Reviewerly/exHarmony huggingface dataset:
Multi-view dual-channel graph convolutional networks with multi-task learning

Abstract Network embedding has been extensively used in several practical applications and achieved great success. However, existing studies mainly focus on single task or single view and cannot obtain deeper relevant information for accomplishing tasks. In this paper, a novel approach is proposed to address the problem of insufficient information consideration in network embedding, which is termed multi-task-oriented adaptive dual-channel graph convolutional network (TAD-GCN). We firstly use kNN graph construction method to generate three views for each network dataset. Then, the proposed TAD-GCN contains dual-channel GCN which can extract the specific and shared embeddings from multiple views simultaneously, and attention mechanism is adopted to fuse them adaptively. In addition, we design similarity constraint and difference constraint to further enhance their semantic similarity and ensure that they capture the different information. Lastly, a multi-task learning module is introduced to solve multiple tasks simultaneously and optimize the model with its losses. The experimental results demonstrate that our model TAD-GCN not only completes multiple downstream tasks at the same time, but also achieves excellent performance compared with eight state-of-the-art methods.
Violence region localization in video and the school violent actions classification

Classification of school violence has been proven to be an effective solution for preventing violence within educational institutions. As a result, technical proposals aimed at enhancing the efficacy of violence classification are of considerable interest to researchers. This study explores the utilization of the SORT tracking method for localizing and tracking objects in videos related to school violence, coupled with the application of LSTM and GRU methods to enhance the accuracy of the violence classification model. Furthermore, we introduce the concept of a padding box to localize, identify actions, and recover tracked objects lost during video playback. The integration of these techniques offers a robust and efficient system for analyzing and preventing violence in educational environments. The results demonstrate that object localization and recovery algorithms yield improved violent classification outcomes compared to both the SORT tracking and violence classification algorithms alone, achieving an impressive accuracy rate of 72.13%. These experimental findings hold promise, especially in educational settings, where the assumption of camera stability is justifiable. This distinction is crucial due to the unique characteristics of violence in educational environments, setting it apart from other forms of violence.
IoT network traffic classification: a deep learning method with Fourier transform-assisted hyperparameter optimization

There has been a significant increase in IoT-related network traffic in recent years. The surge in IoT has resulted in a more complex network environment than ever before. In light of this, deep learning (DL)-based network traffic classification has gained prominence, because of its powerful feature extraction capabilities for complex problems. However, selecting hyperparameters for DL models, such as network depth, lacks a theoretical basis and costs a lot of time. Often, the setting of hyperparameters is not directly related to the inherent characteristics of the data but relies on empirical knowledge. Traditionally, hyperparameters are adjusted based on performance during model training, leading to a significant amount of tuning work. To address these problems, this paper proposes a novel DL-based anomaly network traffic classification algorithm. This algorithm estimates the required hyperparameters by analyzing the spectrum obtained from Fourier Transform of the input samples in advance, enhancing the efficiency of model training for IoT network traffic classification. Our experiments reveal that the complexity of the neural network is directly proportional to the spectrum of the data being trained. As the presence of high-frequency components increases, the complexity needed for the neural network parameters also rises. Based on the conclusions drawn from our experiments, we can pre-determine appropriate hyperparameters for the neural network, thereby saving over 70% of the time in neural network parameter tuning.
Training software architects suiting software industry needs: A literature review

Abstract The ability to define, evaluate, and implement software architectures is a fundamental skill for software engineers. However, teaching software architecture can be challenging as it requires students to be involved in real-context projects with high degrees of complexity. This involves making trade-off decisions among several quality attributes. Furthermore, the academic perception of software architecture differs from the industrial viewpoint. To address this issue, a study was conducted to identify and analyze the strategies, challenges, and course experiences used for teaching software architectures. The study analyzed 56 articles reporting on teaching experiences focused specifically on software architectures or focused on software engineering in general but discussing software architecture. The main contributions of this work include identifying strategies used in educating software architecture students aligned with the needs of the software industry. These strategies include short design projects, large development projects, and projects with actual clients. Additionally, the study compared curriculum contents in software development and architecture courses and identified recurring topics such as architecture patterns, quality attributes, and architectural views. This study also recognizes the set of skills that students of software architecture should develop during training, such as leadership and negotiation. The challenges in software architecture training were discussed, such as instructors’ lack of experience in actual projects, the abstract and fuzzy nature of software architectures, and the difficulty of involving clients and industry experts. Evaluation methods commonly used in training software architects, such as surveys, pre-test/post-test, and quality metrics on architectural artifacts, were identified and described. Overall, this study guides researchers and educators in improving their software architecture courses by incorporating strategies reported by the literature review. These strategies can bring architecture courses closer to the needs and conditions of the software industry.
Design and Development of Automatic Tennis Ball Collector

