Reviewerly/exHarmony huggingface dataset:
A Re-detection Target Tracking Framework after Loss for Unmanned Aerial Vehicle

Abstract Currently, the target tracking algorithm based on discriminative correlation filter (DCF) and deep learning (DL) plays an increasingly important role in UAV (unmanned aerial vehicle). However, existing algorithms have limitations such as limited search region, difficulty in re-capturing targets when tracking is lost, and high computational complexity, which makes them difficult to apply to the UAV platform. In this paper, a target re-detection tracking algorithm (RDT) after tracking loss is designed for mobile platforms. RDT uses DCF in simple scenes to track targets and establish a target model between different frames. To ensure the algorithm when tracking is lost due to occlusion or moving out of view, an efficient switching criterion is designed to indirectly invoke the detection algorithm to re-capture the target and guide online learning of DCF. And the experiment results on the OTB benchmark show that RDT can re-capture the target after tracking is lost, and the speed of operation on the CPU is 85fps.
Employing Source Code Quality Analytics for Enriching Code Snippets Data

The availability of code snippets in online repositories like GitHub has led to an uptick in code reuse, this way further supporting an open-source component-based development paradigm. The likelihood of code reuse rises when the code components or snippets are of high quality, especially in terms of readability, making their integration and upkeep simpler. Toward this direction, we have developed a dataset of code snippets that takes into account both the functional and the quality characteristics of the snippets. The dataset is based on the CodeSearchNet corpus and comprises additional information, including static analysis metrics, code violations, readability assessments, and source code similarity metrics. Thus, using this dataset, both software researchers and practitioners can conveniently find and employ code snippets that satisfy diverse functional needs while also demonstrating excellent readability and maintainability.
Tibetan text classification based on graph convolutional network

Abstract In this study of Tibetan text classification based on graph convolutional networks, the text graph is first constructed for the Tibetan news corpus based on word-word relations and word-document relations using the constructive relations of Tibetan words and documents, then initialized using the unique thermal representations of words and documents. The embeddings of words and documents are learned using graph convolutional networks under the supervision of the training set of documents, and finally, the text classification problem is transformed into a node classification problem. The graph convolutional network achieves an accuracy of 68.45 % on the Tibetan text classification task, which is higher than other baseline models. In this paper, two comparative experiments are designed to illustrate the effectiveness of graph convolutional networks on Tibetan text classification, which helps people to solve the problem of confusing information in Tibetan texts.
Spatial-temporal regularized UAV correlation filter based on adaptive filter template

Abstract Due to its quick computation, the correlation filter (CF) is frequently employed in object-tracking activities. The occlusion and deformation of the tracking object during the CF tracking process will pollute the filter, which will cause the filter’s performance to degrade. This is especially obvious in unmanned aerial vehicle (UAV) tracking and increased interfered sequences will further aggravate the problem. In response to this problem, we use ARCF as the baseline and add a spatial-temporal regularization term based on the adaptive template. We also introduce a historical filter template to generate a more global adaptive template. The experimental results illustrate that our tracker has the most sophisticated capabilities compared to the other 8 SOTA trackers on the DTB70 benchmark.
Enhancing the Security and Privacy in the IoT Supply Chain Using Blockchain and Federated Learning with Trusted Execution Environment

Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring the security and tamper resistance of the data upload and aggregation process. Finally, each cluster can obtain the aggregated parameters from the blockchain. To evaluate the performance of our proposed framework, we conducted several experiments with different numbers of participants and different datasets and validated the effectiveness of our scheme. We tested the final global model obtained from federated training on a test dataset and found that increasing both the number of iterations and the number of participants improves its accuracy. For instance, it reaches 94% accuracy with one participant and five iterations and 98.5% accuracy with ten participants and thirty iterations.
Detection of Oriented Scene Text in Natural Images

