Reviewerly/exHarmony huggingface dataset:
Mobile Network NPS Analysis Based on Machine Learning

With the improvement of people's living standard and the development of network technology, people put forward higher and higher requirements for the network quality and service quality of operators. In recent years, the domestic telecom operation system began to investigate the customer loyalty of its key business with the help of NPS, but how to combine the NPS investigation to improve the short board of the business is still a difficult problem. This paper mainly takes the location information of users surveyed by NPS as the starting point, and the mobile services of operators as the research object, extracts the action track of users surveyed by NPS, frequently resides in the community and other information, and then analyzes the network KPI indicators of users, and finds out the corresponding relationship between NPS, network KPI and user characteristics through data analysis by big data and artificial intelligence technology. Then establish the NPS evaluation system, predict the potential NPS derogation of users, focus on the business shortcomings behind the NPS survey, so as to better promote business development and market construction.
Capped L2p-Norm Based 2DPCA with Adaptive Capped Threshold Learning for Image Processing

Two-dimensional principal component analysis (2DPCA) is an effective technique to extract low-dimensional representations for face clustering and recognition. However, it suffers from extreme sensitivity to outliers and noise. To improve the robustness of 2DPCA, we design a novel optimal-mean 2DPCA with adaptive capped L2p-norm minimization, called Adaptive Capped L2p-2DPCA, which adopts capped L2p-norm in the objective function to measure the reconstruction error. Simultaneously, Adaptive Capped L2p-2DPCA integrates the adaptive capped threshold learning during the optimization process, such that the capped threshold can be obtained without human intervention. Furthermore, we develop a non-greedy iterative algorithm to solve Adaptive Capped L2p-2DPCA. The proposed algorithm has a closed-form solution in each iteration and is proven to achieve good convergence. We conduct experiments on four face image datasets, and the recognition rates on the four datasets Extended Yale B, AR, CMU PIE, and ORL are 5.02%, 1.86%, 0.47%, and 5.5% higher than the second best method, respectively, which indicates the effectiveness and superiority of our method.
The Limits of AI Content Detectors

As ChatGPT became a popular and powerful language model used by people worldwide in 2023, the problem of students using it to cheat on schoolwork became palpable. While many existing AI content detectors can detect AI-generated texts, such as GPT-2 Content Detector and GPTZero, the accuracy of an AI content detector in detecting generated essays that have been post-edited by humans is unknown. This research discovered the limitations of the GPT-2 Content Detector and answered the question, “How does human post-editing of AI-generated high school English essays affect the result of an AI content detector?” Ten English essays were generated using ChatGPT Plus based on prompts from high school English teachers. Each essay was then edited in 5 different ways to create pairs of unedited and edited essays. All unedited and edited essays were evaluated using GPT-2 Output Detector Demo, and then the results from the detector were studied and analyzed. It was found that introducing spelling mistakes in generated essays and processing the essays with QuillBot will make the result of AI content detectors less accurate. The findings from this research can be used as a guide for companies developing AI-generated text detectors, making them more accurate when dealing with edited generated text. The findings can also be helpful for schools and educators, because knowing that students can edit essays to bypass AI content detectors, educators can develop new ways to examine students’ writing ability.
Sociodemographic Influence on Adolescent Alcohol Perception

