Reviewerly/exHarmony huggingface dataset:
Analyzing Miners’ Dynamic Equilibrium in Blockchain Networks under DDoS Attacks

Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing potential security threats to blockchain systems. A distributed denial of service (DDoS) attack is a major threat to the PoW algorithm. It utilizes multiple nodes in the blockchain network to attack honest miners to obtain illegal rewards. To solve this problem, academia has proposed a DDoS attack detection mechanism based on reinforcement learning methods and static game modeling methods based on mining pools. However, these methods cannot effectively make miners choose the strategy with the best profit over time when facing DDoS attacks. Therefore, this paper proposes a dynamic evolutionary game model for miners facing DDoS attacks under blockchain networks to solve the above problems for the first time. We address the model by replicating the dynamic equation to obtain a stable solution. According to the theorem of the Lyapunov method, we also obtain the only stable strategy for miners facing DDoS attacks. The experimental results show that compared with the static method, the dynamic method can affect game playing and game evolution over time. Moreover, miners’ strategy to face DDoS attacks gradually shifts from honest mining to launching DDoS attacks against each other as the blockchain network improves.
An Improved S2A-Net Algorithm for Ship Object Detection in Optical Remote Sensing Images

Ship detection based on remote sensing images holds significant importance in both military and economic domains. Ships within such images exhibit diverse scales, dense distributions, arbitrary orientations, and narrow shapes, which pose challenges for accurate recognition. This paper introduces an improved S2A-Net (Single-shot Alignment Network) based oriented object detection algorithm for ship detection. In network structure, pyramid squeeze attention is embedded in order to focus on key features and a context information module is designed to enhance the context understanding capability of the network. In the training strategy, considering the distortion problems such as blurring and low contrast in remote sensing images, a fog density and depth decomposition-based unpaired image dehazing network D4 is adopted to improve the image quality, besides, an image weight sampling strategy is proposed to enhance the training opportunities of small and difficult samples, thereby mitigating the issue of imbalanced ship category distribution. Experimental results demonstrate that the improved S2A-Net algorithm achieves the mean average precision of 77.27% for ship detection in the FAIR1M dataset, which is 5.6% better than the original S2A-Net algorithm, and outperforms the current common object detection algorithms.
Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.
Attention-Mechanism-Based Models for Unconstrained Face Recognition with Mask Occlusion

Masks cover most areas of the face, resulting in a serious loss of facial identity information; thus, how to alleviate or eliminate the negative impact of occlusion is a significant problem in the field of unconstrained face recognition. Inspired by the successful application of attention mechanisms and capsule networks in computer vision, we propose ECA-Inception-Resnet-Caps, which is a novel framework based on Inception-Resnet-v1 for learning discriminative face features in unconstrained mask-wearing conditions. Firstly, Squeeze-and-Excitation (SE) modules and Efficient Channel Attention (ECA) modules are applied to Inception-Resnet-v1 to increase the attention on unoccluded face areas, which is used to eliminate the negative impact of occlusion during feature extraction. Secondly, the effects of the two attention mechanisms on the different modules in Inception-Resnet-v1 are compared and analyzed, which is the foundation for further constructing the ECA-Inception-Resnet-Caps framework. Finally, ECA-Inception-Resnet-Caps is obtained by improving Inception-Resnet-v1 with capsule modules, which is explored to increase the interpretability and generalization of the model after reducing the negative impact of occlusion. The experimental results demonstrate that both attention mechanisms and the capsule network can effectively enhance the performance of Inception-Resnet-v1 for face recognition in occlusion tasks, with the ECA-Inception-Resnet-Caps model being the most effective, achieving an accuracy of 94.32%, which is 1.42% better than the baseline model.
Addressing Long-Distance Dependencies in AMR Parsing with Hierarchical Clause Annotation

