Reviewerly/exHarmony huggingface dataset:
STRATEGI DAN TANTANGAN MANAJEMEN TEKNOMEDIA DI LEMBAGA PENDIDIKAN ANAK USIA DINI

Digital learning media management is a crucial skill required by the heads of educational institutions to navigate the digital era. This research aims to depict: (1) the challenges faced by the leaders of Early Childhood Education (PAUD) institutions in Merauke City in managing technomedia in the learning process, and (2) the strategies employed by these leaders to overcome challenges in technomedia management during the learning process. The informants for this study were purposefully sampled from school principals in Merauke City. The primary challenges confronted by school principals in managing digital learning media in Early Childhood Education institutions (PAUD) include the availability of adequate technological resources, the development of teachers' skills in using technology, and the management of student data security and privacy. School principals also need to take into account parental involvement in supporting the use of digital media at home and ensure that technology use remains balanced with the physical and social activities of children. To tackle these challenges, strategies employed by school principals include organizing training and developing teachers' skills in technology use, selecting quality digital learning content, and establishing clear policies and guidelines related to technology use. Additionally, careful monitoring of students' technology use and maintaining a balance between technology use and traditional learning experiences are also emphasized. By implementing these strategies, PAUD school principals can effectively manage digital learning media, enhance the quality of early childhood education, and ensure that technology is used judiciously in the learning process.
Chatbot Informasi Penerimaan Mahasiswa Baru Menggunakan Metode FastText dan LSTM

New Student Admission (PMB) is an important stage in the continuity of education in an educational institution. The Faculty of Science and Informatics (FSI) at Jenderal Achmad Yani University (UNJANI) provides information services about PMB to prospective students and parents/guardians of prospective students but is still inefficient, so it is necessary to improve PMB information services by using Chatbots as a solution that is able to serve questions effectively and consistent. This study aims to develop a PMB information Chatbot system for FSI using the FastText and Long Short-Term Memory (LSTM) methods. Several methods have been used in Chatbot development research, such as Term Frequency–Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Convolutional Neural Networks (CNN). However, these studies still have certain limitations, such as the inability to grasp the meaning of words and difficulties in handling certain inputs. In this study, the text classification model uses the FastText method as the stage for representing words in vector form, then combined with several pre-processing methods (Tokenization &amp; Casefolding) and LSTM for the classification stage. Then put it into the Chatbot component according to the architecture that was made. In testing, the Black Box Testing method is used to ensure the functionality of the Chatbot system. The test results show that the Chatbot system is able to understand the topic of questions asked by users properly. The interaction between users and Chatbots also runs smoothly, resulting in appropriate and informative responses. The results of this study are expected to be an effective and consistent solution for providing information about PMB to prospective students and parents/guardians of prospective students at FSI.
GAMBARAN UMUM TOKSOPLASMOSIS OKULER DI RUMAH SAKIT DR KARIADI SEMARANG

Introduction: Ocular toxoplasmosis is the most common infectious etiology of posterior uveitis. The aim of this study is to describe the clinical characteristics of ocular toxoplasmosis in Dr. Kariadi Hospital, Semarang. Methods: A retrospective review of out-patient medical records of ocular toxoplasmosis at Dr. Kariadi Hospital Semarang from January 2018 to December 2022 was done. All toxoplasmic uveitis were included, whereas incomplete medical records and loss of follow-up were excluded. The clinical characteristics of patients were recorded and analyzed. Results: During 5 years a total of 160 patients were recruited of which 123 fulfilled the criteria, consisting of 47 males (38.2%) and 76 females (61.8%), mostly were acquired infection (97.6%) with immunocompetent status (75.6%), and affected unilateral eye (65%). The predominant age was 26-45 years old (41.5%). Primary infection (70.7%) was greater than recurrent infection (8.9%) in which recurrence affects 46-65 years old (72.7%). Visual acuity at the initial presentation was &lt;6/18-6/60 (26.2%), and the final follow-up increased to ?6/18 (33.1%). Posterior uveitis (35.1%) with posterior pole lesion (66%) was a common clinical sign found. Medications given were trimethoprim-sulfamethoxazole (51,3%), and corticosteroid (27.5%) as an adjuvant. The complication detected was cataract (13%). Conclusion: Ocular toxoplasmosis was mostly found in immunocompetent male patients, acquired, unilateral, and in the posterior pole. A good response was found by trimethoprim-sulfamethoxazole and corticosteroid medications.
Microwave Photon-Number Amplification

