Reviewerly/exHarmony huggingface dataset:
Research on Water Hazards Detection Method Based on A-MSRCR and Improved YOLO

The environmental perception capability of ground unmanned vehicles in unstructured environments in the wild is the premise for their path planning in passable areas, and obstacle detection is an essential part of environmental perception technology. In a complex environment, water hazards in the driving section of ground unmanned vehicles will pose a significant threat to the driving safety of vehicles. For example, water ingress into the interior of an unmanned ground vehicle will cause serious faults such as short circuits and sensor failure. Therefore, the research on the detection technology of water hazards in the unstructured environment in the field is of great significance and value. In the unstructured environment in the wild, the texture features of water hazards are not apparent, the features are easily blocked by the shade of trees or other shadows, the proportion in the camera field of view is relatively small, and the recognition rate is low. This paper proposes a fast detection method for water hazards in unstructured environments in the wild based on image enhancement and improved anchor frame YOLOv3.
A Hybrid Global/Reactive Algorithm for Collision-Free Navigation of Mobile Ground Robots in Environments with Steady and Moving Obstacles

In this paper, we present an efficient hybrid navigation strategy for a low-computational non-holonomic mobile robot in an indoor environment with multiple static and moving objects. The Dynamic Programming is stacked with a reactive control algorithm to achieve the desired result. Dynamic Programming helps to navigate among known environments with known static obstacles. The reactive controller assists in avoiding unknown obstacles by using the onboard sensor's information. Computer simulation results validate the proposed strategy.
A GA-BP Neural Network Model for Students Examination Score Prediction

To improve the teaching effect of "Computer Application Basis" course, this paper selects the students’ behavior data in a Chinese university and uses convolution neural network as the basic model to predict students’ final grades. Considering the disadvantages of traditional B-P neural network model, such as over-reliance on empirical knowledge, unpredictability and difficulty in training, we propose an improved adaptive B-P neural network algorithm based on genetic algorithm. By encoding the B-P neural network, the algorithm takes the calculation error and structural complexity as fitness functions, which can ensure the population diversity of the genetic algorithm and improve the convergence speed, thus effectively avoiding the algorithm from falling into the local optimal solution. To verify the performance of the model in performance prediction, we use MSE, MAE and RMSE to evaluate the performance of the model comprehensively. Experiments show that the convolution neural network optimized by genetic algorithm has achieved good results in calculation accuracy and parameter setting, and is superior to the traditional convolution neural network model.
Intent Detection in Urdu Queries using Fine-tuned BERT models

User's intent detection provides essential cues in query understanding and accurate information retrieval through search engines and task-oriented dialogue systems. Intent detection from user queries is challenging due to short query length and lack of sufficient context. Further, limited prior research in query intent detection has been conducted for Urdu, an under-resourced language. With the recent success of Bidirectional Encoder Representation from Transformers (BERT), that provides pre-trained language models, we propose to develop intent detection model for Urdu by fine-tuning BERT variants for intent detection task. We conduct rigorous experimentation on mono and cross-lingual transfer learning approaches by using pre-trained BERT models i.e. mBERT, ArBERT, and roBERTa-urdu-small and two query datasets. Experimental evaluation reveal that the fine-tuned models of mBERT and roBERTa-urdu-small achieve 96.38% and 93.30% accuracy respectively on datasets I and II outperforming strong statistical and neural network baselines.
TrustGNN: Enhancing GNN via Multi-similarity Neighbors Identifying for Social Recommendation

Recently, with the development of Graph Neural Networks (GNN), many GNN-based methods have been developed for recommendation systems to aggregate both user-user and user-item interactions. However, most existing GNN-based models only consider direct relationship during the social aggregation process of user modeling, making the users selected as ‘neighbors’ can’t well reflect target user’s preferences and latent factors. To take more elements (such as the propagation of trust) into account during neighbors recognition and obtain a better user latent factor, we present a framework (TrustGNN), which employs DeepWalk method to help obtain the implicit similarity in one’s trustees, and combines it with the explicit similarity of a certain user’s historical ratings. We identify user’s neighbors according to the combined similarity, and then apply them in the social aggregation process. The method models two graphs and considers heterogeneous strengths. We test our proposed model on two real-life datasets, Filmtrust and CiaoDVD. The experiments demonstrate the effectiveness of our proposed model.
TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide Robot

