Reviewerly/exHarmony huggingface dataset:
An Improved Canny Edge Detection Algorithm with Iteration Gradient Filter

Edge detection is widely used in biological vision and computer vision. Canny edge detection is a common method to locate the sharp intensity changes and seek object boundaries. Nowadays, there are several improvement designs for Canny edge detection algorithms. In this paper, the Gaussian filtering is replaced by median filtering to increase the noise robustness of Canny edge detection. The inhabitant of the false edge is achieved by implementing an improved Sobel operator and iterative threshold filter method before the non-maximum suppression of the Canny operator. The final image is obtained after threshold filtering and binarization. The results show that the proposed algorithm is more robust to noise and can preserve more useful edge information by suppressing more false edges than traditional Canny edge detection.
FocMech-Flow: Automatic Determination of P-Wave First-Motion Polarity and Focal Mechanism Inversion and Application to the 2021 Yangbi Earthquake Sequence

P-wave first-motion polarity is important for the inversion of earthquake focal mechanism solutions. The focal mechanism solution can further contribute to our understanding of the source rupture process, the fault structure, and the regional stress field characteristics. By using the abundant focal mechanism solutions of small and moderate earthquakes, we can deepen our understanding of fault geometry and the seismogenic environment. In this paper, we propose an automatic workflow, FocMech-Flow (Focal Mechanism-Flow), for identifying P-wave first-motion polarity and focal mechanism inversion with deep learning and applied it to the 2021 Yangbi earthquake sequence. We use a deep learning model named DiTingMotion to detect the P-wave first-motion polarity of 2389 waveforms, resulting in 98.49% accuracy of polarity discrimination compared with human experts. The focal mechanisms of 112 earthquakes are obtained by using the CHNYTX program, which is 3.7 times more than that of the waveform inversion method, and the results are highly consistent. The analysis shows that the focal mechanisms of the foreshock sequence of the Yangbi earthquake are highly consistent and are all of the strike-slip type; the focal mechanisms of the aftershock sequence are complex, mainly the strike-slip type, but there are also reverse and normal fault types. This study shows that the deep learning method has high reliability in determining the P-wave first-motion polarity, and FocMech-Flow can obtain a large number of focal mechanism solutions from small and moderate earthquakes, having promising application in fine-scale stress inversion.
Machine Learning Enables Multi‐Degree‐of‐Freedom Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements

Abstract Machine learning can empower the design of cascaded diffractive optical elements (DOEs) at terahertz frequencies enabling the realization of holograms with a tailored multi‐degree‐of‐freedom reconfigurable operation when altering either the number, spacing, rotational alignment, and/or order of the elements. This unprecedented control over the spatial terahertz light distribution can profoundly impact multiple terahertz applications such as signal multiplexing, imaging, and displays. This work demonstrates this multi‐degree‐of‐freedom control in structures fabricated through 3D printing employing low‐loss materials. The designs are validated through 3D finite‐difference time‐domain (FDTD) simulations and experimental measurements, showing that, in all cases, the desired diffraction patterns are generated.
Cross-Receiver Radio Frequency Fingerprint Identification Based on Contrastive Learning and Subdomain Adaptation

Radio frequency fingerprint (RFF) identification is emerging as an attractive paradigm for physical layer security. Despite the exceptional accuracy achieved by deep learning (DL) based schemes, few works consider the cross-receiver scenario. The performance deteriorates significantly when the model is deployed on new receivers directly. To this end, a cross-receiver RFF learning scheme is proposed. First, an unsupervised pre-training method based on contrastive learning is utilized to extract receiver-agnostic features. Then, the model is optimized by subdomain adaptation to further improve identification performance. The proposed scheme does not require multiple labeled datasets from different receivers. And experimental results indicate that the proposed scheme effectively alleviates performance degradation in the cross-receiver scenario.
Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.
On the Vital Aspects and Characteristics of Cryptocurrency—A Survey