Despite federal regulation preventing the sale and consumption of alcohol to minors under the age of 21, rates of alcohol use in adolescents remain high. A collection of recent studies have found that demographics such as race, socioeconomic status, and sexual orientation influence one’s vulnerability to alcohol abuse. The field of knowledge, however, lacked locational data that addressed perceptions towards adolescent alcohol use in specific geographic communities. Focusing specifically on the South Bay, a suburban region in South Los Angeles, filled that niche. Through analyzing similar studies and assessing the feasibility of different approaches to research, a quantitative study that utilized a survey was found to be the most appropriate research design. The survey included three sections: one collecting sociodemographic data, one assessing socio economic status, and one quantifying drinking perception. For distribution, six public and private high schools across the South Bay were contacted through guidance counselors. Between the four schools, 118 survey responses were collected over the course of two weeks. The data collected provides valuable information regarding sociodemographic subpopulation vulnerability to underage alcohol use amongst high school teens in the South Bay. Black students, for instance, had the highest positive perception towards underage drinking when averaged. Students of a high socioeconomic status and members of the LGBTQ community also maintain a high positive perception towards alcohol use. A positive perception towards alcohol alludes to a greater vulnerability of underage alcohol consumption. All three groups could benefit from targeted, preventative measures to combat rates of underage alcohol use.
Pemanfaatan Rumah Adat Tongkonan sebagai Pusat Peningkatan Literasi dan Numerasi bagi Siswa SD di Wilayah Pangala’ Utara

Indonesia is currently ranked sixty-second out of seventy countries, this situation has forced the government to issue new breakthroughs to improve reading and numeracy skills by building several libraries in each region, the government is also working with writers and publishers to make reading books more interesting. and educative. Literacy is the ability to read, write, speak, listen, count and be able to provide information and be able to interact with others. The mentoring for learning to read and count is carried out for students at SDN 2 Rindingallo as an effort to teach children to read and count so that students are more competent in reading and counting. The method used is with study assistance in small groups around the tongkonan traditional house. The results of the service show that through the use of the tongkonan traditional house, elementary school students in the vicinity of the traditional house show an increase in reading and arithmetic skills. The presence of tongkonan in every place in North Pangala' functions as a place for learning and playing activities. Through this activity, the function of the tongkonan is not only as a place to live but also as a place to acquire knowledge.
Multi-risk Aware Trajectory Planning for Car-like Robot in Highly Dynamic Environments

Safe trajectory planning in highly dynamic environments remains a substantial challenge. Traditional risk-based trajectory planning algorithms solve planning problems in environments where humans and robots coexist, by taking into account the risk of collision with both dynamic and static obstacles. However, in highly dynamic environments, their safety during navigation is not guaranteed due to the singularity of risk assessment. Therefore, we first introduce a time-varying density risk map to represent external risks, then introduce a novel interact risk map generated based on the relative state of the robot and dynamic obstacles to represent interact risks. Finally, these two risks are combined based on a convex combination model to obtain a fusion risk map. The planner searches for an efficient, kinematically feasible, and safe trajectory by exploring the state graph built on motion primitives based on the fusion risk map. We have conducted a large number of simulation experiments in various challenging environments, and the experimental results have verified the effectiveness of the algorithm.
Control Architecture for Catching a Fixed-Wing Drone using a Ground Vehicle

In this paper a control architecture designed in 3D for landing a fixed-wing drone on a moving Ground Vehicle (GV) is presented. Two control schemes are proposed, one leads the fixed-wing vehicle towards a desired trajectory while the other regulates the GV's speed and tracks the relative position of the aerial vehicle in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x-y$</tex> plane. The desired trajectory for the aerial vehicle is based on a hyperbolic tangent function, which guarantees a soft descending flight until reach the GV's altitude. The control strategy for the GV allows it to align with the aerial vehicle until its landing. The Lyapunov theory is used for conceiving the control strategies, guaranteeing the stability on each control stage. The control architecture is evaluated in numerical simulations for validating the systems performance in closed loop. Finally, a preliminary experiment is presented for corroborating its well performance.
Convolutional Reservoir Computing for Arrhythmia Classification

Cardiovascular diseases are the foremost cause of mortality worldwide, as stated by the World Health Organization (WHO). The electrocardiogram (ECG) signal is an essential tool for examining the heart’s condition and detecting early arrhythmia abnormalities, which can be life-saving. Our contribution to arrhythmia classification endeavors is a convolutional reservoir computing-based heartbeat classifier. This classifier leverages a convolution neural network (CNN) and reservoir computing (RC) to execute the classification task. The MIT-BIH database is employed to evaluate the algorithm’s validity. An overall accuracy of 99.63% is attained for the convolutional-based classifier compared to 99.32% for the standard RC-based classifier.
PillarDAN: Pillar-based Dual Attention Attention Network for 3D Object Detection with 4D RaDAR

