregated centrally to update a global model in an iterative process. Although this approach is excellent at preserving privacy, FL still suffers from quality issues such as attacks or byzantine faults. Recent attempts have been made to address such quality challenges on the robust aggregation techniques for FL. However, the effectiveness of state-of-the-art (SOTA) robust FL techniques is still unclear and lacks a comprehensive study. Therefore, to better understand the current quality status and challenges of these SOTA FL techniques in the presence of attacks and faults, we perform a large-scale empirical study to investigate the SOTA FL's quality from multiple angles of attacks, simulated faults (via mutation operators), and aggregation (defense) methods. In particular, we study FL's performance on the image classification tasks and use Deep Neural Networks as our model type. Furthermore, we perform our study on two generic image datasets and one real-world federated medical image dataset. We also systematically investigate the effect of the proportion of affected clients and the dataset distribution factors on the robustness of FL. After a large-scale analysis with 496 configurations, we find that most mutators on each user have a negligible effect on the final model in the generic datasets, and only one of them is effective in the medical dataset. Furthermore, we show that model poisoning attacks are more effective than data poisoning attacks. Moreover, choosing the most robust FL aggregator depends on the attacks and datasets. Finally, we illustrate that a simple ensemble of aggregators achieves a more robust solution than any single aggregator and is the best choice in 75% of the cases. The data that support the findings of this study are available in our repository at https://github.com/aminesi/federated .
On the duality between homological quantum codes of a hypermap and its dual hypermap

From a given topological hypermap $$H$$ , we define two related hypermaps $$H^\triangle $$ and $$H^\nabla $$ as complements of the ordinary dual hypermap $$H^*$$ along with the concepts of their edge hypermap quantum codes $${\mathcal {C}}^\triangle $$ and $${\mathcal {C}}^\nabla $$ . We then show that, when the sets of special darts are naturally related, the duality between the two ordinary hypermap quantum codes, i.e., $${\mathcal {C}}$$ from $$H$$ and $${\mathcal {C}}^*$$ from $$H^*$$ can be simplified to the duality between $${\mathcal {C}}^\triangle $$ and $${\mathcal {C}}^\nabla $$ .
Blockchain: The Foundation of Trust in Metaverse

Metaverse is a computer-generated virtual environment which is a combination of real and digital world facilitated by web technologies, Virtual reality and Internet. Despite considerable benefits and attention, a logical question in how to protect its consumers’ digital content and data in the metaverse. In this regard, one of the potential solutions could be blockchain due to its unique features like decentralization, immutability, and transparency. This chapter intends to provide a preliminary information of metaverse and blockchain and how these two technologies can be related to each other. The impact of blockchain technologies on metaverse is also discussed in brief.
Understanding the Blockchain Technology Adoption in Transportation Management: Application in Trucking Industry

Over the past year the transportation industry realizes the changes, expectation and challenges with the growing digitally-driven buy-and-sell ecosystem. Therefore, the transporter plan for the best possible way to provide service to their clients by not only delivering their products/goods safely but also ensure the security of their data and information involved in the transaction. Hence, in order to scale the modern transport business, managers need to ensure a secure platform for recording and maintaining the confidential data in their business operation. Blockchain technology (BT) can help in redesigning of transportation operations and supports in transforming towards more efficient business environment. The BT enables the effective information management and data security of business operations, which can also help in building the transparent, efficient, reducing physical paperwork, and trustworthy business environment. Therefore, this chapter presented recent development in transportation industry considering the blockchain technology. We have further discussed the scope of blockchain technology adoption to enhance the security, trust and operational performance of the transportation business. In addition to this, potential benefits of blockchain technologies in payment, immutability, flexibility and security has been highlighted. We have presented the role of BT considering the Indian trucking industry. This chapter helps in strategic planning and decision making to the transport managers in effective implementation of the BT. At the end of this chapter the future scope of BT adoption in trucking industry have been discussed.
Pedagogical Conditions for Preparing Employees of the Ministry of Internal Affairs of Russia to Act in Special Conditions

The authors consider in detail such aspects of the topic as the pedagogical conditions for training employees of the Ministry of Internal Affairs of Russia to act in special conditions in modern conditions. The article deals with the essence of creating special pedagogical conditions, the issues of teaching academic disciplines in departmental educational organizations to tactically-oriented training of employees of the Ministry of Internal Affairs of Russia to perform service and combat operations, in conditions of various levels of danger that pose a threat to the state, the population, infrastructure and life support facilities. This paper presents recommendations on the conduct of classes, the use of training programs, various forms and methods of conducting classes. The pedagogical conditions for the formation of professional competencies of employees of the Ministry of Internal Affairs of Russia are described: motivation and readiness for professional activity, formation of skills of self-development, regulation, professional special, moral and pedagogical training. The main conclusions of the study are that employees of the internal affairs bodies of the Russian Federation provide protection to citizens and the state. An important point is the formation of their professional motivation skills for further training and work. The basis of the qualities of the employees of the Ministry of Internal Affairs of Russia are the moral characteristics of professional stability. They are formed throughout the life of a law enforcement officer, but it is training that lays the foundation for the characteristics with which he will enter the path of his activity. Pedagogical conditions at the time of training are the main factors of socialization of the future defender of the state, legitimate rights and interests of society.
News Recommendation with Multi-views Emotion Analysis

