Reviewerly/exHarmony huggingface dataset:
Multi-sensor remote sensing image alignment based on fast algorithms

Abstract Remote sensing image technology to the ground has important guiding significance in disaster assessment and emergency rescue deployment. In order to realize the fast automatic registration of multi-sensor remote sensing images, the remote sensing image block registration idea is introduced, and the image reconstruction is processed by using the conjugate gradient descent (CGD) method. The scale-invariant feature transformation (SIFT) algorithm is improved and optimized by combining the function-fitting method. By this way, it can improve the registration accuracy and efficiency of multi-sensor remote sensing images. The results show that the average peak signal-to-noise ratio of the image processed by the CGD method is 25.428. The average root mean square value is 17.442. The average image processing time is 6.093 s. These indicators are better than the passive filter algorithm and the gradient descent method. The average accuracy of image registration of the improved SIFT registration method is 96.37%, and the average image registration time is 2.14 s. These indicators are significantly better than the traditional SIFT algorithm and speeded-up robust features algorithm. It is proved that the improved SIFT registration method can effectively improve the accuracy and operation efficiency of multi-sensor remote sensing image registration methods. The improved SIFT registration method effectively solves the problems of low accuracy and long time consumption of traditional multi-sensor remote sensing image fast registration methods. While maintaining high registration accuracy, it improves the image registration speed and provides technical support for a rapid disaster assessment after major disasters such as earthquakes and floods. And it has an important value for the development of the efficient post-disaster rescue deployment.
Smart Sensing Technologies in Sports Science

The sensing technology is the basis of all kinds of data information acquisition. It is the premise of most information collection and analysis in computational sports science, such as sports training, sports physiology, sports biomechanics, and sports medicine. Smart sensing technologies use novel sensing technologies and machine learning to measure and recognize physiological behavior, including heart rate, pulse, breath, movement, force, joint bending angle, body temperature, sweat, energy consumption, etc. In recent years, various technologies have been integrated with sensing technologies, such as intelligent materials, embedded technology, visualization technology, and artificial intelligence technology. In this paper, several smart sensing technologies developed by our research team are introduced, which may provide promising paths for sports science, rehabilitation medicine, human-computer interaction, etc.
Local Standpoint and Human-Centred Design for Local Health: A Methodological Approach

This paper engages in design research within the Indigenous Dayak community in East Kalimantan, focusing on addressing local health discourse. It focuses on methodological questions to adopt more sensitive and ethical research methods when working with local people. In doing so, this paper encompasses three key aspects: (1) examining transdisciplinary approaches from an Indigenous standpoint and human-centred design, exploring their intersections; (2) contextualizing the research within the local Indonesian community; and (3) proposing a set of methods to examine Indigenous Dayak health within the chosen approaches and context. This paper suggests adapting ethnographic sensibility to blend local and design vocabularies, attempting to create a space for discourse for setting up the design research and practice within this inquiry.
An Overview of the Effects of Technology on Education: Teaching and Learning

This study seeks to understand the effects of technology on education: teaching and learning. All the data used in this study comes from secondary sources. Information was gathered from several sources, including peer-reviewed journal papers, published and unpublished dissertations, online and print textbooks, and encyclopaedias. Findings: Evidence on the effects of technology is contradictory, according to a review of the relevant literature. Thanks to information technology developments, classroom collaboration between teachers and students is more efficient than ever. It broadens their academic and intellectual horizons, making students more versatile in the classroom. Due to the internet's accessibility, remote learning may now be supported by technological means. Students can use search engines to conduct research. Hardware failures, software incompatibilities between home and school, sluggish or non-existent internet connections, and outdated program versions are just some of the dependability problems that students and teachers face in the classroom. Because of developments in technology like graphical calculators, high-tech gadgets, and small cameras, dishonest students may now easily gain an edge over their classmates. Distractions in modern life may be traced back in large part to technological advancements. Students' health might be negatively impacted by excessive technology usage.
Determining Signal Latency in Correlated Series

This paper proposes a method of determining speed of signal/information transfer via unique specifications of transfer entropy. The unique specifications are demonstrated on simulated data and compared to another common specification to show superiority in estimating true latency.
Transfer Learning for Region-Wide Trajectory Outlier Detection

