Reviewerly/exHarmony huggingface dataset:
A Honeybee-Inspired Framework for a Smart City Free of Internet Scams

Internet scams are fraudulent attempts aim to lure computer users to reveal their credentials or redirect their connections to spoofed webpages rather than the actual ones. Users' confidential information, such as usernames, passwords, and financial account numbers, is the main target of these fraudulent attempts. Internet scammers often use phishing attacks, which have no boundaries, since they could exceed hijacking conventional cyber ecosystems to hack intelligent systems, which emerged recently for the use within smart cities. This paper therefore develops a real-time framework inspired by the honeybee defense mechanism in nature for filtering phishing website attacks in smart cities. In particular, the proposed framework filters phishing websites through three main phases of investigation: PhishTank-Match (PM), Undesirable-Absent (UA), and Desirable-Present (DP) investigation phases. The PM phase is used at first in order to check whether the requested URL is listed in the blacklist of the PhishTank database. On the other hand, the UA phase is used for investigation and checking for the absence of undesirable symbols in uniform resource locators (URLs) of the requested website. Finally, the DP phase is used as another level of investigation in order to check for the presence of the requested URL in the desirable whitelist. The obtained results show that the proposed framework is deployable and capable of filtering various types of phishing website by maintaining a low rate of false alarms.
HFR-Video-Based Stereo Correspondence Using High Synchronous Short-Term Velocities

This study focuses on solving the correspondence problem of multiple moving objects with similar appearances in stereoscopic videos. Specifically, we address the multi-camera correspondence problem by taking into account the pixel-level and feature-level stereo correspondences, and object-level cross-camera multiple object correspondence. Most correspondence algorithms rely on texture and color information of the stereo images, making it challenging to distinguish between similar-looking objects, such as ballet dancers and corporate employees wearing similar dresses, or farm animals such as chickens, ducks, and cows. However, by leveraging the low latency and high synchronization of high-speed cameras, we can perceive the phase and frequency differences between the movements of similar-looking objects. In this study, we propose using short-term velocities (STVs) of objects as motion features to determine the correspondence of multiple objects by calculating the similarity of STVs. To validate our approach, we conducted stereo correspondence experiments using markers attached to a metronome and natural hand movements to simulate simple and complex motion scenes. The experimental results demonstrate that our method achieved good performance in stereo correspondence.
A Patch Information Supplement Transformer for Person Re-Identification

Extracting fine-grained features from person images has proven crucial in person re-identification (re-ID). Although the research of convolutional neural networks (CNN) has been very successful in person re-ID, due to the small receptive field and downsampling operation, the existing CNNs cannot solve the problem of information loss. The multi-head attention modules in transformer can solve the above problems well. However, since dicing operations destroy the spatial correlation between patches, transformer still loses some local features. in this paper, we propose the scheme of the patch information supplement transformer (PIT) to extract fine-grained features in the dicing stage. Patch pyramid network (PPN) is introduced to solve the problem of local information loss. This is accomplished by dividing the image into different scales through the dicing operation and adding them together from top to bottom according to the pyramid structure. In addition, we insert a learnable identity information-embedding module (IDE) to reduce the feature bias of clothing and camera perspective. Experiments verify the superiority and effectiveness of PIT compared to state-of-the-art methods.
Topological Vortexes, Asymptotic Freedom, and Multifractals

We study the Kelvinons: monopole ring solutions to the Euler equations, regularized as the Burgers vortex in the viscous core. There is finite anomalous dissipation in the inviscid limit. However, in the anomalous Hamiltonian, some terms are growing as logarithms of Reynolds number; these terms come from the core of the Burgers vortex. In our theory, the turbulent multifractal phenomenon is similar to asymptotic freedom in QCD, with these logarithmic terms summed up by an RG equation. The small effective coupling does not imply small velocity; on the contrary, velocity is large compared to its fluctuations, which opens the way for a quantitative theory. In the leading order in the perturbation theory in this effective coupling constant, we compute running multifractal dimensions for high moments of velocity circulation, in good agreement with the data for quantum Turbulence and available data for classical Turbulence. The logarithmic dependence of fractal dimensions on the loop size comes from the running coupling in anomalous dimensions. This slow logarithmic drift of fractal dimensions would be barely observable at Reynolds numbers achievable at modern DNS.
A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios

Localization and tracking in multi-player sports present significant challenges, particularly in wide and crowded scenes where severe occlusions can occur. Traditional solutions relying on a single camera are limited in their ability to accurately identify players and may result in ambiguous detection. To overcome these challenges, we proposed fusing information from multiple cameras positioned around the field to improve positioning accuracy and eliminate occlusion effects. Specifically, we focused on soccer, a popular and representative multi-player sport, and developed a multi-view recording system based on a 1+N strategy. This system enabled us to construct a new benchmark dataset and continuously collect data from several sports fields. The dataset includes 17 sets of densely annotated multi-view videos, each lasting 2 min, as well as 1100+ min multi-view videos. It encompasses a wide range of game types and nearly all scenarios that could arise during real game tracking. Finally, we conducted a thorough assessment of four multi-view multi-object tracking (MVMOT) methods and gained valuable insights into the tracking process in actual games.
Cross-Modal Image Registration via Rasterized Parameter Prediction for Object Tracking

Object tracking requires heterogeneous images that are well registered in advance, with cross-modal image registration used to transform images of the same scene generated by different sensors into the same coordinate system. Infrared and visible light sensors are the most widely used in environmental perception; however, misaligned pixel coordinates in cross-modal images remain a challenge in practical applications of the object tracking task. Traditional feature-based approaches can only be applied in single-mode scenarios, and cannot be well extended to cross-modal scenarios. Recent deep learning technology employs neural networks with large parameter scales for prediction of feature points for image registration. However, supervised learning methods require numerous manually aligned images for model training, leading to the scalability and adaptivity problems. The Unsupervised Deep Homography Network (UDHN) applies Mean Absolute Error (MAE) metrics for cost function computation without labelled images; however, it is currently inapplicable for cross-modal image registration. In this paper, we propose aligning infrared and visible images using a rasterized parameter prediction algorithm with similarity measurement evaluation. Specifically, we use Cost Volume (CV) to predict registration parameters from coarse-grained to fine-grained layers with a raster constraint for multimodal feature fusion. In addition, motivated by the utilization of mutual information in contrastive learning, we apply a cross-modal similarity measurement algorithm for semi-supervised image registration. Our proposed method achieves state-of-the-art performance on the MS-COCO and FLIR datasets.
Householder Transformation-Based Temporal Knowledge Graph Reasoning

Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effective at modeling entities and relations on a knowledge graph. However, due to the lack of temporal information representation capability, existing approaches can only model partial relational patterns and they cannot handle temporal combination reasoning. In this regard, we propose HTTR: Householder Transformation-based Temporal knowledge graph Reasoning, which focuses on the characteristics of relations that evolve over time. HTTR first fuses the relation and temporal information in the knowledge graph, then uses the Householder transformation to obtain an orthogonal matrix about the fused information, and finally defines the orthogonal matrix as the rotation of the head-entity to the tail-entity and calculates the similarity between the rotated vector and the vector representation of the tail entity. In addition, we compare three methods for fusing relational and temporal information. We allow other fusion methods to replace the current one as long as the dimensionality satisfies the requirements. We show that HTTR is able to outperform state-of-the-art methods in temporal knowledge graph reasoning tasks and has the ability to learn and infer all of the four relational patterns over time: symmetric reasoning, antisymmetric reasoning, inversion reasoning, and temporal combination reasoning.
Knowledge map construction based on association rule mining extending with interaction frequencies and knowledge tracking for rules cleaning

Knowledge maps are teaching tools that can promote deeply learning and avoid knowledge loss by helping students plan learning paths. Mining potential association rules of concepts from student exercise data was a common method to construct knowledge maps automatically. While manual conditions should be set to filter the association rules future to improve the accuracy of knowledge maps, which made the construction of the knowledge map can not automatic totally. So, the study proposed a knowledge map construction method that combined knowledge tracking and association rule mining expanding with interaction frequencies based on exercise data to achieve rules cleaning automatically. The method first predicted students’ knowledge mastery sequences by a deep knowledge tracking model and discovered clustering relations to represent potential structures between concepts by fuzzy cluster analysis. Meanwhile, the method investigated association rule mining expanding with interaction frequencies to discover association rules between concepts. Finally, the clustering relations were used to filter the mined association rules automatically. To verify the effectiveness of the method, we constructed a knowledge map based on 34,350 online exercise data of 117 students in a computer programming course. Experimental results proved that the map was valid. Our implementations are available at https://github.com/xxdmw/FPGF-master.
MFF: An effective method of solving the ill regions in stereo matching