News recommendation aiming to find attractive news for users has been received many attentions in recent years. Existing news recommendation methods mainly focus on modeling user preference based on the interaction behaviors between users and news without the consideration of emotion information in the interaction. However, emotion information also plays an important role in improving the accuracy of news recommendation. In this paper, we propose an emotion analysis method for news recommendation with using multi-views to explore the impact of emotion information during the process of user's decision making. The emotion features extracted by the method are combined with the content features of the news to provide a comprehensive feature representation of the candidate news to improve the performance of recommendation. Experiments on real-world datasets show the effectiveness of the proposed method in improving accuracy of news recommendation.
Research on Radar Signal Recognition Technology Based on Residual Convolutional Neural Network

The research on recognition of radar intra-pulse modulated signals is an important development direction of radar countermeasure technology. In order to recognize the radar intra-pulse modulated signals effectively, many sgnal recognition techniques have been developed. In which, the one that based on residual convolutional neural network is one of the most promising techniques. In this paper, radar signal recognition techniques based on residual convolutional neural network are researched. The influence of model depth and residual block on time-frequency image recognition is verified. Firstly, using the feature extraction and recognition method of radar signal time-frequency image from Choi Williams, the problem of signal recognition is transformed into image recognition. Time-frequency images of 8 kinds of common radar signals are converted to grayscale images by the Choi Williams time-frequency transformation. Secondly, these grayscale images are recognized with six kinds of signal recognition algorithm models (AlexNet, DarkNet-19, GoogLeNet, VGG-16, ResNet-18, MobileNet-v2) and the recognition effect is compared. Thirdly, the residual block of the above six models are modified by increasing or decreasing residual and Inverted residual block, and the experimental results are compared. The results show that the shallow model AlexNet has the best accuracy and speed in recognizing time-frequency images, and the shallow network with an inverted residual block will improve the recognition speed with the cost of reducing the recognition accuracy slightly.
Few-shot Learning with Attention Mechanism and Transfer Learning for Import and Export Commodities Classification

As deep learning theory develops rapidly, the convolutional neural network model has been widely used in many fields with its powerful characterization ability and outstanding classification performance. Therefore, the number of parameters in deep convolutional neural network models is usually very large, and massive labeled data is often required for model training. In some scenarios, it is difficult or even impossible to collect enough labeled data. Instead, few-shot learning can obtain considerable learning performance with a small sample size. Thus, we study a few-shot learning model with feature enhancement and transfer learning on a small dataset of import and export commodities. We choose ResNetl 8 as the backbone and use data augmentation to expand the original small dataset before training, which somewhat alleviates the overfitting problem of the convolutional neural network model. Moreover, we introduce the attention module and transfer learning into the backbone. The experimental results on the dataset clearly verify the effectiveness of above methods.
Fostering Learning Motivation of Students with Reading and Spelling Difficulties by an AR-Enhanced Gamified Educational App for Literacy Learning

Students with special educational needs benefit from carefully designed learning approaches that consider both their individual learning requirements and the advances in teaching and learning methods and tools. This paper presents the initial exploratory results from a pilot study on the advancement of literacy skills through an innovative Augmented Reality (AR)-enhanced gamified educational app for students diagnosed with reading / spelling difficulties or dyslexia. A sample of 5 teachers and 23 students worked with the AR app for the duration of a school term and filled in standardized scales on student motivation upon completion of the pilot study. The analysis of the results indicates that both groups of teachers and students found the AR app motivating to a certain degree. However, there were challenges within the pilot implementation that impeded the successful app use and potentially delimited the perceived motivational effects. Conclusions and outlooks of research perspectives for further development highlight the necessity for further research in this important field.
3D Hexglyph Maps: An Immersive Analytics Technique Combining Hexbin Maps with Space-Time Cubes for Visualizing eSports Data

Displaying multivariate tempo-spatial data such as from competitive strategy games can support people in the challenging task of analyzing them. Within the Immersive An-alytics discipline, technologies such as Virtual Reality (VR) and Augmented Reality are utilized for rendering such complex data and extending the tool set of analysts. In this paper, we introduce 3D Hexglyph Maps - a visualization technique drawing from both the Space-Time Cube and the Hexbin Map techniques for use in VR. We illustrate how we applied our glyph-based technique for analyzing trajectorial match data of the prominent Electronic Sports game League of Legends and discuss identified potential and limitations. The results of our user study show that our technique performed well concerning the completion rates, that our participants needed a short period before efficiently understanding the visualization by means of the completion times, and that an increase of the players whose data are visualized leads to a slight deceleration of the analysis process.
Balancing the Performance of a FightingICE Agent using Reinforcement Learning and Skilled Experience Catalogue

Dynamic Difficulty Adjustment (DDA) is the process of changing the challenge offered dynamically based on the player's performance, as opposed to the player manually choosing the difficulty from a set of options. This helps in alleviating player frustration by having the opponents' skill match that of the player's. In this work, we propose a novel application of a DDA technique called Skilled Experience Catalogue (SEC) which has previously been used with success in First Person Shooter games. This approach uses experiential milestones of the learning process of an agent trained using Reinforcement Learning (RL). We have designed and implemented a custom SEC on top of the FightingICE platform that is used in the Fighting Game Artificial Intelligence (FTGAI) competition. We deployed our SEC agent against three fixed-strategy opponents and showed that we could successfully balance the game-play in two out of the three opponents over 150 games against each. Balancing was not achieved against the third opponent since the RL agent could not reach the required skill level after its initial training.