A new photon-number amplification scheme, which combines the advantages of a single-photon detector and a power meter, could lead to new photon-detection possibilities in quantum-sensing and quantum-computing applications.
Quantifying the unextendibility of entanglement

Abstract Entanglement is a striking feature of quantum mechanics, and it has a key property called unextendibility. In this paper, we present a framework for quantifying and investigating the unextendibility of general bipartite quantum states. First, we define the unextendible entanglement, a family of entanglement measures based on the concept of a state-dependent set of free states. The intuition behind these measures is that the more entangled a bipartite state is, the less entangled each of its individual systems is with a third party. Second, we demonstrate that the unextendible entanglement is an entanglement monotone under two-extendible quantum operations, including local operations and one-way classical communication as a special case. Normalization and faithfulness are two other desirable properties of unextendible entanglement, which we establish here. We further show that the unextendible entanglement provides efficiently computable benchmarks for the rate of exact entanglement or secret key distillation, as well as the overhead of probabilistic entanglement or secret key distillation.
Text summarization using modified generative adversarial network1

Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99% . The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively.
Enhancing Semi Supervised Semantic Segmentation Through Cycle-Consistent Label Propagation in Video

Abstract To perform semantic image segmentation using deep learning models, a significant quantity of data and meticulous manual annotation is necessary (Mani in: Research anthology on improving medical imaging techniques for analysis and intervention. IGI Global, pp. 107–125, 2023), and the process consumes a lot of resources, including time and money. To resolve such issues, we introduce a unique label propagation method (Qin et al. in IEEE/CAA J Autom Sinica 10(5):1192–1208, 2023) that utilizes cycle consistency across time to propagate labels over longer time horizons with higher accuracy. Additionally, we acknowledge that dense pixel annotation is a noisy process (Das et al. in: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 5978–5987, 2023), whether performed manually or automatically. To address this, we present a principled approach that accounts for label uncertainty when training with labels from multiple noisy labeling processes. We introduce two new approaches; Warp-Refine Propagation and Uncertainty-Aware Training, for improving label propagation and handling noisy labels, respectively, and support the process with quantitative and qualitative evaluations and theoretical justification. Our contributions are validated on the Cityscapes and ApolloScape datasets, where we achieve encouraging results. In later endeavors, the aim should be to expand such approaches to include other noisy augmentation processes like image-based rendering methods (Laraqui et al. in Int J Comput Aid Eng Technol 18(5):141–151, 2023), thanks to the noisy label learning approach.
Generative adversarial network for car following trajectory generation and anomaly detection

Car-following trajectory generation and anomaly detection are critical functions in the sensing module of an automated vehicle. However, developing models that capture realistic trajectory data distribution and detect anomalous driving behaviors could be challenging. This paper proposes 'TrajGAN', an unsupervised learning approach based on the Generative Adversarial Network (GAN) to exploit vehicle car following trajectory data for generation and anomaly detection. The proposed TrajGAN consists of two modules, an encoder-decoder Long Short-Term Memory (LSTM)-based generator and an LSTM-multilayer perceptron (MLP) based discriminator, whose former component is used to generate vehicular car following trajectories and the latter one is for trajectory anomaly detection. By letting these two modules game with each other in training, we can simultaneously achieve robust trajectory generators and anomaly detectors. Trained with the Next Generation Simulation (NGSIM) dataset, TrajGAN can generate realistic trajectories with a similar distribution of training data and identify a manifold of anomalous trajectories based on an anomaly scoring scheme. Simulation results indicate that the proposed approach is efficient in reproducing artificial trajectories and identifying anomalous driving behaviors.
Exploring and reflecting on the influences that shape teacher professional digital competence frameworks

