Reviewerly/exHarmony huggingface dataset:
Analysis of Ecoliteracy Based Science Learning Resources Needs to Realize SDGs

This research aimed to describe needs of students’ Science learning resources with ecoliteracy at PGSD STKIP Al Hikmah Surabaya. Students, the majority of whom are young, have great potential to realize a more environmentally friendly Indonesia as stated in the Sustainable Development Goals. Research method that is used in this research was qualitative. Data collected by using questionnaires and interviews. Data analyzed descriptively. Research revealed that PGSD students have middle ecoliteracy . Science learning resource that is needed for students was the Science Education Book with Ecoliteracy . Through the development of Ecoliteracy -based Science Education Books, it is hoped that PGSD students will be able to become students who understand and are aware of environmental issues (ecoliterate), and can design learning activities for elementary students that can instill environmental care characters. The book consisted of information about ecoliteracy and teaching and learning in elementary school for enhancing students’ ecoliteracy. The book was also completed with youtube, instagram, and journal links as an additional information.This research aimed to describe needs of students’ Science learning resources with ecoliteracy at PGSD STKIP Al Hikmah Surabaya. Students, the majority of whom are young, have great potential to realize a more environmentally friendly Indonesia as stated in the Sustainable Development Goals.Research method that is used in this research was qualitative. Data collected by using questionnaires and interviews. Data analyzed descriptively. Research revealed that PGSD students have middle ecoliteracy . Science learning resource that is needed for students was the Science Education Book with Ecoliteracy .Through the development of Ecoliteracy -based Science Education Books, it is hoped that PGSD students will be able to become students who understand and are aware of environmental issues (ecoliterate), and can design learning activities for elementary students that can instill environmental care characters. The book consisted of information about ecoliteracy and teaching and learning in elementary school for enhancing students’ ecoliteracy. The book was also completed with youtube, instagram, and journal links as an additional information.
SIMULATION GAME AS AN EDUCATIONAL TECHNOLOGY PROFESSIONAL TRAINING OF SPECIALISTS PUBLISHING AND PRINTING

The article deals with the peculiarities of using such an interactive educational technology as a simulation game in the educational process of a higher education institution. Approaches to defining the essence of this technology are generalized. It has been established that simulation games can be used as a pedagogical technology or one of the methods of active learning, in conducting social and psychological training, in solving production, social and psychological problems that a graduate may encounter in his or her professional activity. The classification of educational simulation games is presented. Their didactic potential is determined and possible options for building and structuring simulation games are revealed. The specifics of the use of simulation games in the professional training of bachelors of publishing and printing in higher education are considered. Their use will facilitate the rapid inclusion of students in the model of professional activity, allow them to take on different functional roles, identify the specifics of their characters' activities, interact in a simulated, but as close to real, production process, and most importantly, see and evaluate the final result of their work. The author's electronic resource with methodical instructions on the use of a simulation game. The experience of using the simulation game in the professional training of bachelors of publishing and printing is presented.
Learning Linear Gaussian Polytree Models With Interventions

We present a consistent and highly scalable local approach to learn the causal structure of a linear Gaussian polytree using data from interventional experiments with known intervention targets.Our methods first learn the skeleton of the polytree and then orient its edges.The output is a CPDAG representing the interventional equivalence class of the polytree of the true underlying distribution.The skeleton and orientation recovery procedures we use rely on second order statistics and lowdimensional marginal distributions.We assess the performance of our methods under different scenarios in synthetic data sets and apply our algorithm to learn a polytree in a gene expression interventional data set.Our simulation studies demonstrate that our approach is fast, has good accuracy in terms of structural Hamming distance, and handles problems with thousands of nodes.
A Topic Model for the Data Web