Abstract In the current stereo matching field, the accuracy of the derived disparity map is highly dependent on the processing capability of ill regions. Fortunately, we find that the use of local information will eliminate the negative effects associated with ill regions. As a result of the above discovery, we propose the Concatenated Dilated Convolution (CDC) block and the Multi‐scale Feature Fusion module (MFF), which are capable of effectively extracting regional context by increasing the receptive field in parallel and channel‐wise ways. The CDC block can expand the receptive field by applying multiple dilated convolutions at different dilation rates in parallel to enhance the smoothness of the feature map. By constructing parallel CDC blocks in a multiple dilated manner, the MFF module can improve the smoothness of the feature map. In addition, to control the number of parameters in the MFF network, a high‐performance channel‐distribution algorithm is proposed, capable of adjusting the weights of each module and convolution in an adaptive manner while reducing the number of parameters. Extensive experiments have demonstrated that MFF and CDC can effectively improve the performance of ill areas and networks with a minimal number of parameters.
A New Hybrid Method for Secure Data Transmission Using Watermarking based on Fuzzy Encryption in IoT

Here, irregular fuzzy mapping is used for encryption and scrambling of image information. The proposed image encryption algorithm has many characteristics including large key space, few relationships between the pixels of the encrypted image, high sensitivity to the key and high security, which can effectively protect the security of the encrypted image. Therefore, a robust audio watermarking method that hides encrypted image information in the time domain is presented. After dividing the audio signal into consecutive parts, five parameters are calculated independently, including the average frequency, edge, energy, zero crossing rate, and standard deviation for different parts of the signal. With the LBP method, the encoded image is placed in different parts based on the weights determined by the fuzzy system defined on the five parameters for each part of the audio signal. Consequently, the distortion caused by hiding the watermark will not result in adverse listening effects, and the watermark will remain resistant to attacks and common audio processing. Based on the simulation results of the proposed technique under MATLAB software, it reveals the resistance of the watermark against various attacks and increasing the speed of calculations. Due to the combination of watermarking and simultaneous encoding of image information, as well as examining a large number of image evaluation criteria, including information entropy criteria and peak signal-to-noise ratio (PSNR), number of pixel change rate (NPCR), unified average changing intensity (UACI), we have been able to create a good improvement for the security of information transmission in the IoMT field.
An Analysis of Innovative Development Based on Computer Assisted in Promoting the International Transmission of Chinese Culture under the Background of Big Data

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A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition

Courtesy amount recognition from bank checks is an important application of pattern recognition. Although much progress has been made on isolated digit recognition for Indian digits, there is no work reported in the literature on courtesy amount recognition for Arabic checks using Indian digits. Arabic check courtesy amount recognition comes with its own unique challenges that are not seen in isolated digit recognition tasks and, accordingly, need specific approaches to deal with them. This paper presents an end-to-end system for courtesy amount recognition starting from check images as input to recognizing amounts as a sequence of digits. The system is a hybrid system, combining rule-based modules as well as machine learning modules. For the amount recognition system, both segmentation-based and segmentation-free approaches were investigated and compared. We evaluated our system on the CENPARMI dataset of real bank checks in Arabic. We achieve 67.4% accuracy at the amount level and 87.15% accuracy at the digit level on the test set consisting of 626 check images. The results are presented with detailed analysis, and some possible future work is identified. This work can be used as a baseline to benchmark future research in Arabic check courtesy amount recognition.
Cloud–Edge Hybrid Computing Architecture for Large-Scale Scientific Facilities Augmented with an Intelligent Scheduling System