Abstract Recently, in the field of computer vision, people are paying more attention to extracting and understanding text information in natural scenes, which are widely used in real life, such as image OCR, image retrieval, and multi-language translation. Text detection plays a vital role in the process of text information extraction and understanding. Diverse text patterns and highly cluttered backgrounds are the main challenges of precise text detection. This paper studies oriented scene text detection, the horizontal scene text detector, and CTPN network architecture, which is improved and extended for oriental scene text detection. The main proposed improvements include: 1. The pre-processing method and parameter setting in the post-processing are improved to make the annotation more accurate; 2. The anchor mechanism is improved by setting a series of different anchor widths depending on the anchor heights instead of the fixed width. The improved model was trained with the training set of ICDAR2015 and part of ICDAR2017 MLT. Then it was tested on the ICDAR online platform with test sets of ICDAR2013 and ICDAR2015. Finally, it was tested with some images outside the datasets. The results show that the improved model can detect text with a tilt angle more precisely.
Accuracy in Object Detection by Using Deep Learning Method

Accuracy and Precision measurement of an object in a complex environment for mobile robot applications is an important task. In a complex environment it is difficult to identify similar objects, such as chairs, tables, furniture and other objects available. Different methods are available to identify such objects, but high accuracy and precision are not easily achievable. This paper presents MobileNet with Vision Transformer for identification of objects in complex environments. Several experiments have been conducted for indoor environments where 94.3% accuracy is achieved for indoor environment with the implementation of MobileNet with Vision Transformer model.
Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report

Artificial intelligence is gaining interest among clinicians, but its results are difficult to be interpreted, especially when dealing with survival outcomes and censored observations. Explainable machine learning (XAI) has been recently extended to this context to improve explainability, interpretability and transparency for modeling results. A cohort of 231 patients undergoing an allogeneic bone marrow transplantation was analyzed by XAI for survival by two different uni- and multi-variate survival models, proportional hazard regression and random survival forest, having as the main outcome the overall survival (OS) and its main determinants, using the survex package for R. Both models’ performances were investigated using the integrated Brier score, the integrated Cumulative/Dynamic AUC and the concordance C-index. Global explanation for the whole cohort was performed using the time-dependent variable importance and the partial dependence survival plot. The local explanation for each single patient was obtained via the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile. The survex package common interface ensured a good feasibility of XAI for survival, and the advanced graphical options allowed us to easily explore, explain and compare OS results coming from the two survival models. Before the modeling results to be suitable for clinical use, understandability, clinical relevance and computational efficiency were the most important criteria ensured by this XAI for survival approach, in adherence to clinical XAI guidelines.
PENENTUAN JURUSAN PADA SMKN 1 MUARO JAMBI DENGAN METODE K-MEANS CLUSTERING

This research is motivated by the lack of structure in determining the majors of class X students at SMKN 1 Muaro Jambi, because in the current majors they still use the manual system, namely written exams, this makes it difficult for students to determine majors according to their abilities. The problem in this study is how to apply data mining with the K-Means method in the application of student majors at SMKN 1 Muaro Jambi. How to design an application program for determining student majors at SMKN 1 Muaro Jambi using the K-Means method. This study uses the K-Means Clustering method to obtain majors that will make it easier for teachers or schools to determine student majors. K-means Clustering non-hierarchical method is a simple and effective algorithm to find groupings in the data in the majors process that is used is grade data and student data in the 2022/2023 entry year. The conclusion of this research is to produce a majors determination system using the K-Means Clustering method according to the majors criteria. Generating student majors from calculations using the K-means Clustering method.
Optimized control for high-fidelity state transmission in open systems

Quantum state transfer through spin chains has been extensively investigated. Two schemes, the coupling set for perfect state transfer (PST) or adding a leakage elimination operator (LEO) Hamiltonian have been proposed to boost the transmission fidelity. However, these ideal schemes are only suitable for closed systems and will lose their effectiveness in open ones. In this work, we invoke a well-explored optimization algorithm, Adam, to expand the applicable range of PST couplings and LEO to the open systems. Our results show that, although the transmission fidelity decreases with increasing system-bath coupling strength, Markovianity and temperature for both ideal and optimized cases, the fidelities obtained by the optimized schemes always outweigh the ideal cases. The enhancement becomes bigger for a stronger bath, indicating a stronger bath provides more space for the Adam to optimize. This method will be useful for the realization of high-fidelity information transfer in the presence of environment.
Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer

The utilization of unmanned systems has witnessed a steady surge in popularity owing to its tremendous potential for a wide range of applications. In particular, the coordination among multiple vehicle systems has been demonstrated to possess unparalleled efficacy in accomplishing intricate and diverse tasks. In light of this, the present paper delves into the coordinated path planning mission that is accomplished by collaborative efforts amongst multiple Autonomous Underwater Vehicles (AUVs). First, considering the potential threats, arrival time windows, space, and physical constraints for the AUVs, a sophisticated coordinated path planning model is formulated in a 3D environment, serving as a systematic and structured blueprint for the underlying mechanism. Subsequently, the optimization problem is addressed through the incorporation of a restricted initialization scheme and a multi-objective clustering strategy in the proposed methodology. The resulting approach leads to the development of the Parallel Grey Wolf Optimizer (P-GWO) which exhibits strong global searching abilities and a rapid convergence rate, rendering it a dependable and effective solution. The results demonstrate a 10–15% improvement in convergence rate and a reduction of over 60% in the average cost value compared to reliable references, thus presenting an effective solution for underwater missions with specific requirements.
A Gap Analysis of the Adoption Maturity of Certificateless Cryptography in Cooperative Intelligent Transportation Systems

Cooperative Intelligent Transport Systems (C-ITSs) are an important development for society. C-ITSs enhance road safety, improve traffic efficiency, and promote sustainable transportation through interconnected and intelligent communication between vehicles, infrastructure, and traffic-management systems. Many real-world implementations still consider traditional Public Key Infrastructures (PKI) as the underlying trust model and security control. However, there are challenges with the PKI-based security control from a scalability and revocation perspective. Lately, certificateless cryptography has gained research attention, also in conjunction with C-ITSs, making it a new type of security control to be considered. In this study, we use certificateless cryptography as a candidate to investigate factors affecting decisions (not) to adopt new types of security controls, and study its current gaps, key challenges and possible enablers which can influence the industry. We provide a qualitative study with industry specialists in C-ITSs, combined with a literature analysis of the current state of research in certificateless cryptographic in C-ITS. It was found that only 53% of the current certificateless cryptography literature for C-ITSs in 2022–2023 provide laboratory testing of the protocols, and 0% have testing in real-world settings. However, the trend of research output in the field has been increasing linearly since 2016 with more than eight times as many articles in 2022 compared to 2016. Based on our analysis, using a five-phased Innovation-Decision Model, we found that key reasons affecting adoption are: availability of proof-of-concepts, knowledge beyond current best practices, and a strong buy-in from both stakeholders and standardization bodies.
Exploring the Potential of Mixed Fourier Series in Signal Processing Applications Using One-Dimensional Smooth Closed-Form Functions with Compact Support: A Comprehensive Tutorial

This paper studies and analyzes the approximation of one-dimensional smooth closed-form functions with compact support using a mixed Fourier series (i.e., a combination of partial Fourier series and other forms of partial series). To explore the potential of this approach, we discuss and revise its application in signal processing, especially because it allows us to control the decreasing rate of Fourier coefficients and avoids the Gibbs phenomenon. Therefore, this method improves the signal processing performance in a wide range of scenarios, such as function approximation, interpolation, increased convergence with quasi-spectral accuracy using the time domain or the frequency domain, numerical integration, and solutions of inverse problems such as ordinary differential equations. Moreover, the paper provides comprehensive examples of one-dimensional problems to showcase the advantages of this approach.
Perubahan Pemahaman Mahasiswa Program Studi Pendidikan Bahasa Jepang Terhadap Rencana Pelaksanaan Pembelajaran Pasca Pelaksanaan Plp

This study aims to determine the understanding of Japanese Language Education Study Program students' understanding of the learning device components in the form of learning implementation plans before and after the implementation of the school field introduction and to understand the learning device components in the form of learning implementation plans after the implementation of the school field introduction. The type of research used is descriptive research with a qualitative approach. The data obtained based on the research are primary data and secondary data with data acquisition in the form of interviews and documentation. The test subjects were Japanese language study program students who had carried out an introduction to the school field. The results of the study were obtained in the implementation of the school field introduction, the data source was able to determine the components in the learning implementation plan that adapted to the character of the students and the conditions of the class and school where the data source carried out the school field introduction and there were changes in the allocation of time, determining learning objectives, and determining methods learning. Thus it can be concluded that students of the Japanese Language Study Program have received a change in understanding in the components of making a lesson plan after attending an introduction to the school field.
PERANCANGAN SISTEM ELEKTRONIK VOTING PENENTUAN PENGURUS OSIS MENGGUNAKAN PHP NATIVE (STUDI KASUS : SMK N 4 BATANGHARI)