Trajectory outlier detection is a crucial task in trajectory data mining and has received significant attention. However, the distribution of trajectories is tied to social activities, resulting in extreme unevenness among regions. While existing methods have demonstrated excellent performance in regions with sufficient historical trajectories, they frequently struggle to detect outliers in regions with limited trajectories. Unfortunately, this issue has not received much attention, leaving a gap in the current understanding of trajectory mining. To deal with this problem, we in this paper propose a model called TTOD that can effectively detect outliers in regions with sparse data by transferring knowledge among regions. The main idea is to learn a feature mapping function that maps the global feature space of auxiliary regions to the target region’s specific feature space. To achieve this, we adopt a VAE-based model called the Global VAE to learn the global feature space in auxiliary regions by modeling the trajectory patterns with Gaussian distributions. Then, we propose a Specific-region VAE that serves as the mapping function to learn the target feature space. Additionally, considering the data drift of feature distributions among regions, we introduced an additional pattern synthesis layer, named the De-drift Layer, to diversify the target feature space, thus addressing the pattern missing issue caused by the gap of feature distributions between the auxiliary regions and the target regions. Then the target feature space can be well studied and applied to detect outliers. Finally, we conduct extensive experiments on two real taxi trajectory datasets and the results show that TTOD achieves state-of-the-art performance compared with the baselines.
An Efficient Smart Contract Vulnerability Detector Based on Semantic Contract Graphs Using Approximate Graph Matching

The Internet of Things (IoT) has become a focus of information infrastructure development in recent years. The smart blockchain can provide various solutions for trust, security, and privacy (TSP) challenges to protect IoT data, and smart contracts are the foundation of blockchain intelligence, and greatly enhance the ability of smart blockchain to solve TSP problems. So the security of smart contracts must be addressed. We propose an efficient smart contract vulnerability detector to improve the safety of smart contracts. It comprises a graph extraction method and a complete vulnerability detection process. The graph extraction method consists of vulnerability pattern extraction and a graph generation process. The vulnerability detection process first uses the approximate graph matching algorithm to select representative SCGraphs from the dataset to build vulnerability SCGraph libraries. Secondly, determine whether the contract contains vulnerabilities by calculating the similarity between the SCGraphs generated from the contracts to be detected and the SCGraphs in the vulnerability library. Experiments show that our approach achieves an inspiring high detection rate and is the fastest among existing vulnerability detection tools, which indicates that it can provide good vulnerability detection for smart contracts.
FedUR: Federated Learning Optimization Through Adaptive Centralized Learning Optimizers

Introducing adaptiveness to federated learning has recently ushered in a new way to optimize its convergence performance. However, adaptive learning strategies originally designed in centralized machine learning are in naїve extended to federated learning in existing works, which does not necessarily improve convergence performance and further reduce communication overhead as expected. In this paper, we fully investigate those centralized learning-based adaptive learning strategies, and propose an adaptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fed</u> erated learning algorithm targeting the model parameter <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</u> pdate <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> ule, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedUR</i> . Convergence upper bounds under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedUR</i> are derived from the aspect of both local iterations and global aggregations. Through comparison with the convergence upper bounds of original federated learning, we theoretically analyze how those strategies should be tuned to help federated learning effectively optimize convergence performance and reduce overall communication overhead. Extensive experiments are conducted based on several real datasets and machine learning models, which show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedUR</i> can effectively increase final convergence accuracy with even lower communication overhead requirement.
Facial Expression Transfer Based on Conditional Generative Adversarial Networks

With the development of computer vision and image transfer, facial expression transfer has been more and more widespread applications. But there are still some problems, such as lack of realistic expression, poor retention of facial identity features and low synthesis efficiency. In order to solve the problems of facial expression transfer, the paper proposes a facial expression transfer model based on conditional generative adversarial network, which can generate a highly realistic face image with source facial expression and target facial identity features, when gave a source face image and a target face image. The model consists of two parts: the facial feature point fusion module and the expression transfer module. Among them, the facial feature point fusion module uses an auto-encoder to encode the face key feature point image of the source facial expression and the face feature key point image of the target face, so as to transfer the source facial expression information to the corresponding face key feature points of the target image; the expression transfer module uses the facial feature point fusion module to generate the face key feature point image and the target face image, and then generates an image with the source facial expression and the target face identity features through the modified U-net network. The model is finally validated on two publicly available datasets, RaFD and CK+, and the experimental results show that the generated facial expression is more realistic than the pix2pix model, and the model only needs to be trained once to complete the transfer between any facial expression.
LMRank: Utilizing Pre-Trained Language Models and Dependency Parsing for Keyphrase Extraction