Tennis players frequently practice from one side of the court without an opponent on the other side to return by hitting many balls. During practice sessions, personally collecting the balls took more time. The aim of this project is to designDesign and construct a tennis ball-collection robot that can automatically gather the balls from one half of the court, releasing the player to rest rather than physically collecting the balls. This work includes a detailed designDesign, a background data, prototypes, and concept models. We developed a novel autonomous or semiautonomous mobile robotAutonomous mobile robot for the purpose of gathering tennis balls for our capstone project. Two independently operating wheels help the robot move, while a castor serves to stabilize it. Tennis balls are pushed into a basket on top of the robot by a rotating brush, which serves as the novel collection mechanism. The drum spins with the help of a motor at a speed that has been confirmed through trial. To ensure that the adopted concepts and conceptual designsDesign are practical and solid, three subsequent functional prototypes have been conceived, constructed, and tested. Tennis players can also use Arduino programming to direct the robot to move and collect the balls if it is unable to locate, identify, and gather tennis balls on its own. All tennis balls on the court will be recognized and localized in future using global cameras and a centralized image-processing system. Furthermore, it is believed that this system will handle navigation for mobile robots as well as path planningPath planning for the best possible outcome.
CMorse—Automated Laser-Based Morse Code Transmission Through Multi-layered Encryption

Streamlining and boosting the effectiveness of Morse codeMorse code communication are a major challenge that limits the application of Morse codeMorse code in real-life and secure and flawless communication. The main objective of this research is to automate and increase the efficiencyEfficiency of short-range Morse codeMorse code communication by using multiple laser diodesLaser diode which allow each message character to be encrypted and transmitted simultaneously in place of the laborious and time-consuming traditional method of Morse communication. In the present approach, message characters are transmitted as a whole, one character at a time instead of a dot (.) or dash (–) at a time. The light rays are projected onto a screen at the transmitter side, and a camera on the descriptor end captures the projected rays and decrypts them using image processingImage processing. This reduces the time taken to transmit messages by a significant amount, enabling faster and more reliable Morse communication. Upon entering the desired message on the transmission side, it is encrypted using multiple layers of pangram cryptography. This message is transmitted letter by letter via the Arduino Mega ports onto the screen. The encryptionEncryption uses a randomized set of pangram keys at each level, making it robust to interceptions and decryptionDecryption by third parties.
A systematic evaluation of an RTK-GPS device for wearable augmented reality

Abstract Global Positioning Satellite (GPS) systems sample points on the Earth’s surface with meter accuracy. Real-Time Kinematic (RTK) devices improve GPS performances by providing real-time correction data from ground stations, achieving centimeter accuracy. Reliable tracking approaches are essential for Augmented Reality (AR) applications, especially for outdoor scenarios, which still present unsolved challenges. AR handheld tracking capabilities have been greatly improved by integrating visual tracking approaches with RTK devices, whereas little is known about combining wearable AR interfaces with RTK systems. Although wearable AR devices are intrinsically designed for AR applications, their performance dramatically reduces in large outdoor areas, comprising the user experience. Hence, this paper provides a rigorous evaluation of a small-size RTK device that does not need any additional software integration to collect positional data. The main goal of the assessment is to verify whether its integration with a wearable AR device is advantageous or not. The evaluation has been performed considering both static and dynamic scenarios in open-sky and urban areas. The results show that the RTK device can achieve 1 cm accuracy when used in open-sky areas. In contrast, its accuracy dramatically reduces in the proximity of buildings and obstacles, showing average errors ranging from 76 to 2561%. Since wearable AR devices have an average accuracy of 2 cm, the outcomes indicate that RTK devices should be combined with wearable AR devices only when the RTK device is far from obstacles. On the contrary, the positional data should be completely avoided when barriers surround the RTK device.
Examining The Impact Of Using Authentic Materials On Esl/Efl Learners