Cryptocurrencies acquire user confidence by making the whole creation and transaction history transparent to the public. In exchange, the transaction history accurately captures the complete range of user activities related to cryptocurrencies. It is thought to be one of the safest and simplest payment methods that may be employed in the future. The trend of banks and other financial institutions investing in cryptocurrencies has increased rapidly in recent years. Therefore, it is necessary to synthesize the findings of previous studies on cryptocurrencies. In this paper, the use of data mining methods in Bitcoin transactions is analyzed and summarized. Cryptocurrencies, similar to the well-known Bitcoin, were targeted to ensure transaction security and privacy and overcome the drawbacks of traditional banking systems as well as other centralized systems. In addition, a comprehensive analysis of the literature on the challenges and applications of electronic currencies is conducted. The evolution of digital currency from electronic cash to cryptocurrencies is summarized and the methods used to increase user privacy are highlighted. The security threats in existing cryptocurrency systems (that compromise the privacy of Bitcoin users) are also highlighted. Finally, several research gaps and trends are identified that need to be further explored.
Multiscaled Multi-Head Attention-Based Video Transformer Network for Hand Gesture Recognition

Dynamic gesture recognition is one of the challenging research areas due to variations in pose, size, and shape of the signer's hand. In this letter, Multiscaled Multi-Head Attention Video Transformer Network (MsMHA-VTN) for dynamic hand gesture recognition is proposed. A pyramidal hierarchy of multiscale features is extracted using the transformer multiscaled head attention model. The proposed model employs different attention dimensions for each head of the transformer which enables it to provide attention at the multiscale level. Further, in addition to single modality, recognition performance using multiple modalities is examined. Extensive experiments demonstrate the superior performance of the proposed MsMHA-VTN with an overall accuracy of 88.22% and 99.10% on NVGesture and Briareo datasets, respectively.
Speed Adaptive Robot Trajectory Generation Based on Derivative Property of B-Spline Curve

In order to improve the safety of autonomous flight of quadrotor in three-dimensional complex environments, this letter proposes a speed adaptive motion planning method by limiting the distance between two adjacent control points based on the derivative property of B-Spline curve. First the initial B-Spline curve control points and the speed constraints based on derivative property are efficiently obtained by the front-end search that is divided into the shortest safe path search and reasonable step targeted expansion satisfying dynamic constraints. Then the back-end optimization assigns the corresponding safe flight corridor to the local trajectories according to the stage of the initial control points and uses MINVO basis (Tordesillas and How, 2022) to limit the simplexes with minimum volume enclosing local trajectories. The trajectory generation is finally formulated as a Quadratically Constrained Quadratic Program (QCQP) which can be solved efficiently. Both simulations and real-world experiments validate the efficiency and performance of this method in generating safe, smooth, speed adaptive trajectories.
Data Integrity Audit Scheme Based on Quad Merkle Tree and Blockchain

Cloud storage is an essential method for data storage. Verifying the integrity of data in the cloud is critical for the client. Traditional cloud storage approaches rely on third-party auditors (TPAs) to accomplish auditing tasks. However, third-party auditors are often not trusted. To eliminate over-reliance on third-party auditors, this paper designs a blockchain-based auditing scheme that uses blockchain instead of third-party auditors to ensure the reliability of data auditing. Meanwhile, our scheme is based on the audit method of the quad Merkle hash tree, using the root of the quad Merkle hash tree to verify the integrity of data, which significantly improves computing and storage efficiency. Automated verification of auditing activities by deploying smart contracts on the blockchain allows us to have a more up-to-date picture of data integrity. The performance of the scheme is evaluated through security analysis and experiments, which prove that the proposed scheme is secure and effective.
Skeleton-Based Action Recognition Through Contrasting Two-Stream Spatial-Temporal Networks