The usage of knowledge graphs in industry and at Web scale has increased steadily within recent years. However, the decentralized approach to data creation which underpins the popularity of knowledge graphs also comes with significant challenges. In particular, gaining an overview of the topics covered by existing datasets manually becomes a gargantuan if not impossible feat. Several dataset catalogs, portals and search engines offer different ways to interact with lists of available datasets. However, these interactions range from keyword searches to manually created tags and none of these solutions offers an easy access to human-interpretable categories. In addition, most of these approaches rely on metadata instead of the dataset itself. We propose to use topic modeling to fill this gap. Our implementation LODCat automatically creates human-interpretable topics and assigns them to RDF datasets. It does not need any metadata and solely relies on the provided RDF dataset. Our evaluation shows that LODCat can be used to identify the topics of hundreds of thousands of RDF datasets. Also, our experiment results suggest that humans agree with the topics that LODCat assigns to RDF datasets. Our code and data are available online.
GESTURE-BASED HUMAN-COMPUTER INTERACTION

This project explores touchless interaction with computers using gesture recognition technology. By combining computer vision and gesture recognition, users can control their computers using hand gestures. The system uses a webcam for input, processes images with OpenCV, tracks hand movements with MediaPipe, and controls mouse and keyboard actions with PyAutoGUI. Users can perform various gestures like clicks, scrolls, and custom Open Paint gesture to control their computer. The project integrates with a paint application, allowing users to create digital art using their hand gestures, enhancing the overall user experience. Users can seamlessly control their computers by executing predefined gestures in the air, eliminating the limitations of conventional input device like mice. KEYWORDS - Gesture-based interaction, Human-Computer Interaction, MediaPipe, OpenCV, Paint Application, PyAutoGUI, Virtual mouse.
Exploring Low-Code Development: A Comprehensive Literature Review

Low-code development provides the ability to create software using visual application development tools, predefined components, and automation, minimizing the reliance on manual coding. Furthermore, it enables individuals with limited programming expertise to actively contribute to software development in collaboration with information technology (IT) professionals, improving the alignment between customer or business requirements and the developed solution. This approach speeds up the development process, fosters closer collaboration between software development and business teams, and enhances the organization’s ability to respond to business and market demands or changes. In this article, existing knowledge of low-code development from multiple perspectives is summarized, including the definitions, used tools, applied development lifecycles, application areas, potential benefits, challenges, and, consequently, relevant development and delivery principles.
GraphFed: A Personalized Subgraph Federated Learning Framework for Non-IID Graphs

Recently, federated graph learning has attracted significant attention, as subgraphs of a global graph may often distribute across different institutions and are subject to privacy restrictions. However, inevitable data heterogeneity among sub-graphs can lead to poor performance of Graph Neural Networks (GNNs) trained by vanilla federated learning algorithm, which has been neglected before. To address this problem we look at subgraphs and find that there are usually overlapping nodes among them. Inspired by the common overlapping nodes among subgraphs, we propose GraphFed, a subgraph federated learning framework which solve the challenge in terms of both data augmentation and personalized federated learning. GraphFed uses common overlapping nodes among subgraphs to locate lost node information and applies data augmentation by retrieving lost nodes and forming common node sets that mitigates data distribution difference. Furthermore, GraphFed treats the overlapping nodes as bridges to calculate the distance among different subgraphs, which implies the similarity of the graph data distribution. Then, we use the distance to perform personalized federated learning. Empirical results and analysis on three real-world graph datasets with graph federated learning settings demonstrate the effectiveness of our proposed framework.
A comparative study for nuclei segmentation using latest deep learning optimizers