This study aims to investigate the impact of using authentic materials on language learning outcomes in ESL/EFL learners. The study involved a control group that relied on traditional textbook materials and an experimental group that was exposed to authentic materials with the sample population of 111 students who took GEN60-116- English for Community Development course in term/1/ 2018 at Walailak University with the course duration of 13 weeks. Post-test results were analyzed and compared to pre-test scores for both groups. The control group's post-test scores were lower than their pre-test scores, suggesting that the traditional textbook approach may not effectively support language learning needs. In contrast, the experimental group's post-test scores surpassed their pre-test scores, indicating that exposure to authentic materials had a positive impact on their language learning outcomes. The study's findings align with the previous studies on the benefits of authentic materials in language instruction, as exposure to authentic materials can enhance vocabulary acquisition, listening comprehension, reading comprehension, writing skills, and overall language proficiency. These results have significant implications for language teachers and curriculum designers. This study suggests that incorporating authentic materials in language instruction can enhance language learning outcomes, enabling learners to develop a deeper understanding of the language and its usage in authentic contexts.
An Approach for Deployment of Service-Oriented Simulation Run-Time Resources

The requirements for low latency and high stability in large-scale geo-distributed training simulations have made cloud-edge collaborative simulation an emerging trend. However, there is currently limited research on how to deploy simulation run-time resources (SRR), including edge servers, simulation services, and simulation members. On one hand, the deployment schemes of these resources are coupled and have mutual impacts. It is difficult to ensure overall optimum by deploying these resources separately. On the other hand, the pursuit of low latency and high system stability is often challenging to achieve simultaneously because high stability implies low server load, while a small number of simulation services implies high response latency. We formulate this problem as a multi-objective optimization problem for the joint deployment of SRR, considering the complex combinatorial relationship between simulation services. Our objective is to minimize the system time cost and resource usage rate of edge servers under constraints such as server resource capacity and the relationship between edge servers and base stations. To address this problem, we propose a learnable genetic algorithm for SRR deployment (LGASRD) where the population can learn from elites and adaptively select evolution operators performing well. Extensive experiments with different settings based on real-world data sets demonstrate that LGASRD outperforms the baseline policies in terms of optimality, feasibility, and convergence rate, verifying the effectiveness and excellence of LGASRD when deploying SRR.
Exploring the effect of mobile-assisted task-based learning on vocabulary achievement and student attitude

Abstract This study explores the effect of mobile-assisted task-based learning (M-TBL) on vocabulary achievement and student attitude among English as a in content and language integrated learning (CLIL) science lessons, the researcher focused on foreign language (FL) students. To investigate this, a quasi-experimental research design was utilized. In the first semester of the 2022–2023 academic year, 34 fourth-grade students from a private school in Istanbul took part in the study. During the course of 6 weeks, the experimental group engaged in six different M-TBL activities and were assigned a one-page worksheet as homework, while the control group completed only the one-page worksheet. Quantitative data for the study were collected through vocabulary achievement tests and a student attitude questionnaire. The data underwent analysis through paired sample t-tests and descriptive analyses. The results indicated a significant difference in vocabulary achievement between the experimental and control groups, and they also revealed a positive impact on student attitudes, igniting their interest in learning the target language. In light of these findings, it can be concluded that mobile-assisted task-based activities yield positive effects on both vocabulary achievement and student attitude in primary FL learners participating in CLIL science lessons.
Fast detection of bag-breakups in pulsating and steady airflow using video analysis and deep learning

Abstract Object detection methods based on deep learning have made great progress in recent years and have been used successfully in many different applications. However, since they have been evaluated predominantly on datasets of natural images, it is still unclear how accurate and effective they can be if used in special domain applications, for example in scientific, industrial, etc. images, where the properties of the images are very different from those taken in natural scenes. In this study, we illustrate the challenges one needs to face in such a setting on a concrete practical application, involving the detection of a particular fluid phenomenon—bag-breakup—in images of droplet scattering, which differ significantly from natural images. Using two technologically mature and state-of-the-art object detection methods, RetinaNet and YOLOv7, we discuss what strategies need to be considered in this problem setting, and perform both quantitative and qualitative evaluations to study their effects. Additionally, we also propose a new method to further improve accuracy of detection by utilizing information from several consecutive frames. We hope that the practical insights gained in this study can be of use to other researchers and practitioners when targeting applications where the images differ greatly from natural images.
A Multiclustering Evolutionary Hyperrectangle-Based Algorithm