3D object detection plays an indispensable role in autonomous driving. Most existing methods for 3D object detection from point clouds are LiDAR-based. However, LiDAR may suffer significant performance degradation from poor environmental conditions, such as rainy and foggy weather. Compared to LiDAR, 4D RaDAR is more robust to various environments and can provide velocity information. Nevertheless, its point cloud is sparser and contains more noise. Thus, existing LiDAR-dependent 3D object detection methods cannot be effectively applied to 4D Radar. To cope with this issue, we propose a new framework to improve the 3D object detection performance of 4D Radar, dubbed Pillar-based Dual Attention Network (PillarDAN). Specifically, PillarDAN builds the Global Pillar Attention (GPA) to enhance the feature extraction capability from sparser 4D Radar point clouds. Meanwhile, the Pillar Feature Attention (PFA) is proposed to focus on the truly effective information, thus suppressing the noise of point clouds. We also present an effective 3D coordinate embedding to improve the position awareness of the bird's-eye-view (BEV) feature map. Experimental results on the Astyx HiRes2019 dataset show our PillarDAN achieves effective performance improvement, which is 3.28% higher in 3D mAP and 3.12% higher in BEV mAP than the previous best method.
OPTIMIZATION OF ASYMMETRIC BILATERAL COMPLEX FENESTRATION SYSTEMS IN STATE ELEMENTARY SCHOOL CLASSROOMS IN INDONESIA

Abstract_ Penelitian ini bertujuan untuk meningkatkan kinerja pencahayaan alami siang hari (PASH) dengan menggunakan sistem penetrasi kompleks (CFS) bilateral asimetris di ruang kelas hipotetis yang terletak di dua kota tropis di Indonesia, yaitu Bandung dan Lhokseumawe, yang masing-masing terletak di sebelah selatan dan utara garis khatulistiwa. Bandung merupakan kota tropis diatas pegunungan, sedangkan Lhokseumawe merupakan kota pesisir. Optimalisasi dilakukan untuk keempat orientasi mata angin. Kinerja PASH ruang kelas dinilai dengan menggunakan metrik aUDI250-750lx, aUDI100-3000lx, sDA300/50%, dan ASE1000.250. Kondisi awal menunjukkan performa pencahayaan siang hari yang tidak memadai ditunjukkan oleh nilai aUDI100-3000lx yang rendah (di bawah 80%) dan nilai ASE1000,250 yang tidak memuaskan (di atas 10%). Untuk memenuhi standar performa pencahayaan siang hari yang baik, penelitian ini menggunakan metode simulasi komputasi untuk kondisi tahunan. Selanjutnya, algoritma RBFOpt digunakan melalui Opossum untuk melakukan optimasi. Berdasarkan hasil optimasi, integrasi CFS ke dalam selubung bangunan menghasilkan peningkatan kinerja PASH di kedua lokasi. &#x0D; Keywords: Penetrasi kompleks; Pencahayaan alami siang hari; Bukaan asimetris; Optimasi.&#x0D; &#x0D; Abstrak_ This study aims to enhance the daylighting performance of an asymmetric bilateral complex fenestration system (CFS) in a hypothetical classroom located in two Indonesian tropical cities, namely Bandung and Lhokseumawe, which are located slightly south and north of the equator, respectively. Bandung is a mountainous tropical city, whereas Lhokseumawe is a coastal city. Optimization is conducted for all four cardinal orientations. The classroom's daylight performance is assessed using aUDI250-750lx, aUDI100-3000lx, sDA300/50%, and ASE1000,250 metrics. The baseline conditions reveal inadequate daylighting performance with a low aUDI100-3000lx reading (below 80%) and an unsatisfactory ASE1000,250 value (above 20%). To meet the good daylighting performance standards, this study utilizes a computational simulation method for annual daylight simulation. Furthermore, the RBFOpt algorithm was used through Opossum to conduct optimization. According to the optimization results, the integration of CFS into the building's envelope results in improved daylight performance in both locations.&#x0D; Kata kunci: Complex fenestration; Daylighting; Asymmetrical bilateral opening; Optimization.&#x0D;
Designing a Test Case Catalog based on Document Review for Optimizing Test Case Development Process

