Reviewerly/exHarmony huggingface dataset:
A text classification method based on a convolutional and bidirectional long short-term memory model

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Field Evaluation of Path-Planning Algorithms for Autonomous Mobile Robot in Smart Farms

Path planning is crucial for several applications, including those in industrial facilities, network traffic, computer games, and agriculture. Enabling automated path-planning methods in smart farms is essential to the future development of agricultural technology. Path planning is divided into global and local planners. Global planners are divided into different types and use well-known grid-based and sampling-based algorithms. In this paper, we propose an algorithm suitable for smart farms in combination with simultaneous localization and mapping (SLAM) technology. The characteristics of the grid-based Dijkstra algorithm, the grid-based A* algorithm, the sampling-based rapidly exploring random tree (RRT) algorithm, and the sampling-based RRT* algorithm are discussed, and an algorithm suitable for smart farms is investigated through field tests. We hypothesized path planning for an agricultural harvesting robot, a spraying robot, and an agricultural transport robot, and conducted experiments in environments with static and dynamic obstacles. In addition, the set parameters are validated experimentally. The Shapiro&#x2013;Wilk test is used to confirm the shape of the normal distribution, and the analysis of variance (ANOVA) and Kruskal&#x2013;Wallis test are performed to confirm the significance of the experimental results. Smart farms aim to minimize crop damage; thus, it is vital to reach the goal point accurately rather than quickly. Based on the results, we determined that the A* algorithm is suitable for smart farms. The results also open the possibility of reaching the correct destination in the shortest time when working in smart farms.
An Improved Lightweight RetinaNet for Ship Detection in SAR Images

The rapid development of remote sensing technology has led to a sharp increase in the amount of Synthetic Aperture Radar (SAR) measurements, which put forward higher requirements for remote sensing image processing. As an important application of SAR, fast and accurate ship detection has always been a research hotspot. In this paper, an improved lightweight RetinaNet for ship detection in SAR images is proposed. Compared with the standard RetinaNet, the shallow convolutional layers of the backbone are replaced by ghost modules and the number of the deep convolutional layers is reduced. The spatial and channel attention modules are embedded into the model to enhance detectability. K-Means clustering algorithm is applied to adjust the initial aspect ratios of the model. The effectiveness and robustness of the proposed method is demonstrated by numerical experiments with SSDD dataset, Gaofen-3 mini dataset, and a large-scale SAR image of Hisea-1 satellite, it is shown that the proposed method can significantly reduce the FLOPs and the number of parameters without decreasing the detection accuracy and recall ratio. Moreover, the experimental results also show the proposed model&#x0027;s robustness and the ability to detect ship targets in small datasets.
TKAGFL: A Federated Communication Framework Under Data Heterogeneity

Federated learning still faces many problems from research to technology implementation and the most critical problem is that the communication efficiency is not high. Therefore, the main task of this work is to improve the communication efficiency, affected by communication devices, bandwidth, data distribution and so on. Our work focus on two main aspects of federated learning – updates strategy and data heterogeneity, and propose a new federated framework called TKAGFL, which is composed by three key factors. Firstly, in response to the problem of data heterogeneity that often occurs in actual federated learning, federated generative adversarial networks are proposed as a method of data preprocessing. Secondly, the improved homomorphic encryption method counterbalances the conflicts between data-sharing and privacy protection. Thirdly, in the communication process, classic AdaGrad optimization in deep learning and top-K algorithms are combined to compress communication parameters to improve communication efficiency. The results of experiments demonstrate our TKAGFL framework’s accuracy is about 15% - 20% better than other algorithms or frameworks and it can converge at 150 communication rounds and is 50 rounds faster than others. Also, our TKAGFL algorithm reduces 10 times communication volume than other algorithms,which is beneficial for federated learning application in industry.
A Semi-Centralized Trust Management Model Based on Blockchain for Data Exchange in IoT System