Keyphrase extraction is a Natural Language Processing task pertaining to the automatic extraction of salient terms that semantically encapsulate the major theme and topics of a document. In this article, we present LMRank, a novel approach that utilizes dependency parsing and the sentence embeddings of pre-trained language models to improve the accuracy of the keyphrase extraction task. In addition, we conduct a benchmark analysis of our approach, which showcases that it scales far better than similar ones in terms of execution time. The contribution of this work is threefold: (i) we propose a novel approach that significantly outperforms the state-of-the-art keyphrase extraction approaches in terms of time performance and accuracy in selected datasets; (ii) we provide a comparative evaluation of our approach against previous ones, by utilizing broadly used datasets in the literature and established evaluation metrics (e.g., the F1 and pF1 scores); (iii) we make the datasets and code used in our experiments public, aiming to further increase the reproducibility of this work and facilitate future research in the field.
Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification

The use of text data with high dimensionality affects classifier performance. Therefore, efficient feature selection (FS) is necessary to reduce dimensionality. In text classification challenges, FS algorithms based on a ranking approach are employed to improve the classification performance. To rank terms, most feature ranking algorithms, such as the Relative Discrimination Criterion (RDC) and Improved Relative Discrimination Criterion (IRDC), use document frequency (DF) and term frequency (TF). TF accepts the actual values of a term with frequently and rarely occurring terms used in existing feature ranking algorithms. However, these algorithms focus on the number of terms in a document rather than the number of terms in the category. In this research, an alternative method to RDC, called Alternative Relative Discrimination Criterion (ARDC) was proposed, which aims to improve the accuracy and effectiveness of RDC feature ranking. Specifically, ARDC is designed to identify terms commonly occurring in the positive class. The results obtained were compared to the existing RDC methods, which are RDC and IRDC, and standard benchmarking functions such as Information Gain (IG), Pearson Correlation Coefficient (PCC), and ReliefF. The experimental results reveal that using the suggested ARDC on the Reuters21578, 20newsgroup, and TDT2 datasets provides better performance in terms of precision, recall, f-measure, and accuracy when employing well-known classifiers such as multinomial naïve Bayes (MNB), Support Vector Machine (SVM), Multilayer perceptron (MLP), k-nearest neighbor (KNN), and decision tree (DT). Another experiment was performed to validate the proposed technique, which aims to showcase the novelty of the ARDC approach. The experiment utilized the 20newsgroup dataset and employed the Relevant-Based Feature Ranking (RBFR) technique. Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) classifiers were used in this experiment to demonstrate the effectiveness of the suggested ARDC.
Controlling Information Flow in Arrays of Spin-Torque Oscillators

Arrays of spin-torque oscillators (STOs) can be used in artificial neural networks. The STOs in such arrays can be synchronized via magnetostatic interactions. In this work, we show that variations in the STO diameter can be used to control the flow of information. Unidirectional information flows can be enabled by a consistent change in diameter along a row of STOs. A switch to direct the information flow can also be constructed using STOs with different diameters.
STEAM Project-Based Learning Approach to Enhance Teaching-Learning Process in the Topic of Pascal’s Principle

With the advancements in information and communications technologies, new teaching approaches arise. In this context, project-based learning (PBL) and science, technology, engineering, the arts, and mathematics model (STEAM) emerge as the most popular in the education field, attributed to their efficacy on students’ learning capacity. This article contributes with a new teaching–learning approach that combines the STEAM model and PBL (called STEAM-PBL) to address the student’s difficulties that may present in the topic of Pascal’s principle. The proposed approach is validated following an experimental design considering control and experimental groups. The metrics used to measure academic performance are the Dellwo index, while the Rasch model is used to quantify the effectiveness of the evaluation instrument. In addition, the proposed approach’s acceptance is quantified through Cronbach’s Alpha factor. The results reveal that STEAM-PBL has the potential to enhance the teaching–learning process while keeping the student motivated during the lectures.
Group-of-Picture Mode Acceleration for Efficient Object Detection in Video Streams