Abstract Clustering is a grouping technique that has long been used to relate data homogeneously. With the huge growth of complex datasets from different sources in the last decade, new paradigms have emerged. Multiclustering is a new concept within clustering that attempts to simultaneously generate multiple clusters that are bound to be different from each other, allowing to analyze and discover hidden patterns in the dataset compared to single clustering methods. This paper presents a hybrid methodology based on an evolutionary approach with the concepts of hyperrectangle for multiclustering, called MultiCHCClust. The algorithm is applied in a post-processing stage and it improves the results obtained for a clustering algorithm with respect to the partitioning of the dataset and the optimization of the number of partitions, achieving a high degree of compactness and separation of the partitioned dataset as can be observed in a complete experimental study.
Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model

The detection of anomalies in software systems has become increasingly crucial in recent years due to their impact on overall software quality. However, existing integrated anomaly detectors usually combine the results of multiple detectors in a clustering manner and do not consider the changes in data anomalies in the time dimension. This paper investigates the limitations of existing anomaly detection methods and proposes an improved integrated anomaly detection approach based on time windows and a voting mechanism. By utilizing multiple time windows, the proposed method overcomes the challenges of cumulative anomalies and achieves enhanced performance in capturing anomalies that accumulate gradually over time. Additionally, two hybrid models are introduced, based on accuracy and sensitivity, respectively, to optimize performance metrics such as AUC, precision, recall, and F1-score. The proposed method demonstrates remarkable performance, achieving either the highest or only a marginal 3% lower performance compared to the optimal model.
TCM-Net: Mixed Global–Local Learning for Salient Object Detection in Optical Remote Sensing Images

Deep-learning methods have made significant progress for salient object detection in optical remote sensing images (ORSI-SOD). However, it is difficult for existing methods to effectively exploit both the multi-scale global context and local detail features due to the cluttered background and different scales that characterize ORSIs. To solve the problem, we propose a transformer and convolution mixed network (TCM-Net), with a U-shaped codec architecture for ORSI-SOD. By using a dual-path complementary network, we obtain both the global context and local detail information from the ORSIs of different resolution. A local and global features fusion module was developed to integrate the information at corresponding decoder layers. Furthermore, an attention gate module was designed to refine features while suppressing noise at each decoder layer. Finally, we tailored a hybrid loss function to our network structure, which incorporates three supervision strategies: global, local and output. Extensive experiments were conducted on three common datasets, and TCM-Net outperforms 17 state-of-the-art methods.
Evolution and Trends in Sign Language Avatar Systems: Unveiling a 40-Year Journey via Systematic Review

Sign language (SL) avatar systems aid communication between the hearing and deaf communities. Despite technological progress, there is a lack of a standardized avatar development framework. This paper offers a systematic review of SL avatar systems spanning from 1982 to 2022. Using PRISMA guidelines, we shortlisted 47 papers from an initial 1765, focusing on sign synthesis techniques, corpora, design strategies, and facial expression methods. We also discuss both objective and subjective evaluation methodologies. Our findings highlight key trends and suggest new research avenues for improving SL avatars.
Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network

In the task of classifying high-altitude flying objects, due to the limitations of the target flight altitude, there are issues such as insufficient contour information, low contrast, and fewer pixels in the target objects obtained through infrared detection technology, making it challenging to accurately classify them. In order to improve the classification performance and achieve the effective classification of the targets, this study proposes a high-altitude flying object classification algorithm based on radiation characteristic data. The target images are obtained through an infrared camera, and the radiation characteristics of the targets are measured using radiation characteristic measurement techniques. The classification is performed using an attention-based convolutional neural network (CNN) and gated recurrent unit (GRU) (referred to as ACGRU). In ACGRU, CNN-GRU and GRU-CNN networks are used to extract vectorized radiation characteristic data. The raw data are processed using Highway Network, and SoftMax is used for high-altitude flying object classification. The classification accuracy of ACGRU reaches 94.8%, and the F1 score reaches 93.9%. To verify the generalization performance of the model, comparative experiments and significance analysis were conducted with other algorithms on radiation characteristic datasets and 17 multidimensional time series datasets from UEA. The results show that the proposed ACGRU algorithm performs excellently in the task of high-altitude flying object classification based on radiation characteristics.