The importance of Digital Competence in teacher education has increased in recent years resulting in a range of digital competency frameworks aimed at guiding national and regional governments in their integration of digital competence in teacher education. The discourses and assumptions underpinning digital competence frameworks are discussed in this paper using two influential supra-national competency frameworks, the UNESCO ICT Competency Framework for Teachers and the DigCompEdu, as examples. The theoretical reflection argues that several influences have shaped the current discourse. These influences include the rise of teacher performance and competency frameworks, the tradition of ranking teachers' levels of technology use and the techno-centric education discourses that align technology use with constructivist practices. Through this theoretical reflection, the paper raises questions about their use in, and implications for, teacher education arguing that, while such frameworks provide helpful guidance for policy makers and teacher educators, they may stifle teachers' professional autonomy and straight-jacket technology use in a linear and deterministic manner.
Comparison of Modern Deep Learning Models for Speaker Verification

This research presents an extensive comparative analysis of a selection of popular deep speaker embedding models, namely WavLM, TitaNet, ECAPA, and PyAnnote, applied in speaker verification tasks. The study employs a specially curated dataset, specifically designed to mirror the real-world operating conditions of voice models as accurately as possible. This dataset includes short, non-English statements gathered from interviews on a popular online video platform. The dataset features a wide range of speakers, with 33 males and 17 females, making a total of 50 unique voices. These speakers vary in age from 20 to 70 years old. This variety helps in thoroughly testing speaker verification models. This dataset is especially useful for research on speaker verification with short recordings. It consists of 10 clips for each person, each clip being no longer than 10 s, adding up to 500 recordings in total. The total length of all recordings is about 1 h and 30 min, which averages to roughly 100 s for each speaker. This dataset is a valuable tool for research in speaker verification, particularly for studies involving short audio clips. The performance of these models is evaluated using common biometric metrics such as false acceptance rate (FAR), false rejection rate (FRR), equal error rate (EER) and detection cost function (DCF). The results reveal that the TitaNet and ECAPA models stand out by presenting the lowest EER (1.91% and 1.71%, respectively) and thus exhibiting higher discriminative features, ensuring, on the one hand, a reduction in intra-class distance (the same speaker), and, on the other hand, maximizing the distance between different speaker embeddings. This analysis also highlights the ECAPA model’s advantageous balance of performance and efficiency, achieving an inference time of 69.43 milliseconds, slightly longer than the PyAnnote models. This study not only compares the performance of models but also provides a comparative analysis of respective model embeddings, offering insights into their strengths and weaknesses. The presented findings serve as a foundation for guiding future research in speaker verification, especially in the context of short audio samples or limited data. This may be particularly relevant for applications requiring quick and accurate speaker identification from short voice clips.
Synthetic Car Dataset for Vehicle Detection: Integrating Aerial and Satellite Imagery

Vehicle detection is a very important aspect of computer vision application to aerial and satellite imagery, facilitating activities such as instance counting, velocity estimation, traffic predictions, etc. The feasibility of accurate vehicle detection often depends on limited training datasets, requiring a lot of manual work in collection and annotation tasks. Furthermore, there are no known publicly available datasets. Our aim was to construct a pipeline for synthetic dataset generation from aerial imagery and 3D models in Blender software. The dataset generation pipeline consists of seven steps and results in a wished number of images with bounding boxes in YOLO and coco formats. This synthetic dataset has been produced following the steps described in this pipeline. It consists of 5000 2048 × 2048 images with cars inserted into the roads and highways at the images without cars from all over the world. We believe that this dataset and the respective pipeline might be of great importance for vehicle detection, facilitating the customizability of the models to specific needs and context.
Dynamic Transitive Closure-Based Static Analysis through the Lens of Quantum Search