A service robot serving safely and politely needs to track the surrounding people robustly, especially for Tour-Guide Robot (TGR). However, existing multi-object tracking (MOT) or multi-person tracking (MPT) methods are not applicable to TGR for the following reasons: 1. lacking relevant large-scale datasets; 2. lacking applicable metrics to evaluate trackers. In this work, we target the visual perceptual tasks for TGR and present the TGRDB dataset, a novel large-scale multi-person tracking dataset containing roughly 5.6 hours of annotated videos and over 450 long-term trajectories. Besides, we propose a more applicable metric to evaluate trackers using our dataset. As part of our work, we present TGRMPT, a novel MPT system that incorporates information from head shoulder and whole body, and achieves state-of-the-art performance. We have released our codes and dataset in https://github.com/wenwenzju/TGRMPT.
Application of improved generalized predictive control for frequency control in ultrasound therapy instruments

Aiming at the phenomenon that the output frequency and power of ultrasonic therapeutic instrument can not reach the expected result due to poor precision and resonant frequency drift after long-time use, an improved generalized predictive control algorithm was proposed to optimize the output. Based on the input and output of ultrasonic transducer, the control system model is built, and the parameters of the controlled object are identified online by the recursive augmented least square method with forgetting factor. The controller can predict the future variation trend according to the current output and compensate the current control quantity. Experimental results show that this method can compensate the output quickly, improve the control efficiency, and greatly improve the stability and anti-interference ability of the system. Compared with the traditional PID control method, the stabilization time is greatly shortened and the control accuracy is higher.
SOC Estimation of Lithium Batteries Based on Cluster-Growing Leaky Intergrator Echo State Network

This thesis provides a new estimation model called the Cluster-Growing Leaky Integrator Echo State Network (CGLESN) to improve the accuracy of traditional neural networks for Lithium batteries SOC estimation and speed up the convergence. CGLESN changes the traditional Leaky-ESN with fixed reservoir size generated with experience. CGLESN enabled the reservoir to grow clustered according to the task requirements and determined the model structure when mission deadlines are met. CGLESN improved the structure of completely random generation of reservoir in traditional echo state network, which can better adapt to various tasks. The collected voltage, current and temperature under DST conditions are used as the model input, and SOC is used as the output. CGLESN is used to train and test the collected data as the estimation model. The results show that the CGLESN has higher prediction accuracy compared to traditional network models, such as: BP networks and standard Leaky-ESN networks.
Markowitz Portfolio Model Based on LSTM

This paper establishes a model for daily gold and bitcoin trading needs. Firstly, based on LSTM recursive neural network model, the historical data set is divided into training set and test set. By training the data in the training set, using the neural network model to fit, and then using the test set to predict the past rate of return of each asset. Secondly, an improved Markowitz mean-variance model is established to determine the objective function of the return rate under portfolio and find its maximum value. The risk objective function is obtained by using income data and portfolio weights, and its minimum value is calculated. The PSO algorithm is used to solve the nonlinear programming problem of objective function and its constraints, and the daily weight portfolio and daily total return rate are obtained. Finally, the model is verified and its feasibility is proved.
Multi-Agent Path Finding Using Imitation-Reinforcement Learning with Transformer

Multi-Agent Path Finding is a problem of finding the optimal set of paths for multiple agents from the starting position to the goal without conflict, which is essential to large-scale robotic systems. Imitation and reinforcement learning are applied to solve the MAPF problem and have achieved certain results, which provides a feasible solution for the path planning problem of large-scale robot systems. The current method improves the performance of distributed strategy-guided agent planning paths in complex environments by introducing the communication between graph neural networks and agents but dramatically reduces the system's robustness. This paper develops a novel imitation reinforcement learning framework by introducing Transformer, which enables algorithms to perform well in complex environments without relying on communication between agents. Compared with its counterparts, experiments show that the policy trained by our method guides the agent to drive from the initial position to the goal without collision and achieve better performance.
Fine-tuning convolutional neural network based on relaxed Bayesian-optimized support vector machine for random-valued impulse noise removal

Denoising convolutional neural network (DnCNN) has achieved competitive denoising performance for Gaussian noise using residual learning. The same idea can also be applied in the impulse noise (IN) removal by changing the training data. However, as a discriminative learning-based algorithm, DnCNN lacks flexibility in removing spatially varying noise, such as random-valued impulse noise (RVIN). To address this problem, we propose a fine-tuning CNN based on relaxed Bayesian-optimized support vector machine (BO-SVM) for RVIN removal using a relaxed BO-SVM as a detector and a fine-tuning DnCNN as a denoiser. The relaxed BO-SVM is developed to classify all pixels of the test image into two categories: noise and noise-free. A relaxed rule is provided to preserve the information of slightly corrupted pixels, and the system is trained with an optimal feature set, which comprises of newly introduced local ternary pattern with previously used gray-level difference, average background difference, and median value difference. The key hyperparameters of the relaxed SVM are obtained using the Bayesian optimization technique. Then the DnCNN works for image reconstruction through fine-tuning experiments by the use of the corrupted pixels identified by the detector. The experimental results show that the proposed method reports promising results on RVIN removal over other state-of-the-art denoising methods.
Unsupervised Grammatical Correction With Optimized Layer Normalization and Dynamic Embedding Enhancement