Software development projects are prone to failure. This failure is due to the challenges faced. Finding and fixing bugs is time-consuming and costly, so it must be done effectively and efficiently. In this research, we designed a Test Case Catalog that reused existing test cases. Based on the XYZ smart campus system project, we have collected test cases referring to existing Software Requirement Documents (SRDs), as the basis to the testing process. To form the test case catalog, the test cases were generated from common test cases among twenty eight of thirty seven targeted applications in the XYZ project. The design of test case catalog consisted of five steps: reviewing requirement document, counting words, populating common functions, generating test cases, and forming test case catalog. The experimental results of using the Test Case Catalog in application testing by one Leading University’s IT Support unit showed that using the Test Case Catalog helped testing by increasing the number of test cases executed and the test variation value. Therefore, it shows that our proposed test case catalog can optimize test case development process. It is expected that by using the Test Case Catalog, testers can better reuse and manage test cases in the catalog, so that she can generate test case faster and better.
BI-TLM: Bilinear Interpolation with Transfer Learning Model for Breast Cancer Classification

Breast cancer is another fastest growing reason for death in women, trailing only lung cancer. Breast cancer mortality can be reduced if it is discovered early and treated. Because conventional breast cancer assessment needs much longer, an automated method needs to be invented for early cancer detection. This research provides a network for detecting breast cancer from ultrasound images which uses deep neural networks and a trainable sub-layer network to select the best features. Because ultrasound images contain noise and artifacts, a bilinear filtering method is considered in the preprocessing step to reduce the noise and artifacts. The DenseNet201 model was used in our proposed network as a deep learning-based technique since It has the potential to disperse feature strength, stimulate feature reuse, and significantly decrease the parameters. The proposed system along with other models were trained and evaluated on a balanced ultrasound dataset divided into benign, malignant, and normal classes. Each model’s performance was evaluated utilizing several accuracy metrics. Among the models examined, the suggested model outperformed the others, suggesting its potential for accurately diagnosing breast cancer from ultrasound images. The discoveries of the work contribute to medical imaging and emphasize the efficacy of the DenseNet201 model.
Research of online learning status monitoring based on deep learning

In contrast to traditional classroom settings, online teaching leverages network technology to facilitate instruction. A pressing challenge in this domain is the effective monitoring of students’ engagement levels to optimize learning outcomes. Addressing this, the present study introduces a deep learning-based online classroom system equipped with a learning status monitoring feature. This system employs image processing techniques and software development to capture and analyze students’ postures during online sessions, thereby providing educators and guardians with comprehensive insights into students’ engagement levels.
Pedagogical essence and structural understanding of graphic organizers in Chemistry

This article analyzes all the most modern interactive teaching methods, in particular, graphic organizers in the form of a cluster, a classification table, a T-chart, KWL diagrams, and the pyramid method in Chemistry at secondary schools. Their functional structure, methods of practical application in the classroom, as well as the conceptual content of the described illustrative teaching methods are analyzed, which will help to assimilate better new educational material not only in Chemistry but also in any academic subject, discipline, and in the education system as a whole.
Open Science Knowledge Platform: A Journey to a Dynamic Resource