Most natural language processing (NLP) tasks operationalize an input sentence as a sequence with token-level embeddings and features, despite its clausal structure. Taking abstract meaning representation (AMR) parsing as an example, recent parsers are empowered by transformers and pre-trained language models, but long-distance dependencies (LDDs) introduced by long sequences are still open problems. We argue that LDDs are not actually to blame for the sequence length but are essentially related to the internal clause hierarchy. Typically, non-verb words in a clause cannot depend on words outside of it, and verbs from different but related clauses have much longer dependencies than those in the same clause. With this intuition, we introduce a type of clausal feature, hierarchical clause annotation (HCA), into AMR parsing and propose two HCA-based approaches, HCA-based self-attention (HCA-SA) and HCA-based curriculum learning (HCA-CL), to integrate HCA trees of complex sentences for addressing LDDs. We conduct extensive experiments on two in-distribution (ID) AMR datasets (AMR 2.0 and AMR 3.0) and three out-of-distribution (OOD) ones (TLP, New3, and Bio). Experimental results show that our HCA-based approaches achieve significant and explainable improvements (0.7 Smatch score in both ID datasets; 2.3, 0.7, and 2.6 in three OOD datasets, respectively) against the baseline model and outperform the state-of-the-art (SOTA) model (0.7 Smatch score in the OOD dataset, Bio) when encountering sentences with complex clausal structures that introduce most LDD cases.
Task type matters: The impact of virtual reality training on training performance

Abstract Background Virtual reality (VR) is becoming increasingly accessible and being utilized in various organizations to meet education and training needs. Despite its potential, research regarding VR applications has focused on measuring the effectiveness of VR programs relative to non‐VR programs; consequently, the ways in which VR programs are effective remain unclear. Objectives Recognizing this research gap, this study examined whether task type‐dependent differences in training outcomes exist between VR training and non‐VR training groups. Methods To examine the effects of VR technology for training purposes in a military setting, we conducted a quasi‐experiment focusing on how the effects vary based on task characteristics. A total of 90 military personnel were randomly assigned to either an experimental group (VR) or a control group (non‐VR). After completing their respective training, the two groups participated in four tasks simulating real battle scenarios and designed to evaluate the effectiveness of the VR‐based training. We analysed primary data collected through survey instruments and performance evaluation using a one‐way between‐group analysis of covariance (ANCOVA) and supplementary interview data using thematic analysis. Results and Conclusions The quantitative analysis showed that the VR group performed better than the control group on three tasks in which communication, interaction and immediate situational judgements were critical. No difference was found between the two groups in the other task, which involved the routine operation of physical objects. Additional interview data revealed the extent to which trainees perceived the VR training as effective in understanding in‐situ conditions, preparing and practising their own and their team's reactions to emergent situations and in manipulating virtual objects. Implications Our findings provide insight into VR technology's potential to enhance human performance in various training contexts. VR is specifically effective in training that aim to improve immediate judgement and group coordination. Our findings provide useful information for those seeking to design and develop training environments that maximize the effects of VR.
Implementing a Synchronization Method between a Relational and a Non-Relational Database

The accelerating pace of application development requires more frequent database switching, as technological advancements demand agile adaptation. The increase in the volume of data and at the same time, the number of transactions has determined that some applications migrate from one database to another, especially from a relational database to a non-relational (NoSQL) alternative. In this transition phase, the coexistence of both databases becomes necessary. In addition, certain users choose to keep both databases permanently updated to exploit the individual strengths of each database in order to streamline operations. Existing solutions mainly focus on replication, failing to adequately address the management of synchronization between a relational and a non-relational (NoSQL) database. This paper proposes a practical IT approach to this problem and tests the feasibility of the proposed solution by developing an application that maintains the synchronization between a MySQL database as a relational database and MongoDB as a non-relational database. The performance and capabilities of the solution are analyzed to ensure data consistency and correctness. In addition, problems that arose during the development of the application are highlighted and solutions are proposed to solve them.
Tau Prolog: A Prolog Interpreter for the Web