Grammatical error correction aims to detect and correct grammatical errors with all types of mistaken, disordered, missing, and redundant characters. However, most existing methods focus more on detecting errors than correcting them. This paper proposes a domain-adaptive model with Interoperable Layer Normalization (ILN) and dynamic word embedding enhancement to optimize the error correction capability. To further improve the Chinese correction capability, we introduce multiple rounds of error correction to refine the sequence tagging model’s ability to fix mistakes. In addition, we propose a data augmentation method based on the complex tag to represent textual error correction traces more completely. We also explore a migration training method based on multiple training datasets. Further, we offer a unique unsupervised domain adaptation technique based on ILN, an innovative channel fusion approach that can significantly improve models’ domain adaptability. Finally, experimental results show that our proposed method substantially outperforms all robust baseline methods and achieves the best results in position-level and correction-level errors on the CGED-2020 dataset.
SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement Learning

Optimizing the cooling system plays a central role for capping the data center power consumption. However, the performance of traditional cooling management strategies is not satisfactory due to the complexity of thermodynamic process. Recently, several works leveraged Reinforcement Learning (RL) to improve the energy efficiency of data center cooling system. While they demonstrated that it is possible to reduce the cooling power consumption via RL, there are still some key challenges that have to be addressed before field deployment, such as safe operation guarantee and sample complexity, etc. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> , an actor-critic Model-Based Reinforcement Learning (MBRL) algorithm for data center cooling management. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> incorporates two system models, i.e., a transition model to predict the future system state and a risk model to estimate the negative effect of executing an action. The safety of proposed algorithm is ensured by Model Predictive Control (MPC) and risk-guided exploration. In addition, by employing the MBRL framework, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> achieves higher sample efficiency and accelerated convergence. Simulations using real-world workload trace reveal that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> saves up to 13.18% cooling power compared with state-of-the-art MBRL data center cooling solutions.
A Blockchain System for TDMA-Based Tactical Wireless Networks With Constrained Resources

This paper proposes a blockchain system with a consensus algorithm and timeslot allocation policy for TDMA-based tactical wireless networks with constrained resources. A blockchain can serve as a distributed database that ensures all nodes can store the same data even in unreliable network environments. Blockchain technology has numerous benefits, such as decentralization, tamper resistance, traceability, and audibility. Due to these characteristics, the application range of blockchains has dramatically expanded from cryptocurrency, financial services, risk management, and IoT to various services in both public and non-public domains. In addition, there has been an increasing demand for using blockchain technology in wireless network environments. Unlike wired networks, communication resources in wireless networks for data transmission and reception are relatively limited; as such, it is necessary to design a consensus algorithm with low resource consumption and an efficient resource allocation policy for these environments. The proposed system allocates timeslots to nodes based on their contribution to blockchain management, which prevents nodes from participating in an unnecessary competition for block generation, thereby preventing the associated waste of energy. We could verify the proposed system’s practicality through numerical analysis and computer simulations under various conditions.
GAGCN: Generative adversarial graph convolutional network for non‐homogeneous texture extension synthesis

Abstract In the non‐homogeneous texture synthesis task, the overall visual characteristics should be consistent when extending the local patterns of the exemplar. The existing methods mainly focus on the local visual features of patterns but ignore the relative position features that are important for non‐homogeneous texture synthesis. Although these methods have achieved success on homogeneous textures, they cannot perform well on non‐homogeneous textures. Thus, it is desirable to model the dependence between pixels to improve the synthesis performance. To ensure synthesis results from both the local detail structure and the overall structure, this paper proposes a non‐homogeneous texture extended synthesis model (GAGCN) combining the generate adversarial network (GAN) and the graph convolutional network (GCN). The GAN learns the internal distribution of image patches, which makes the synthetic image have rich local details. The GCN learns the latent dependence between pixels according to the statistical characteristics of the image. Based on this, a novel graph similarity loss is proposed. This loss describes the latent spatial differences between the sample image and the generated image, which helps the model to better capture global features. Experiments show that our method outperforms existing methods on non‐homogeneous textures.
Implementation of encoder and decoder for low-density parity-check codes in continuous-variable quantum key distribution on a field programmable gate array