For pursuing accurate skeleton-based action recognition, most prior methods use the strategy of combining Graph Convolution Networks (GCNs) with attention-based methods in a serial way. However, they regard the human skeleton as a complete graph, resulting in less variations between different actions (e.g., the connection between the elbow and head in action “clapping hands”). For this, we propose a novel Contrastive GCN-Transformer Network (ConGT) which fuses the spatial and temporal modules in a parallel way. The ConGT involves two parallel streams: Spatial-Temporal Graph Convolution stream (STG) and Spatial-Temporal Transformer stream (STT). The STG is designed to obtain action representations maintaining the natural topology structure of the human skeleton. The STT is devised to acquire action representations containing the global relationships among joints. Since the action representations produced from these two streams contain different characteristics, and each of them knows little information of the other, we introduce the contrastive learning paradigm to guide their output representations of the same sample to be as close as possible in a self-supervised manner. Through the contrastive learning, they can learn information from each other to enrich the action features by maximizing the mutual information between the two types of action representations. To further improve action recognition accuracy, we introduce the Cyclical Focal Loss (CFL) which can focus on confident training samples in early training epochs, with an increasing focus on hard samples during the middle epochs. We conduct experiments on three benchmark datasets, which demonstrate that our model achieves state-of-the-art performance in action recognition.
Collaborative Multi-dynamic Pattern Modeling for Human Motion Prediction

The dynamic information of the joints, such as the movement amplitude, is critical for forecasting precise human joint trajectories. Existing methods adopt global modeling in which all joints are treated as a whole to extract features for movement coordination. Though global modeling can exploit hidden relationships between joints, it also inevitably introduces undesired trajectory dependencies, which weakens the dynamic information effects and simplifies the constraints and kinetics model of joints. Therefore, we propose a dynamic pattern-based collaborative modeling framework (DPnet) that contains a keyframe enhanced module (KEM) and multi-channel feature extractor blocks (MFE-block). The KEM tackles the discontinuity between the last frame of observation and the first predicted one by duplicating the decisive frame. The MFE-block utilizes a multi-channel graph structure to enrich the dynamic information effects and recessive constraints of joints. To distinguish the dynamic information of each joint, we calculate the movement amplitude of the joints and propose three dynamic patterns, including active, inactive, and static patterns. We also propose a dynamic pattern-guided feature extractor (DP-FE) to alleviate the trajectory dependencies between joints with different dynamic patterns. We evaluate our approach on three standard benchmark datasets, including H3.6M [8], CMU-Mocap [44], and 3DPW [45]. Our approach achieves impressive results in both short-term and long-term predictions, confirming its effectiveness and efficiency.
Reed Solomon Codes Against Adversarial Insertions and Deletions

In this work, we study the performance of Reed–Solomon codes against adversarial insertion-deletion (insdel) errors. We prove that over fields of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n^{O(k)}$ </tex-math></inline-formula> there are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> Reed-Solomon codes that can decode from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n-2k+1$ </tex-math></inline-formula> insdel errors and hence attain the half-Singleton bound. We also give a deterministic construction of such codes over much larger fields (of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n^{k^{O(k)}}$ </tex-math></inline-formula> ). Nevertheless, for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=O(\log n /\log \log n)$ </tex-math></inline-formula> our construction runs in polynomial time. For the special case <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=2$ </tex-math></inline-formula> , which received a lot of attention in the literature, we construct an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n], [2]$ </tex-math></inline-formula> Reed-Solomon code over a field of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{4})$ </tex-math></inline-formula> that can decode from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n-3$ </tex-math></inline-formula> insdel errors. Earlier constructions required an exponential field size. Lastly, we prove that any such construction requires a field of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega (n^{3})$ </tex-math></inline-formula> .
Comparative Study of Deep Learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages

Phishing webpages are a type of cyber-attack whose objective is to try to deceive people through fraudulent web pages to harm its victims, commonly in an economical way. Every day new fraudulent web pages are created, so one of the standard methods to detect these recent attacks is the use of Artificial Intelligence algorithms based on the HTML content of a web page. Therefore, this work aims to perform a comparative analysis between Deep Learning algorithms to know if it is more effective to detect an attack, either using HTML code or the text obtained from this code. The content of a large text Dataset was obtained using Web Scraping techniques. Then the Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Recurrent Convolutional Neural Network (RCNN) algorithms were executed, feeding them first with HTML code and then with text. The average of the metrics obtained with HTML was 85%, and the overall metrics obtained for text averaged 84%. In conclusion, it is determined with this study that it makes no difference whether the algorithm is fed with HTML or text because when analyzing with text, the unnecessary features of HTML are eliminated, but simultaneously, the essential elements of HTML are also lost.
Modeling, Control and Implementation of Adaptive Reconfigurable ROtary Wings (ARROWs)

Inspired by the self-rotating motion of the descending maple seeds, we introduce a novel modular aerial robotic platform—ARROWs. With customized wing and control modules, ARROWs can be easily reassembled into different configurations. Unlike conventional multirotor aerial vehicles which rely on the direct thrust from propellers, ARROWs can generate more lift by their revolving wings. However, the complex dynamics causes difficulties in flight controller development. In this work, we first analyze the flight dynamics by considering a combination of effects from the propeller, aerodynamic force on the wing, as well as self-rotating motion. As a result, a cascaded flight controller with a unified framework is designed based on reduced flight dynamics and relaxed hovering conditions to achieve stable flights in all proposed configurations. In addition, a set of inertial measurement units is employed for each flight module to estimate the flight configuration to overcome the dynamic uncertainties caused by manual re-construction. Finally, our proposed platform and flight control strategy are validated using several flight experiments in 12 different configurations (include both centrosymmetric and centrally asymmetric cases). The results show an average position error of 8.9 cm with a deviation of 2.4 cm among all configurations in hovering tests.
Sentiment analysis of Indonesian police chief using multi-level ensemble model

Sentiment analysis is a technique of analyzing text to classify its emotion into positive, negative, or neutral sentiments. The main purpose of this study is to use sentiment analysis to seek Indonesian opinions about the chief of the Indonesian National Police. The dataset obtained from Twitter contains opinions toward the police chief from 20th August until 30th August 2022. Sentiment analysis can be done by using machine learning. Unfortunately, each machine learning algorithm performs differently depending on the data. This performance led to the issue of confusion in picking the best machine learning model to perform sentiment analysis, especially for unseen data. Therefore, ensemble learning is needed to combine multiple machine learning models. Bagging-based and Boosting-based ensemble learning have different algorithms. In order to get a high-performance model, Bagging-based and Boosting-based ensemble models can be combined as one model. This work proposed a second-level ensemble model called a multi-level ensemble model for sentiment analysis. It combines Multinomial Naive Bayes, Boosting-based ensemble learning, and Bagging-based ensemble learning. The data is processed using CountVectorizer to convert text into numbers and oversampling to handle imbalanced data. The experimental results show that our proposed model has the best results in terms of accuracy, precision, and F1-score for testing set compared to individual models.
ERNet: An Efficient and Reliable Human-Object Interaction Detection Network

Human-Object Interaction (HOI) detection recognizes how persons interact with objects, which is advantageous in autonomous systems such as self-driving vehicles and collaborative robots. However, current HOI detectors are often plagued by model inefficiency and unreliability when making a prediction, which consequently limits its potential for real-world scenarios. In this paper, we address these challenges by proposing ERNet, an end-to-end trainable convolutional-transformer network for HOI detection. The proposed model employs an efficient multi-scale deformable attention to effectively capture vital HOI features. We also put forward a novel detection attention module to adaptively generate semantically rich instance and interaction tokens. These tokens undergo pre-emptive detections to produce initial region and vector proposals that also serve as queries which enhances the feature refinement process in the transformer decoders. Several impactful enhancements are also applied to improve the HOI representation learning. Additionally, we utilize a predictive uncertainty estimation framework in the instance and interaction classification heads to quantify the uncertainty behind each prediction. By doing so, we can accurately and reliably predict HOIs even under challenging scenarios. Experiment results on the HICO-Det, V-COCO, and HOI-A datasets demonstrate that the proposed model achieves state-of-the-art performance in detection accuracy and training efficiency. Codes are publicly available at https://github.com/Monash-CyPhi-AI-Research-Lab/ernet.