Abstract Tau Prolog is a client-side Prolog interpreter fully implemented in JavaScript, which aims at implementing the ISO Prolog Standard. Tau Prolog has been developed to be used with either Node.js or a browser seamlessly, and therefore, it has been developed following a non-blocking, callback-based approach to avoid blocking web browsers. Taking the best from JavaScript and Prolog, Tau Prolog allows the programmer to handle browser events and manipulate the Document Object Model (DOM) of a web using Prolog predicates. In this paper we describe the architecture of Tau Prolog and its main packages for interacting with the Web, and we present its programming environment.
Multi-transformer based on prototypical enhancement network for few-shot relation classification with domain adaptation

Few-shot relation classification aims to predict the relation between entity pairs in unseen sentences with a few labeled sentences. In real-world scenarios, the target domains often have limited data and could differ significantly from the source domains. Existing studies focus on the representation of entity pairs, ignoring the relation between the sentences and the entity pairs. Some researchers use feature-adaptive methods that do not consider that features contribute differently to relation classification. To solve these challenges, a multi-transformer based on a prototypical enhancement network (MTPEnet) is presented for few-shot relation classification with domain adaptation in this study. Specifically, for MTPEnet, two main parts are conducted. First, in the calculation of the prototype, a local and global representations (LGR) module is established to improve the representation of sentences. LGR considers not only the head and tail entities in a sentence but also the relation between the entity pair and the entire sentence. Second, a multi-transformer (MT) feature transformation module is introduced to address the cross-domain problem. Different weights are assigned to the target domain features according to the degree of fusion between the features in the target and source domains. Finally, the experimental results on the datasets FewRel 2.0 and FewTAC demonstrate the superiority of MTPEnet over several baseline models.
Person Re-identification in Video Surveillance Systems Using Deep Learning: Analysis of the Existing Methods

This paper is devoted to a multifaceted analysis of person re-identification (ReID) in video surveillance systems and modern solution methods using deep learning. The general principles and application of convolutional neural networks for this problem are considered. A classification of person ReID systems is proposed. The existing datasets for training deep neural architectures are studied and approaches to increasing the number of images in databases are described. Approaches to forming human image features are considered. The backbone models of convolutional neural network architectures used for person ReID are analyzed and their modifications as well as training methods are presented. The effectiveness of person ReID is examined on different datasets. Finally, the effectiveness of the existing approaches is estimated in different metrics and the corresponding results are given.
Allotaxonometry and rank-turbulence divergence: a universal instrument for comparing complex systems

Abstract Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.
CISO: Co-iteration semi-supervised learning for visual object detection

Abstract Semi-supervised learning offers a solution to the high cost and limited availability of manually labeled samples in supervised learning. In semi-supervised visual object detection, the use of unlabeled data can significantly enhance the performance of deep learning models. In this paper, we introduce an end-to-end framework, named CISO (Co-Iteration Semi-Supervised Learning for Object Detection), which integrates a knowledge distillation approach and a collaborative, iterative semi-supervised learning strategy. To maximize the utilization of pseudo-label data and address the scarcity of pseudo-label data due to high threshold settings, we propose a mean iteration approach where all unlabeled data is applied to each training iteration. Pseudo-label data with high confidence is extracted based on an ever-changing threshold (average intersection over union of all pseudo-labeled data). This strategy not only ensures the accuracy of the pseudo-label but also optimizes the use of unlabeled data. Subsequently, we apply a weak-strong data augmentation strategy to update the model. Lastly, we evaluate CISO using Swin Transformer model and conduct comprehensive experiments on MS-COCO. Our framework showcases impressive results, outperforms the state-of-the-art methods by 2.16 mAP and 1.54 mAP with 10% and 5% labeled data, respectively.
Ask to Understand: Question Generation for Multi-hop Question Answering

Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Question Decomposition (QD) are two common approaches at present. The former uses the “black-box” reasoning process to capture the potential relationship between entities and sentences, thus achieving good performance. At the same time, the latter provides a clear reasoning logical route by decomposing multi-hop questions into simple single-hop sub-questions. In this paper, we propose a novel method to complete multi-hop QA from the perspective of Question Generation (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of our proposed QG module, human evaluation further clarifies its interpretability quantitatively, and thorough analysis shows that the QG module could generate better sub-questions than QD methods in terms of fluency, consistency, and diversity.
A Dataflow-Oriented Approach for Machine-Learning-Powered Internet of Things Applications

The rise of the Internet of Things (IoT) has led to an exponential increase in data generated by connected devices. Machine Learning (ML) has emerged as a powerful tool to analyze these data and enable intelligent IoT applications. However, developing and managing ML applications in the decentralized Cloud-to-Things continuum is extremely complex. This paper proposes Zenoh-Flow, a dataflow programming framework that supports the implementation of End-to-End (E2E) ML pipelines in a fully decentralized manner and abstracted from communication aspects. Thus, it simplifies the development and upgrade process of the next-generation ML-powered applications in the IoT domain. The proposed framework was demonstrated using a real-world use case, and the results showcased a significant improvement in overall performance and network usage compared to the original implementation. Additionally, other of its inherent benefits are a significant step towards developing efficient and scalable ML applications in the decentralized IoT ecosystem.
Research on Students’ Action Behavior Recognition Method Based on Classroom Time-Series Images

Students’ action behavior performance is an important part of classroom teaching evaluation. To detect the action behavior of students in classroom teaching videos, and based on the detection results, the action behavior sequence of individual students in the teaching time of knowledge points is obtained and analyzed. This paper proposes a method for recognizing students’ action behaviors based on classroom time-series images. First, we propose an improved Asynchronous Interaction Aggregation (AIA) network for student action behavior detection. By adding a Multi-scale Temporal Attention (MsTA) module and a Multi-scale Channel Spatial Attention (MsCSA) module to the fast pathway and slow pathway, respectively, the accuracy of student action behavior recognition is improved in SlowFast, which is the backbone network of the improved AIA network,. Second, the Equalized Focal Loss function is introduced to improve the category imbalance that exists in the student action behavior dataset. Experimental results on the student action behavior dataset show that the method proposed in this paper can detect different action behaviors of students in the classroom and has better detection performance compared to the original AIA network. Finally, based on the results of action behavior recognition, the seat number is used as the index to obtain the action behavior sequence of individual students during the teaching time of knowledge points and the performance of students in this period is analyzed.
Field Trips and Their Effect on Student Learning: A Comparison of Knowledge Assessment for Physical versus Virtual Field Trips in a Construction Management Course

Teaching through field trips has been very effective in the architecture, engineering and construction (AEC) disciplines as it allows students to bridge the gap between theory and practice. However, it is not always feasible to take a large class on field trips due to time, safety, and cost limitations. To adequately prepare future professionals in the AEC industry, it is imperative that institutions adopt innovative methods of providing the field trip experience. One such approach is using virtual reality (VR) technology. Creating 3D VR construction environments and immersing students in that virtual world could provide an engaging and meaningful experience. Although researchers in AEC schools have developed and deployed many virtual field trips (VFTs) in education, little is known about their potential to provide the same knowledge base. For that reason, a VR app was created to teach students about the design and construction of steel structures, called the Steel Sculpture App (SSA). The SSA served as a VFT, and the location of the steel frame structure served as the actual field trip (AFT). The research was conducted in structure-related courses in the spring, summer, and fall of 2021 and the spring and fall of 2022 semesters. Each semester, students were split into groups, one being the control group and the other being the experimental group. The control groups learned through AFTs, whereas the experimental groups learned through VFTs. A knowledge test was administered at the end of each treatment to collect quantitative data on the students’ performance, understanding, and knowledge retention. The results indicated that the students learning from VFTs scored higher than those learning from AFTs. The paper discusses student assessment results and student feedback about replacing AFTs with VFTs in times of need.