Quantum key distribution (QKD) can provide an unconditionally secure communication encryption method. Continuous-variable QKD (CVQKD) requires extracting the key from the noisy channel, leading to higher requirements for the error-correcting codes used in multidimensional reconciliation. We extend the quasi-cyclic low-density parity-check of the radio design for the global 5G mobile communication technology standard to achieve error correction under lower signal-to-noise ratio on integrated hardware. A field programmable gate array (FPGA) chip is used to realize the approximate lower triangular matrix encoding algorithm, and the core module only has the circular shift register, which reduces the encoding complexity, improves the encoding efficiency, and reduces the hardware resource consumption. For extended super-long code words, the decoding algorithm requires additional hardware resources to implement, which the previous algorithm cannot meet. Therefore, we present a hardware implementation of offset minimum sum algorithm (OMSA), which can avoid operations of complex mathematical formula. The offset factor of OMSA reduce the error caused by the low accuracy of calculation on the hardware. The update of the decoding information is layered in a partially parallel way in the matrix to achieve the cross processing of node information. It speeds up the transmission of information between different nodes and the convergence of decoding algorithm of low-complexity parallel structure. Simulation results show that the maximum theoretical coordination efficiency is 85% when the code with block length is 69632 at a bit rate of 22/68 and the signal-to-noise ratio asymptotic threshold is 0.7. Using an FPGA, ∼0.1 frame error rate was achieved, but with a zero-bit error rate, the optimal achievable secret key rate is about 200 Kbps at 30-km distance theoretically. For a 200-MHz system clock on the successfully decoded block, 125 Gbps encoding throughput and 472 Kbps decoding throughput was achieved, better than the theoretical key rate of prototype of CVQKD.
EWST: an extreme weather scene text detector with dehazing and localization refinement

In real life, the emergence of haze brings great inconvenience and does harm to traffic and pedestrian safety, whereas previous studies paid less attention to text detection in haze scenes. In this experiment, we found that the candidates obtained by the general non-maximum suppression (NMS) method or the soft-NMS method are difficult to precisely match the ground truth, and the incomplete feature extraction will affect the final performance. In this work, a haze scene text detection framework is skillfully designed. An optimized NMS and an optimized long short-term memory for spatial feature extraction and temporal feature extraction are proposed to improve the text detection performance. In addition, a hazing scene text dataset (named HSText-1000) and a hybrid scenario text dataset (named MHSText-4600) have been built in our work for evaluating the performance conveniently, which have been released and can be downloaded from https://github.com/lyy0117/lyy. Experimental results illustrate that our method is superior to some state-of-the-art methods in the hazing scene and the hybrid scene. Meanwhile, we achieved competitive results in nonhaze’s public dataset (ICDAR 2013), which means that our method has satisfactory adaptability. We will release code to facilitate community research.
Computing a Feedback Arc Set Using PageRank

We present a new heuristic algorithm for computing a minimum Feedback Arc Set in directed graphs. The new technique produces solutions that are better than the ones produced by the best previously known heuristics, often reducing the FAS size by more than 50%. It is based on computing the PageRank score of the nodes of the directed line graph of the input directed graph. Although the time required by our heuristic is heavily influenced by the size of the produced line graph, our experimental results show that it runs very fast even for very large graphs used in graph drawing.
An Approach to the Semantic Representation of the Local Government Strategic Planning Process: Ontology-Driven Simulation Method for Assessing Economic Impacts

This paper describes a methodological approach to the creation of a strategic planning ontology (SPO) for local governments. The aim of the given ontology is to provide a semantic description of the development strategy, involved stakeholders, and tools to improve the decision-making process for selecting the most valuable strategic objectives for the economy of the region. The novelty of the research presented in this paper is reflected by the development of an ontological model of actions and reasons, that is, semantically described combinations of activities and requirements that are defined by development strategies. The proposed ontology can provide answers to questions such as: what are the development priorities? How will those priorities be implemented? Who will be responsible for implementation? Why are they being implemented in the first place? We answer the question, “why specific development goals are chosen?”. Answers are given as semantic representations of the economic impact on a region’s industry. The ontology-driven simulation methods that are proposed in this paper provide the possibility of simulating the effects and results of the chosen development strategy goals, evaluate the actions taken, and ensure support for the selection of the best alternative in the process of defining objectives for the development strategy.