IoT data exchange plays a vital role in supporting various applications and services with massive IoT devices. However, the existence of malicious devices threatens the integrity and reliability of the exchanged data. Trust management has been used to mitigate the impact of malicious devices in centralized and decentralized architectures. However, most of these traditional trust management systems bear computation, storage, and communication challenges. In this study, we propose a semi-centralized trust management system architecture based on blockchain in both single and multiple domains. The IoT devices are centralized organized by cloud servers who coordinately sustain a rating data ledger within each domain based the proposed rotation based consensus protocol in a decentralized manner to support cross-domain data exchange. A computational trust model is proposed by aggregating the direct and indirect trust information, where we elaborately design decay function, recommendation credibility and adaptable weights so as to calculate the trust value of dynamic malicious devices. Finally, we evaluate the proposed system model in various situations through simulation based experiments and compare it with two classical models in the literature. The experimental results demonstrate the effectiveness of the proposed trust model in identifying malicious devices and mitigating the influence of malicious devices.
A Blockchain Based System for Healthcare Digital Twin

Digital Twin (DT) is an emerging technology that replicates any physical phenomenon from a physical space to a digital space in congruence with the physical state.However, devising a Healthcare DT model for patient care is seen as a challenging task as the lack of adequate data collection structure.There are also security and privacy concerns as healthcare data is very sensitive and can be used in malicious ways.Because of these current research gaps, the proper way of acquiring the structured data and managing them in a secure way is very important.In this article, we present a mathematical data model to accumulate the patient relevant data in a structured and predefined way with proper delineation.Additionally, the provided data model is described in harmony with real life contexts.Then, we have used the patient centric mathematical data model to formally define the semantic and scope of our proposed Healthcare Digital Twin (HDT ) system based on Blockchain.Accordingly, the proposed system is described with all the key components as well as with detailed protocol flows and an analysis of its different aspects.Finally, the feasibility of the proposed model with a critical comparison with other relevant research works have been provided.
Cooperative Computation Offloading in Blockchain-Based Vehicular Edge Computing Networks

As a novel computing paradigm, multiaccess edge computing (MEC) migrates computing and storage capabilities to edge nodes of the network to meet the requirements of executing computationally intensive or delay-sensitive tasks on intelligent vehicles. In addition, MEC fills the gap between cloud computing and terminals in vehicular networks. In the MEC system, to reduce the load on MEC servers with large-scale vehicle deployment and promote the efficient use of network resources, vehicles can also transfer tasks to neighboring resource-rich vehicles using cooperative computation offloading. However, cooperative computation offloading between vehicles faces the challenges of security and insufficient information about the server vehicle. Therefore, this paper proposes using blockchain technology to achieve efficient data sharing between vehicles and service providers (i.e., server vehicles) and ensure the security of computation offloading between vehicles. First, we design a secure data sharing architecture in blockchain-based vehicular edge computing networks. Then, a new consensus mechanism in this architecture is proposed to improve the efficiency of data sharing and prevent malicious attacks. Furthermore, we present a cooperative offloading decision-making method using an offloading game, and the Nash equilibrium of the offloading strategy is achieved using this method. The results of numerical experiments demonstrate the superior performance of the proposed method.
UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization

In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.
SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution

Easy and efficient acquisition of high-resolution remote sensing images is of importance in geographic information systems. Previously, deep neural networks composed of convolutional layers have achieved impressive progress in super-resolution reconstruction. However, the inherent problems of the convolutional layer, including the difficulty of modeling the long-range dependency, limit the performance of these networks on super-resolution reconstruction. To address the above problems, we propose a generative adversarial network (GAN) by combining the advantages of the swin transformer and convolutional layers, called SWCGAN. It is different from the previous super-resolution models, which are composed of pure convolutional blocks. The essential idea behind the proposed method is to generate high-resolution images by a generator network with a hybrid of convolutional and swin transformer layers and then to use a pure swin transformer discriminator network for adversarial training. In the proposed method, (1) we employ a convolutional layer for shallow feature extraction that can be adapted to flexible input sizes; (2) we further propose the residual dense swin transformer block (RDSTB) to extract deep features for upsampling to generate high-resolution images; and (3) we use a simplified swin transformer as the discriminator for adversarial training. To evaluate the performance of the proposed method, we compare the proposed method with other state-of-the-art methods by utilizing the UCMerced benchmark dataset, and we apply the proposed method to real-world remote sensing images. The results demonstrate that the reconstruction performance of the proposed method outperforms other state-of-the-art methods in most metrics
Performance Optimization for Semantic Communications: An Attention-Based Reinforcement Learning Approach