Synchrotron radiation sources are widely used in interdisciplinary research, generating an enormous amount of data while posing serious challenges to the storage, processing, and analysis capabilities of the large-scale scientific facilities worldwide. A flexible and scalable computing architecture, suitable for complex application scenarios, combined with efficient and intelligent scheduling strategies, plays a key role in addressing these issues. In this work, we present a novel cloud–edge hybrid intelligent system (CEHIS), which was architected, developed, and deployed by the Big Data Science Center (BDSC) at the Shanghai Synchrotron Radiation Facility (SSRF) and meets the computational needs of the large-scale scientific facilities. Our methodical simulations demonstrate that the CEHIS is more efficient and performs better than the cloud-based model. Here, we have applied a deep reinforcement learning approach to the task scheduling system, finding that it effectively reduces the total time required for the task completion. Our findings prove that the cloud–edge hybrid intelligent architectures are a viable solution to address the requirements and conditions of the modern synchrotron radiation facilities, further enhancing their data processing and analysis capabilities.
An Adaptive Model Filtering Algorithm Based on Grubbs Test in Federated Learning

Federated learning has been popular for its ability to train centralized models while protecting clients' data privacy. However, federated learning is highly susceptible to poisoning attacks, which can result in a decrease in model performance or even make it unusable. Most existing defense methods against poisoning attacks cannot achieve a good trade-off between robustness and training efficiency, especially on non-IID data. Therefore, this paper proposes an adaptive model filtering algorithm based on the Grubbs test in federated learning (FedGaf), which can achieve great trade-offs between robustness and efficiency against poisoning attacks. To achieve a trade-off between system robustness and efficiency, multiple child adaptive model filtering algorithms have been designed. Meanwhile, a dynamic decision mechanism based on global model accuracy is proposed to reduce additional computational costs. Finally, a global model weighted aggregation method is incorporated, which improves the convergence speed of the model. Experimental results on both IID and non-IID data show that FedGaf outperforms other Byzantine-robust aggregation rules in defending against various attack methods.
A New Technical Ear Training Game and Its Effect on Critical Listening Skills

Technical ear training has proven to be an effective tool for developing the skills of junior audio engineers and enhancing their proficiency in audio and music production. To provide a comprehensive auditory training experience, the authors have created a gamified training program that encompasses four modules: spectral identification, auditory localization, consistent judgment, and memory of a mix balance. Each module is designed to give trainees closed-loop audiomotor training, allowing them to instantly assess their performance and identify areas where they need to improve. This new ear-training game helped players to be more engaged and resulted in the improvement of trained audio engineering skills. Moreover, the game also benefited a non-trained auditory skill, speech understanding in noise.
Deep Ising Born Machine

Abstract A quantum neural network (QNN) is a method to find patterns in quantum data and has a wide range of applications including quantum chemistry, quantum computation, quantum metrology, and quantum simulation. Efficiency and universality are two desirable properties of a QNN but are unfortunately contradictory. In this work, a deep Ising Born machine (DIBoM) is examined, and shown that it has a good balance between efficiency and universality. More precisely, the DIBoM has a flexible number of parameters to be efficient, and achieves provable universality with sufficient parameters. The architecture of the DIBoM is based on generalized controlled‐Z gates, conditional gates, and some other ingredients. To compare the universality of the DIBoM with other QNNs, a fidelity‐based expressivity measure is proposed, which may be of independent interest. Extensive empirical evaluations corroborate that the DIBoM is both efficient and expressive.
Dynamic role discovery and assignment in multi-agent task decomposition

Abstract Effective multi-agent teamwork can be facilitated by using personas to decompose goals into lower-level team subtasks through a shared understanding of multi-agent tasks. However, traditional methods for role discovery and assignment are not scalable and fail to adapt to dynamic changes in the environment. To solve this problem, we propose a new framework for learning dynamic role discovery and assignment. We first introduce an action encoder to construct a vector representation for each action based on its characteristics, and define and classify roles from a more comprehensive perspective based on both action differences and action contributions. To rationally assign roles to agents, we propose a representation-based role selection policy based on consideration of role differences and reward horizons, which enables agents to play roles dynamically by dynamically assigning agents with similar abilities to play the same role. Agents playing the same role share their learning of the role, and different roles correspond to different action spaces. We also introduce regularizers to increase the differences between roles and stabilize training by preventing agents from changing roles frequently. Role selection and role policy integrate action representations and role differences in a restricted action space, improving learning efficiency. We conducted experiments in the SMAC benchmark and showed that our method enables effective role discovery and assignment, outperforming the baseline on four of the six scenarios, with an average improvement in win rate of 20 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , and is effective in hard and super hard maps. We also conduct ablation experiments to demonstrate the importance of each component in our approach.