Nuclei segmentation is a critical task in biological image analysis, with numerous applications in cancer diagnosis, grading, staging, and treatment planning. However, this task is challenging, particularly when dealing with low-resolution and low signal-to-noise ratio microscopy images. Segmentation problems arise, such as touching and missing cells, which make the process even more challenging. Deep learning models, including Attention U-Net and TransUNet, have demonstrated exceptional performance in medical image segmentation. Nonetheless, the choice of optimizer can significantly impact the model’s performance, especially in nuclei segmentation. In this work, we assessed the efficiency of the latest deep learning optimizers, such as Ranger and GC, on Attention U-Net and TransUNet models and compared their performance with traditional optimizers, i.e., Adam. We conducted experiments on the publicly available Fluo-N2DH-HeLa dataset that was adapted from the cell tracking challenge. The Ranger optimizer, along with the TransUNet model, achieved the highest mean IOU of 95.05% and reasonable recall and precision scores of 94.4% and 96.8%, respectively. The experimental results proved that integrating the TransUNet model with the Ranger optimizer significantly enhanced touching nuclei segmentation performance in fluorescence microscopy images.
Towards the On-Demand Whole Slide Image Generation: Prostate Patch Synthesis Through a Conditional Progressive Growing GAN

Prostate cancer is a common disease that affects men, and its diagnosis and prognosis rely on the Gleason scoring system. To automate this process, generative deep learning models can be used to synthesize histopathological tissue patches of non-cancerous and malignant patterns. This work proposes a conditional Progressive Growing GAN to generate synthetic samples by selecting the desired pattern. The model is trained using conditional information about the pattern, and minibatch standard deviation and pixel normalization are used to improve performance and stability. The synthetic samples are assessed using the Frechet Inception Distance (FID). Finally, the proposed framework was applied to the SICAPv2 dataset, and the results showed that data augmentation with our method improved the classification accuracy obtained on this dataset. This demonstrates the effectiveness of the proposed framework as a data augmentation method. Overall, this study provides a promising approach to address the issue of insufficient and unbalanced datasets in prostate cancer diagnosis and prognosis, which can improve the accuracy and reliability of clinical decision-making.
Internet of Things System for Solar Resource Monitoring

Photovoltaic (PV) solar energy is a sustainable and clean source of energy that uses solar radiation to produce electricity. Although an environmental friendly source of energy, it is prone to irregular variations due to atmospheric factors that can cloud the Sun. That is why data acquisition systems (DAQ) are required to monitor closely these atmospheric changes, be they even short lasting. This paper proposes an Internet of Things (IoT) data acquisition system used for solar resource monitoring. In order to ascertain the performance ratio (defined as the produced energy divided by the potential available energy) of a solar plant, meteorological measurements are required. Among these atmospheric parameters, the global horizontal irradiance (GHI) quantifies well the potential energy that can be harvested from the Sun, and can be obtained using a pyranometer. Along with a low-cost All Sky Imager (ASI) used for sampling sky images with a frequency of one minute (for dataset building) for short term irradiance prediction, and a Raspberry Pi 4B as the central computing unit, a low cost IoT system is constructed. This data acquisition system for monitoring solar resources can be used in a PV power plant to analyze and potentially predict short term irradiance variations caused by cloud cover.
Identifying Linguistic and Speech Acts Variations of the Buginese Tribe Through Socio-Pragmatic Analysis in Establishing Daily Discourse Interactions

Buginese is a local language in South Sulawesi, Indonesia, which is spoken in almost 60% of Sulawesi Island. This study aimed to explore the selection of linguistic variations and language acts as a linguistic model used by ethnic Buginese in establishing discourses of everyday socio-pragmatic interactions. It relied on a qualitative design to obtain the linguistic corpus that occurs empirically and naturally in communication interactions. This study used the snowball method by combining ethnographic strategies. The instrument used the Speaking Involvement Technique, in which the researchers were directly involved in people’s interactions by paying attention to communicating situations, recording data, and conducting interviews for data validation purposes. All data or corpus of linguistic variations and speech acts collected were analyzed by the speaking strategy componential technique strategy developed by Dell Hymes. This study successfully discovers a model for identifying the Buginese ethnicity's linguistic variations and speech acts to build a discourse of communication interactions. The expressions of selection of linguistic variations and speech acts are dominantly based on polite sentence constructions and formulations, and ethically according to the local culture of Buginese ethnicity, namely the culture of siri. The results of this study can affect the enrichment of scientific information, which functions and acts as a meaningful reference in the field of linguistic studies, especially examining speech act variation in the expression of conversational discourse, in addition to being a foundation in the development of local content material and multicultural understanding.
DSEN: A Distance-Based Semantic Enhancement Neural Network for Pronoun Anaphora Resolution