Although modern object detection AI models have the potential to be widely used in various applications such as autonomous vehicles, these models are very computationally demanding.Using highresolution image data further increases the computational burden.Hence, we propose an acceleration method called Group of Picture (GoP) mode for object detection in video sequences by removing the temporal redundancy, unlike the existing model compression schemes.A GoP structure is composed of only one key frame and several non-key frames.In GoP-mode, object detection is adopted for key frames only, while object tracking is employed to predict the position of each object in the following non-key frames based on the tracked trajectory and momentum of each object.By using the proposed method, the thrilling latency saving can result in multiple times of execution speed acceleration so that both high detection accuracy and high execution speed can be obtained.In theory, if we adopt a GoP structure of one key frame with N non-key frames, the execution speed of object detection is accelerated to (1+N) times by equipping the GoP-mode.The effect of the number of non-key frames on the accuracy variation of an object detector equipped with GoP-mode has been analyzed.According to the experimental results, the mean average precision (mAP) of adopting GoP mode with four non-key frames in one GoP structure is competitive to that using object detection for all frames.Meanwhile, the execution frame rate is increased from the original 8 frames per second (FPS) to 35.8 FPS on the mobile platform-Jetson Nano, i.e. a speedup of 348%.
An Improved Point-Level Supervision Method for Temporal Action Localization

Recently, with the expansion of the video platform market, research has been actively conducted on temporal action localization (TAL) for detecting actions in atypical videos. Most learning methods for TAL include full and weak supervision (weak supervision with only action classes) approaches. Full supervision requires considerable time for labeling and weak supervision exhibits low localization performance owing to the lack of informative annotations. To solve this problem, point-level weak supervision using single-point timestamps within the temporal interval of action instances has been proposed, which demonstrates superior performance to weakly-supervised methods using only action classes of action instances. In this study, we proposed an improved point-level supervision mechanism that provides point-level annotations for each action and background instance. In addition, a widely used multiple instance learning (MIL)-based framework was used to verify the proposed method, and pseudo-labels were used for action instance boundary learning. Also, the background point loss was designed to leverage the added point-level annotations. The datasets used in the experiment were THUMOS14, GTEA, BEOID, and ActivityNet1.2, and improved results were obtained compared to existing point-level supervision. The code is available from https://github.com/sang9390/An-Improved-Point-Level-Supervision-Method-for-TAL.
Obstacle Avoidance Behavior Combining Dodging Control and Movable Soft Material

In recent years, robots have been spreading into our living spaces. In everyday life, humans and robots are often in the same space. In such spaces, robots are sometimes subjected to physical impacts from various objects. If the robot collides with a human, the human will be injured. If the robot collides with a hard object, the robot will be damaged. For these reasons, it is important to avoid physical shocks from the outside and to reduce the shocks to a level where they do not become a problem in the event of a collision. In this letter, we focus on obstacle avoidance behavior for safe human–robot interaction and aim to mitigate the impact by combining dodging control and movable soft material. As a result of comparative experiments, it was confirmed that the proposed approach could mitigate the force and reduce the impact.
Deep Learning Based Pilot-Free Transmission: Error Correction Coding for Low-Resolution Reception Under Time-Varying Channels

Recently, deep learning aided methods have been developed for error correction coding with quantitative constraints. However, previous studies only focus on additive white Gaussian noise (AWGN) channels, which is not sufficient for actual communication environments. In this paper, we propose a novel autoencoder aided error correction coding scheme for low-resolution reception under time-varying channels. Based on the symbol extension of the proposed autoencoder and the faster-than-Nyquist (FTN) technology, pilot-free transmission can be realized without adding additional bandwidth. The transformer block is introduced to lighten and improve the decoder. Additionally, two kinds of preamplification techniques are applied for further performance boosting. Simulations show that the proposed method can achieve better performance compared with the traditional methods at high signal-to-noise ratio (SNR) under different time-varying channels without quantization. Moreover, it outperforms the previous state-of-the-art ECCNet and can achieve remarkable transmission performance even under time-varying low-resolution reception scenarios.