Many existing static analysis algorithms suffer from cubic bottlenecks because of the need to compute a dynamic transitive closure (DTC). For the first time, this paper studies the quantum speedups on searching subtasks in DTC-based static analysis algorithms using quantum search (e.g., Grover’s algorithm). We first introduce our oracle implementation in Grover’s algorithm for DTC-based static analysis and illustrate our quantum search subroutine. Then, we take two typical DTC-based analysis algorithms: context-free-language reachability and set constraint-based analysis, and show that our quantum approach can reduce the time complexity of these two algorithms to truly subcubic ( \(O(N^2\sqrt {N}{polylog}(N)) \) ), yielding better results than the upper bound ( O ( N 3 /log N )) of existing classical algorithms. Finally, we conducted a classical simulation of Grover’s search to validate our theoretical approach, due to the current quantum hardware limitation of lacking a practical, large-scale, noise-free quantum machine. We evaluated the correctness and efficiency of our approach using IBM Qiskit on nine open-source projects and randomly generated edge-labeled graphs/constraints. The results demonstrate the effectiveness of our approach and shed light on the promising direction of applying quantum algorithms to address the general challenges in static analysis.
<i>RE-Trace</i> : Re-Identification of Modified GPS Trajectories

GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace – a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all data sets and identifies modified GPS trajectories effectively and efficiently.
Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this paper, we present a novel counterexample-guided abstraction refinement (CEGAR) approach for scalable and exact verification of DNNs. Specifically, we propose a novel abstraction to break down the size of DNNs by over-approximation. The result of verifying the abstract DNN is conclusive if no spurious counterexample is reported. To eliminate each spurious counterexample introduced by abstraction, we propose a novel counterexample-guided refinement that refines the abstract DNN to exclude the spurious counterexample while still over-approximating the original one, leading to a sound, complete yet efficient CEGAR approach. Our approach is orthogonal to and can be integrated with many existing verification techniques. For demonstration, we implement our approach using two promising tools Marabou and Planet as the underlying verification engines, and evaluate on widely-used benchmarks for three datasets ACAS Xu , MNIST and CIFAR-10 . The results show that our approach can boost their performance by solving more problems in the same time limit, reducing on average 13.4%–86.3% verification time of Marabou on almost all the verification tasks, and reducing on average 8.3%–78.0% verification time of Planet on all the verification tasks. Compared to the most relevant CEGAR-based approach, our approach is 11.6–26.6 times faster.
A novel CNN-based method for localization of temperature anomalies in RDTS system

In this study, the effect of convolutional neural network combined with cosine similarity approach on the localization of temperature anomalous events was investigated by designing Raman-based Distributed Temperature Sensing (RDTS) experiments. We first constructed convolutional neural network (CNN) models to extract temperature anomaly features. RDTS experiments were designed to obtain the dataset for training the CNN models. Subsequently, the cosine similarity between the anomalous features and the original signal was calculated to overcome the disadvantage of feature offset. Finally, the feature with the highest cosine similarity was used as the final temperature anomaly event feature. Moreover, another experiment was designed to verify the performance of the proposed method. Based on optical fiber data with a resolution of 1 m, we cut the original signal into data segments with a length of 100 sampling points and used them as input to the CNN model. The main results show that the proposed method has an accuracy of 98.37 %, a precision of 93.53 %, a recall of 75.54 %, and an overall evaluation index F1 score of 0.84 (0.81 for the Copula-based outlier detection method, and 0.75 for the quartile method). The proposed method shows excellent generalization performance on datasets with different signal-to-noise ratios, providing a novel and effective solution for locating temperature abnormal events in RDTS systems.
Mixed Graph Contrastive Network for Semi-Supervised Node Classification

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed M ixed G raph C ontrastive N etwork (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.