Open science has gained significant momentum over the past few years, with various movements and initiatives emerging to promote sharing of scientific knowledge, data, and resources with the wider community. Open science is a broad term encompassing a range of practices that strive to increase transparency, inclusivity, and accessibility in science.
Towards the Integration of Digital Citizenship in Foreign Language Education: concepts, practices and training

This article reports on different research and pedagogical practices that have been taking place as part of a transnational European Erasmus+ project aiming to strengthen the profile of Digital Citizenship Education (DCE) in the context of Foreign Language Education (FLE). In order to do this, this article briefly reviews some of the key concepts (eg critical digital literacies, democratic cultures) for an evolving definition and (re)conceptualization of digital citizenship in the context of FLE. Equally, a critical analysis of European documents concerning these important topics will be carried out to inform our proposed pedagogical framework, which will be suggested as the grounds for presenting the results of different DCE practices. The first of these practices is the development of resources for teachers to integrate DCE into foreign language learning followed by a training package for teachers. The results collected after piloting one component of the training package -the self-study online course- and the feedback received from the course participants' questionnaire, will be presented and discussed, together with a self-reflection tool for teachers. In addition, some qualitative data results will allow us to explore the perceptions of pre-service teachers regarding the challenges of integrating DCE into FLE. To conclude, some concrete recommendations will be given toward the development and implementation of DCE in foreign language education.
Ultra-high Spatial and Strain Resolution 3D Shape Sensing Based on Twice-differential φ-OFDR System

By employing with twice-differential φ-OFDR system, the 3D shape is precisely reconstructed with the spatial resolution of 20μm, the strain resolution of 0.1με and maximum reconstruction error of 1.187% over a length of 200mm.
2-D Phase Unwrapping in DAS with Transport of Intensity Equation

We introduce a 2-D phase unwrapping algorithm to DAS, surpassing dynamic range limits of conventional methods. Considering inter-trace phases, it enhances unwrap ability, mitigating stripe error caused by low SNR.
Surface Threat Event Identification for Subway Tunnels Using Multi-classifier Fusion Algorithm Based Optical Fiber DAS System

Multi-classifier fusion algorithm is proposed to identify surface intrusion events in the subway tunnel. Experimental demonstration verifies that the average recognition rate of 96.1% for four target events is achieved by the proposed scheme.
Collaborative System Design of Mixed Reality Communication for Medical Training

We present the design of a mixed reality (MR) telehealth training system that aims to close the gap between in-person and distance training and re-training for medical procedures. Our system uses real-time volumetric capture as a means for communicating and relating spatial information between the non-colocated trainee and instructor. The system's design is based on a requirements elicitation study performed in situ, at a medical school simulation training center. The focus is on the lightweight real-time transmission of volumetric data - meaning the use of consumer hardware, easy and quick deployment, and low-demand computations. We evaluate the MR system design by analyzing the workload for the users during medical training. We compare in-person, video, and MR training workloads. The results indicate that the overall workload for central line placement training with MR does not increase significantly compared to video communication. Our work shows that, when designed strategically together with domain experts, an MR communication system can be used effectively for complex medical procedural training without increasing the overall workload for users significantly. Moreover, MR systems offer new opportunities for teaching due to spatial information, hand tracking, and augmented communication.
Criteria Based Evaluation of Cross-Platform Development Frameworks

Cross-platform development frameworks continue to play an important role in developing of mobile applications. There are a plethora of possible frameworks to choose between, and all claim to give developers an advantage in one or more aspects of the development cycle. To further shed light on this area, our research investigates the selection of mobile cross-platform frameworks by investigating criteria and methods for choosing the best suitable framework for the development context. Our study confirm previous research findings and with that anchor the categorisation for a cross-platform comparison taxonomy. Our research further contribute with a criteria check-list to apply for cross-platform framework selection and showcase its use through a comparative study of a cross-platform application implemented in two frameworks. Although showcasing novelty in the criteria checklist, further research is needed to make the checklist robust and streamlined for public use.