In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textual data, and transmits it to each user. The semantic information is modeled by a knowledge graph (KG) that consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the considered semantic communication framework, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS may not be able to transmit the entire semantic information to each user and satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users. As such, we formulate an optimization problem whose goal is to maximize the total MSS by jointly optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a proximal-policy-optimization-based reinforcement learning (RL) algorithm integrated with an attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Compared to traditional RL algorithms, the proposed algorithm can dynamically adjust its learning rate thus ensuring convergence to a locally optimal solution. Simulation results show that the proposed framework can reduce by 41.3% data that the BS needs to transmit and improve by two-fold the total MSS compared to a standard communication network without using semantic communication techniques.
Augmented reality in architecture and construction education: state of the field and opportunities

Abstract Over the past decade, the architecture and construction (AC) industries have been evolving from traditional practices into more current, interdisciplinary and technology integrated methods. Complex and intricate digital technologies and mobile computing such as simulation, computational design and immersive technologies, have been exploited for different purposes such as reducing cost and time, improving design and enhancing overall project efficiency. Immersive technologies and augmented reality (AR), in particular, have proven to be extremely beneficial in this field. However, the application and usage of these technologies and devices in higher education teaching and learning environments are yet to be fully explored and still scarce. More importantly, there is still a significant gap in developing pedagogies and teaching methods that embrace the usage of such technologies in the AC curricula. This study, therefore, aims to critically analyse the current state-of-the-art and present the developed and improved AR approaches in teaching and learning methods of AC, addressing the identified gap in the extant literature, while developing transformational frameworks to link the gaps to their future research agenda. The conducted analysis incorporates the critical role of the AR implications on the AC students’ skillsets, pedagogical philosophies in AC curricula, techno-educational aspects and content domains in the design and implementation of AR environments for AC learning. The outcomes of this comprehensive study prepare trainers, instructors, and the future generation of AC workers for the rapid advancements in this industry.
A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends

Currently available reviews in the area of artificial intelligence-based music generation do not provide a wide range of publications and are usually centered around comparing very specific topics between a very limited range of solutions. Best surveys available in the field are bibliography sections of some papers and books which lack a systematic approach and limit their scope to only handpicked examples In this work, we analyze the scope and trends of the research on artificial intelligence-based music generation by performing a systematic review of the available publications in the field using the Prisma methodology. Furthermore, we discuss the possible implementations and accessibility of a set of currently available AI solutions, as aids to musical composition. Our research shows how publications are being distributed globally according to many characteristics, which provides a clear picture of the situation of this technology. Through our research it becomes clear that the interest of both musicians and computer scientists in AI-based automatic music generation has increased significantly in the last few years with an increasing participation of mayor companies in the field whose works we analyze. We discuss several generation architectures, both from a technical and a musical point of view and we highlight various areas were further research is needed. • Scoping review of Artificial Intelligent based automatic music generation systems. • 139 works included from over 300 reviewed and 1512 originally identified. • Analyzes both musical aspects and system architectures exploring their advantages. • Provides a clear picture of where the focus of the research is headed.
CSKG4APT: A Cybersecurity Knowledge Graph for Advanced Persistent Threat Organization Attribution

Open-source cyber threat intelligence (OSCTI) is becoming more influential in obtaining current network security information. Most studies on cyber threat intelligence (CTI) focus on automating the extraction of threat entities from public sources that describe attack events. The cybersecurity knowledge graph aims to change the expression of threat knowledge so that security researchers can accurately and efficiently obtain various types of threat information for preliminary intelligent decisions. The attribution technology can not only assist security analysts in detecting advanced persistent threats, but can also identify the same threat from different attack events. Therefore, it is important to trace the attack threat actor. In this study, we used the knowledge graph technology, considered the latest research on cyber threat attack attribution, and thoroughly examined key related technologies and theories in the process of constructing and applying the advanced persistent threat (APT) knowledge graph from OSCTI. We designed a cybersecurity platform named CSKG4APT based on a knowledge graph. Inspired by the theory of ontology, we constructed CSKG4APT as an APT knowledge graph model based on real APT attack scenarios. We then designed an APT threat knowledge extraction algorithm for completing and updating the knowledge graph using deep learning and expert knowledge. Finally, we proposed a practical APT attack attribution method with attribution and countermeasures. CSKG4APT is not a passive defense method in traditional network confrontation but one that integrates a large amount of fragmented intelligence and can actively adjust its defense strategy. It lays the foundation for further dominance in network attack and defense.
A genetic algorithm with jumping gene and heuristic operators for traveling salesman problem

Aiming at the problems of slow convergence speed, low solution quality, and easily falling into a local optimum in solving traveling salesman problem (TSP) with genetic algorithm (GA), a genetic algorithm with jumping gene and heuristic operators (GA-JGHO) is proposed, which contains five modifications: (1) an improved roulette selection of combined fitness function is proposed to maintain population diversity and strengthen the exploitation ability, which is helpful to overcome the low population diversity with the standard roulette selection; (2) a bidirectional heuristic crossover (BHX) operator is proposed, which aims to increase the possibility of the potential offspring produced by crossover operation; (3) the combination mutation operator is presented to balance the exploration and exploitation ability; (4) a jumping gene operator is designed, which is beneficial to expand the searching space and reduce the possibility of falling into a local optimum; (5) a unique operator is added to avoid the occurrence of nimiety identical individuals in the population. Besides, the local search operator is integrated to enhance exploitation ability. Moreover, a large number of instances from TSPLIB and a real-world path optimization problem of the cruise robot are selected to verify the validity of the modifications and the potential of GA-JGHO. Experimental results and statistical analyses demonstrate that GA-JGHO performs better in quality stability, accuracy, and convergence speed compared with the other six algorithms.
Time-Aware Language Models as Temporal Knowledge Bases

Many facts come with an expiration date, from the name of the President to the basketball team Lebron James plays for. But language models (LMs) are trained on snapshots of data collected at a specific moment in time, and this can limit their utility, especially in the closed-book setting where the pretraining corpus must contain the facts the model should memorize. We introduce a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time and highlight problems with LMs at either end of the spectrum -- those trained on specific slices of temporal data, as well as those trained on a wide range of temporal data. To mitigate these problems, we propose a simple technique for jointly modeling text with its timestamp. This improves memorization of seen facts from the training time period, as well as calibration on predictions about unseen facts from future time periods. We also show that models trained with temporal context can be efficiently "refreshed" as new data arrives, without the need for retraining from scratch.
Blockchain and crypto-exposed US companies and major cryptocurrencies: The role of jumps and co-jumps

We examine whether the occurrence of jumps in the return of major cryptocurrencies increases the likelihood of jumps in the stock returns of blockchain and crypto-exposed US companies. We use two criteria to identify the US stocks with blockchain and cryptocurrency exposure; i) text search and ii) membership in the blockchain indices. We first detect that both asset classes are subject to jump behaviour. Then, we employ logistic regressions and show that the occurrence of jumps in some cryptocurrencies increases the probability of jumps in several blockchain and crypto-exposed companies. The co-jumping behaviour is not affected by the COVID-19 outbreak.