In the world of education, information technology plays a very important role in supporting the teaching and learning process and supporting activities in schools, but not all activity processes have been accommodated by technological developments. The election system for the chairman and vice chairman of the OSIS at SMK Negeri 4 Batanghari still uses the conventional method. The voting process that is carried out is by ticking or punching on the ballot paper as a way of selecting candidates for the chairman and vice chairman of the OSIS. To overcome this, an application was built to carry out web-based voting or what is called e-voting (electronic voting). The evoting system is considered more effective and efficient in facilitating the vote counting process and the results of the election can be known quickly and accurately. In the process of making and designing an e-voting information system for the election of chairman and vice chairman of student council at SMK Negeri 4 Batanghari using the PHP programming language and using the MySql database, the author carried out system development with design analysis, system design, data flow diagrams.
MEDIA PEMBELAJARAN INTERAKTIF

State Elementary School 94/IV is a public education school located in Jambi City, Indonesia. State Elementary School 94/IV Jambi City is an educational institution operated or provided by the government. The learning that has been implemented is based on the Learning Implementation Plan (RPP) and the syllabus that has been implemented according to the K13 guidelines (2013 curriculum) and has 6 classrooms. However, Elementary School 94/IV has not implemented a teaching system using learning videos or other interactive learning media in order to attract students' attention and focus in the teaching-teaching process. With the analysis of these problems, the researchers tried to design an interactive learning media system in the form of animation using Adobe Flash CS6 with the aim of attracting attention, focus and motivation for students' interest in learning Mathematics. With interactive learning media methods students can optimize understanding in learning Mathematics so that the teaching-teaching process becomes effective and efficient.
Learning an anchor-free network with angle attention for remote sensing object detection

ABSTRACTRotation object detection has achieved significance progress in the community of remote sensing image processing. In contrast to horizontal bounding boxes, rotating bounding boxes require an accurate angle parameter to position the object. However, angle regression accuracy remains an urgent challenge due to the arbitrary orientation, large aspect ratio, and dense alignment of aerial images. To solve this problem, we propose an anchor-free network with angle attention (FAA-Net), which delicately develops two core designs, i.e, angle feature extraction (AFE) module and angle prediction head (APH) module. Specifically, the AFE module performs feature extraction and angle selection to reduce the negative effects of angle adaptation. In addition, APH module is further employed to better predict the object orientation by introducing the frequency attention and integral operation. Extensive experiments on two classical benchmark datasets have demonstrated that our method can effectively boost detection performance against several start-of-the-art approaches.KEYWORDS: Oriented object detectionanchor-free detectorangle attentionremote sensing images Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Liaoning Provincial Applied Basic Research Project under Grant 2022JH2/101300247.
Effective Excess Noise Suppression in Continuous-Variable Quantum Key Distribution through Carrier Frequency Switching

Continuous-variable quantum key distribution (CV-QKD) is a promising protocol that can be easily integrated with classical optical communication systems. However, in the case of quantum-classical co-transmissions, such as dense wavelength division multiplexing with classical channels and time division multiplexing with large-power classical signal, a quantum signal is more susceptible to crosstalk caused by a classical signal, leading to signal distortion and key distribution performance reduction. To address this issue, we propose a noise-suppression scheme based on carrier frequency switching (CFS) that can effectively mitigate the influence of large-power random noise on the weak coherent state. In this noise-suppression scheme, a minimum-value window of the channel's noise power spectrum is searched for and the transmission signal frequency spectrum shifts to the corresponding frequency to avoid large-power channel noise. A digital filter is also utilized to filter out most of the channel noise. Simulation results show that compared to the traditional fixed carrier frequency scheme, the proposed noise-suppression scheme can reduce the excess noise to 1.8%, and the secret key rate can be increased by 1.43 to 2.86 times at different distances. This noise-suppression scheme is expected to be applied in scenarios like quantum-classical co-transmission and multi-QKD co-transmission to provide noise-suppression solutions.