Anaphora resolution is one of the fundamental tasks in natural language processing, aiming to identify the specific entities referred to by pronouns or noun phrases. In recent years, with the advancement of deep learning, the performance of anaphora resolution has significantly improved. To accurately identify the entities referred to by pronouns based on context, various factors need to be considered, such as the semantic dependencies in the context, the positions of entity words and pronouns, among others. However, recent research has lacked consideration of the impact of word distances on the correlation between word pairs. In this paper, we propose a anaphora resolution model called DSEN, which stands for Distance-based Semantic Enhancement Neural Network for Pronoun Anaphora Resolution. Our model integrates explicit positional relationships between entity words and pronouns, key meanings, and semantic information. By considering the influence of word distances, our model effectively incorporates the dependencies between word pairs. The fusion of these factors empowers our model to achieve outstanding results on the CLUEWSC2020 anaphora resolution dataset.
TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs

Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TemporalFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TemporalFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TemporalFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC .
Reinforcement Learning-based Answer Selection with Class Imbalance Handling and Efficient Differential Evolution Initialization

Answer selection (AS) involves the task of selecting the best answer from a given list of potential options. Current methods commonly approach the AS problem as a binary classification task, using pairs of positive and negative samples. However, the number of negative samples is usually much larger than the positive ones, resulting in a class imbalance. Training on imbalanced data can negatively impact classifier performance. To address this issue, a novel reinforcement learning-based technique is proposed in this study. In this approach, the AS problem is formulated as a sequence of sequential decisions, where an agent classifies each received instance and receives a reward at each step. To handle the class imbalance, the reward assigned to the majority class is lower than that for the minority class. The parameters of the policy are initialized using an improved Differential Evolution (DE) technique. To enhance the efficiency of the DE algorithm, a novel cluster-based mutation operator is introduced. This operator utilizes the K-means clustering approach to identify the winning cluster and employs an upgrade strategy to incorporate potentially viable solutions into the existing population. For word embedding, the DistilBERT model is utilized, which reduces the size of the BERT (Bidirectional encoder representations from transformers) model by 40% and improves computational efficiency by running 60% faster. Despite the decrease, the DistilBERT model maintains 97% of its language comprehension abilities by utilizing knowledge distillation in the pretraining phase. Extensive experiments are carried out on LegalQA, TrecQA, and WikiQA datasets to assess the suggested model. The outcomes showcase the superiority of the proposed model over existing techniques in the domain of AS.
Estimation of True Scores, True score variance, Reliability and tests of parallelism

Under classical test theory the paper presents a method to find test reliability (
Improved GCC Technique: A Comprehensive Approach to Color Cast Rectification and Image Enhancement

The domain of underwater imaging is riddled with multifarious challenges, such as light attenuation, scattering, and color distortion, which can have a detrimental impact on the quality of images. In order to address these challenges, the Generalized Color Compensation (GCC) technique has been introduced, which utilizes color compensation and color mean adjustment to rectify color cast while integrating contrast enhancement via the Contrast Limited Adaptive Histogram Equalization (CLAHE). Nevertheless, the performance of GCC is limited due to the production of bright and smooth images. To overcome this challenge, we have introduced the improved GCC approach, which employs color compensation and color mean adjustment to rectify color cast. Subsequently, a contrast-enhanced image is generated through CLAHE to improve image contrast, while the detail-enhanced image is produced via a cumulative distribution function. Furthermore, image fusion between the detail-enhanced and contrast-enhanced images yields a superior-quality image. Our experimental results demonstrate the effectiveness of our proposed technique in improving the visual quality of underwater images. Objective metrics such as Underwater Image Quality Measure (UIQM) demonstrate that our technique surpasses GCC in terms of image sharpness, colorfulness, and contrast.
Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission

Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance. The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost.