Reviewerly/exHarmony huggingface dataset:
A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.
Parallel Instance Query Network for Named Entity Recognition

Named entity recognition (NER) is a fundamental task in natural language processing.Recent works treat named entity recognition as a reading comprehension task, constructing typespecific queries manually to extract entities.This paradigm suffers from three issues.First, type-specific queries can only extract one type of entities per inference, which is inefficient.Second, the extraction for different types of entities is isolated, ignoring the dependencies between them.Third, query construction relies on external knowledge and is difficult to apply to realistic scenarios with hundreds of entity types.To deal with them, we propose Parallel Instance Query Network (PIQN), which sets up global and learnable instance queries to extract entities from a sentence in a parallel manner.Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.Instead of being constructed from external knowledge, instance queries can learn their different query semantics during training.For training the model, we treat label assignment as a one-to-many Linear Assignment Problem (LAP) and dynamically assign gold entities to instance queries with minimal assignment cost.Experiments on both nested and flat NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models 1 .
Cognitive Semantic Communication Systems Driven by Knowledge Graph

Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, the existing semantic communication frameworks do not involve inference and error correction, which limits the achievable performance. In this paper, in order to tackle this issue, a cognitive semantic communication framework is proposed by exploiting knowledge graph. Moreover, a simple, general and interpretable solution for semantic information detection is developed by exploiting triples as semantic symbols. It also allows the receiver to correct errors occurring at the symbolic level. Furthermore, the pre-trained model is fine-tuned to recover semantic information, which overcomes the drawback that a fixed bit length coding is used to encode sentences of different lengths. Simulation results on the public WebNLG corpus show that our proposed system is superior to other benchmark systems in terms of the data compression rate and the reliability of communication.
Assessment of the Blockchain Technology Adoption for the Management of the Electronic Health Record Systems

Blockchain has applications in a broad range of settings, including healthcare (e.g., patient data management, health supply chain management, financial and insurance claims, and preventing counterfeit medications). There are, however, challenges associated with implementing such solutions, for example project failure rate, and the need for various parties to function together. Currently, there is a lack of quantitative and comprehensive models in the literature that researchers and practitioners can use to assess healthcare organizations’ readiness for blockchain adoption, particularly from multiple perspectives and using expert judgements. Thus, the objective of this article is to develop a scoring model to evaluate healthcare organization's readiness to adopt blockchain in the context of electronic health records system management. Specifically, in this article, a literature review and expert feedback were used to identify the most important factors influencing blockchain adoption. The outcomes of the article present an identification and prioritization of the blockchain adoption impacting factors. The article identified 17 factors. These factors are grouped into five perspective: financial, social, technical, organizational, and regulations and legal. The results of the article highlight that regulation compliance, regulation uncertainty, budget availability, management support, security and privacy, and financial risk and uncertainty play an important role in the organization's readiness for blockchain adoption. The readiness scoring model then developed and evaluated through two real-world case studies at two healthcare organizations. Two case studies were conducted to demonstrate the practicality of the research model, identify areas of deficiencies, and propose corrective actions in the healthcare organization's capabilities. The goal is to prevent any possible issues, before the project starts, in order to increase chances of a successful blockchain adoption.
Building customer loyalty in online shopping: the role of online trust, online satisfaction and electronic word of mouth

Various scholars have empirically investigated customer beliefs regarding satisfaction, loyalty and trust in the context of B2C e-commerce in several countries. However, the number of investigations into such important aspects of e-commerce in developing Arab countries, such as Jordan, is limited. Therefore, the main aim of this study is to explore the main factors that affect the degree to which consumers in Jordan are satisfied with, loyal to and trust B2C e-commerce. This research relies on previous and related literature to formulate a conceptual research model that proposes relationships between online trust, satisfaction and loyalty, and their determinants. To examine the proposed model, a self-administered paper-based survey questionnaire was issued to B2C e-commerce customers in a Jordanian private university. The findings suggest that customer loyalty to B2C e-commerce in Jordan is significantly influenced by online customer satisfaction, electronic word of mouth (eWOM) and online trust.
Recurrent dendritic neuron model artificial neural network for time series forecasting

Various neuron models have been proposed in the literature. Their structures are the simplest imitation for the biological neuron models. The dendritic neuron model is closer to biological neuron than the others. The single dendritic neuron model artificial neural networks have produced good forecasting results in the literature. In this study, a new recurrent dendritic neuron model artificial neural network is proposed. Moreover, a training algorithm based on particle swarm optimization is proposed. The performance of the proposed method is examined on SP500 stock exchange data sets. The proposed method produces better forecasts than long short term memory and Pi-Sigma artificial neural network. Moreover, the forecasting performance of the proposed method is investigated by using M3 and M4 competitions yearly data, totally used 23645-time series. According to application results, the proposed method produces very competitive results and it is the second-best method for M3 competition yearly data and it is the fifth-best method for M4 competition yearly data. Moreover, it is the best machine learning method for both M3 and M4 performance.
Extraction of indoor objects based on the exponential function density clustering model

Indoor point cloud includes wall, ceiling, floor and many other indoor objects. Extraction of wall, ceiling, floor and many objects in the room is the key for many applications including object identification, facility management and reconstruction of building. In view of this, this paper uses exponential function to construct density clustering model according to the local density within cutoff distance. First, the distances between boundary and indoor point cloud are used to construct the constraint condition of wall density clustering. Second, ceiling and floor are extracted according to the density clustering of z direction and local density model. Third, the distance δi is determined according to the size of local density. Simultaneously, we find that the cluster centers are recognized as points for which the product value of the local density and distance δi is anomalously large. Finally, the cluster belonging of each point to the cluster center is determined according to the distance between each point and cluster center. Finally, indoor objects extraction is achieved by judging the distance between neighboring clusters. We conduct the extraction of indoor objects of different type scenes and compare with clustering and deep learning methods. Comparison results show that the proposed method is superior to the clustering and deep learning methods when adjacent objects are not close to each other, but inferior to the deep learning methods when they are next to each other. Additionally, the extraction accuracy, recall and F1-score are calculated according to the matching rate, TP, FP and FN. It illustrates that the performance of the proposed method is affected by the degree of closeness between objects.
Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.
Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

The rapid technological development of computing power and system operations today allows for increasingly advanced algorithm implementation, as well as path planning in real time. The objective of this article is to provide a structured review of simulations and practical implementations of collision-avoidance and path-planning algorithms in autonomous underwater vehicles (AUVs). The novelty of the review paper is to consider not only the results of numerical research but also the newest results of verifying collision-avoidance and path-planning algorithms in real applications together with a comparison of the difficulties encountered during simulations and their practical implementation. Analysing the last 20 years of AUV development, it can be seen that experiments in a real environment are dominated by classical methods. In the case of simulation studies, artificial intelligence (AI) methods are used as often as classical methods. In simulation studies, the APF approach is most often used among classical methods, whereas among AI algorithms reinforcement learning and fuzzy logic methods are used. For real applications, the most used approach is reactive behaviors, and AI algorithms are rarely used in real implementations. Finally, this article provides a general summary, future works, and a discussion of the limitations that inhibit the further development in this field.
BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models.
An Efficient Detection and Classification of Acute Leukemia Using Transfer Learning and Orthogonal Softmax Layer-Based Model

For the early diagnosis of hematological disorders like blood cancer, microscopic analysis of blood cells is very important. Traditional deep CNNs lead to overfitting when it receives small medical image datasets such as ALLIDB1, ALLIDB2, and ASH. This paper proposes a new and effective model for classifying and detecting Acute Lymphoblastic Leukemia (ALL) or Acute Myelogenous Leukemia (AML) that delivers excellent performance in small medical datasets. Here, we have proposed a novel Orthogonal SoftMax Layer (OSL)-based Acute Leukemia detection model that consists of ResNet 18-based deep feature extraction followed by efficient OSL-based classification. Here, OSL is integrated with the ResNet18 to improve the classification performance by making the weight vectors orthogonal to each other. Hence, it integrates ResNet benefits (residual learning and identity mapping) with the benefits of OSL-based classification (improvement of feature discrimination capability and computational efficiency). Furthermore, we have introduced extra dropout and ReLu layers in the architecture to achieve a faster network with enhanced performance. The performance verification is performed on standard ALLIDB1, ALLIDB2, and <inline-formula><tex-math notation="LaTeX">$ C\_{N}MC\_{2}019$</tex-math></inline-formula> datasets for efficient ALL detection and ASH dataset for effective AML detection. The experimental performance demonstrates the superiority of the proposed model over other compairing models.
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling

Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensation. However, such temporal modeling techniques increase the model complexity and might fail in case of occlusion or complex motion, resulting in serious distortion and artifacts. In this paper, we propose to explore the role of explicit temporal difference modeling in both LR and HR space. Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference. They are separately processed with two branches of different receptive fields in order to better extract complementary information. To further enhance the super-resolution result, not only spatial residual features are extracted, but the difference between consecutive frames in high-frequency domain is also computed. It allows the model to exploit intermediate SR results in both future and past to refine the current SR output. The difference at different time steps could be cached such that information from further distance in time could be propagated to the current frame for refinement. Experiments on several video super-resolution benchmark datasets demonstrate the effectiveness of the proposed method and its favorable performance against state-of-the-art methods.
Towards Green Metaverse Networking: Technologies, Advancements and Future Directions

As the Metaverse is iteratively being defined, its potential to unleash the next wave of digital disruption and create real-life value becomes increasingly clear. With distinctive features of immersive experience, simultaneous interactivity, and user agency, the Metaverse has the capability to transform all walks of life. However, the enabling technologies of the Metaverse, i.e., digital twin, artificial intelligence, blockchain, and extended reality, are known to be energy-hungry, therefore raising concerns about the sustainability of its large-scale deployment and development. This article proposes Green Metaverse Networking for the first time to optimize energy efficiencies of all network components for Metaverse sustainable development. We first analyze energy consumption, efficiency, and sustainability of energy-intensive technologies in the Metaverse. Next, focusing on computation and networking, we present major advancements related to energy efficiency and their integration into the Metaverse. A case study of energy conservation by incorporating semantic communication and stochastic resource allocation in the Metaverse is presented. Finally, we outline the critical challenges of Metaverse sustainable development, thereby indicating potential directions of future research towards the green Metaverse.
An Overview on Research Trends, Challenges, Applications and Future Direction in Digital Image Watermarking

Digital data including photographs, audio, and video are now readily available because to the development of the Internet.The ease of access to multimedia raises concerns about ownership identification, authentication of content, security, and copyright protection.Here, we talk about the idea of digital picture watermarking with an emphasis on the method utilized for embedding and extracting the watermark from images.This paper also presents a complete classification of digital watermarking along with its fundamental properties, such as visual imperceptibility, resilience, capacity, and security.Additionally, we have covered the most recent uses of digital watermarking in the fields of healthcare, distance learning, electronic voting, and the military.The robustness is assessed by looking at how image processing assaults affect the content that is signed and the recoverability of the watermark.The thorough survey that is offered in this study, in the opinion of the authors, will aid brand-new scholars in learning more about this area.Additionally, the comparative study can spark suggestions for how to enhance the methods already outlined.
Constructing Complex NPC Behavior via Multi-Objective Neuroevolution

It is difficult to discover effective behavior for NPCs automatically. For instance, evolutionary methods can learn sophisticated behaviors based on a single objective, but realistic game playing requires different behaviors at different times. Such complex behavior is difficult to achieve. What is needed are multi-objective methods that reward different behaviors separately, and allow them to be combined to produce multi-modal behavior. While such methods exist, they have not yet been applied to generating multi-modal behavior for NPCs. This paper presents such an application: In a domain with noisy evaluations and contradictory fitness objectives, evolution based on a scalar fitness function is inferior to multi-objective optimization. The multi-objective approach produces agents that excel at the task and develop complex, interesting behaviors.
Stochastic Negative Mining for Learning with Large Output Spaces

We consider the problem of retrieving the most relevant labels for a given input when the size of the output space is very large. Retrieval methods are modeled as set-valued classifiers which output a small set of classes for each input, and a mistake is made if the label is not in the output set. Despite its practical importance, a statistically principled, yet practical solution to this problem is largely missing. To this end, we first define a family of surrogate losses and show that they are calibrated and convex under certain conditions on the loss parameters and data distribution, thereby establishing a statistical and analytical basis for using these losses. Furthermore, we identify a particularly intuitive class of loss functions in the aforementioned family and show that they are amenable to practical implementation in the large output space setting (i.e. computation is possible without evaluating scores of all labels) by developing a technique called Stochastic Negative Mining. We also provide generalization error bounds for the losses in the family. Finally, we conduct experiments which demonstrate that Stochastic Negative Mining yields benefits over commonly used negative sampling approaches.