Reviewerly/exHarmony huggingface dataset:
A Learning Model for Acute Myeloid Leukemia Prediction Using Dense Polynomial Dimensionality-Based Predictor

Analysis of microarray data is extremely complex and considered as a hot topic in recent research. Acute Myeloid Leukemia (AML) prediction based on machine learning shows huge impact on prediction which automatically diagnoses the disease severity and any malfunctions. It is important to design the relevant classifier that processes the large data volume with large data size. Deep learning is an updated machine learning approach for mitigating these issues. It is easy to handle the huge volume of data because of the large number of hidden layers. The proposed classification methodology is used for understanding the training of the proposed Dense Polynomial Dimensionality based Predictor Model (). The hidden neuron numbers are large in a sufficient way where the proposed is elaborated to predict AML. AML and ALL samples are classified using five layers in the deep network model. The data is partitioned as 20% data and 80% data testing and training in the network. Compared with other classifiers, the satisfying outcome from the proposed is higher and fulfilling. The validation is done in three datasets: Kaggle, Gene expression and Bio GPS and it gives 96% accuracy, 94% precision, 96% recall, 96% F1-score, and 98% AUROC while executing with Kaggle; then, 95.50% accuracy, 94% precision, 95% recall, 96% F1-score, and 96% AUROC is achieved while executing with Gene expression and finally 98% accuracy, 94.5% precision, 98.5% recall, 96% F1-score, and 94% AUROC is achieved while executing with Bio GPS. Based on this analysis, it is proven that the model works well with the proposed and establishes a better trade-off.
Prathamik Shikshan Padwika Abhayaskramatil (D.El.Ed) Aakarik Mulyamapan Pranali.

"The future of the nation is taking shape in the classroom. This statement of the Kothari Commission report is familiar to all. The important burden of shaping the future rests on the shoulders of teachers. Therefore, inculcating values and developing skills in the student teacher who aspires to be a teacher is of utmost importance. -The State Teacher Training Board has been responsible for the development of suitable pre-service courses since 1968. Diploma in Primary Education course is a milestone in this development.
Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Semantic segmentation is an important imaging analysis method enabling the identification of tissue structures. Histological image segmentation is particularly challenging, having large structural information while providing only limited training data. Additionally, labeling these structures to generate training data is time consuming. Here, we demonstrate the feasibility of a semantic segmentation using U-Net with a novel sparse labeling technique. The basic U-Net architecture was extended by attention gates, residual and recurrent links, and dropout regularization. To overcome the high class imbalance, which is intrinsic to histological data, under- and oversampling and data augmentation were used. In an ablation study, various architectures were evaluated, and the best performing model was identified. This model contains attention gates, residual links, and a dropout regularization of 0.125. The segmented images show accurate delineations of the vascular structures (with a precision of 0.9088 and an AUC-ROC score of 0.9717), and the segmentation algorithm is robust to images containing staining variations and damaged tissue. These results demonstrate the feasibility of sparse labeling in combination with the modified U-Net architecture.
Situs Megalitik Tanjung Telang, Kabupaten Lahat: Kajian Bentuk Dan Fungsi.

Abstract&#x0D; This article is entitled Megalithic site Tanjung Telang, Lahat Regency: Study of Form and Function. This article aims to examine the from and function at the Tanjung Telang megalithic site, Lahat Regency, South Sumatra Province. This article uses the historical research method (historical method). This article uses the following stages: heuristics, source criticism, interpretation and historiography. The results of the research in this article say that in Lahat Regency there are many thousands of megalithic sites with various unique shapes and different sizes and have a certain meaning for each site found. Lahat Regency has a hilly area and a very fertile area, namely Bukit Selero which was trusted by the people in the past as a reminder that there is still a God above humans so humans must remain humble. Tanjung Telang Village, West Merapi District, Lahat Regency, South Sumatra, Found megalithic cultural relics inthe form of the Tanjung Telang Human Statue, Megalithic Flat Stone and Menhirs. The heritage site in the form of megalithic culture is located with in the environment of West Merapi 2 Junior High School, in the highlands (hills), shrubs, and is located in the community’s coffe garden.&#x0D;
Prediksi Parameter Kelembapan Udara Berdasarkan Data Penyinaran Matahari Menggunakan Metode Aproksimasi Kuadrat Terkecil

Humidity is the measure, generally expressed as a percentage, of water vapor that presents in the air. Each place has different percentage of humidity. It happens since humidity is affected by solar radiation intensity. In this study, the percentage of relative humidity is being predicted by applying least square method and Gauss elimination. The data used in this research is the data of relative humiditity and solar radiation intensity during 2018 until 2022 which have been collected by Trunojoyo Stationary of Meteorology. The result shows that the approximation function generated by linear square method is powerful enough in order to predict the relative humidty, based on the relatively small error accumulated.
The Effect of Family Welfare on Students' Cognitive Abilities

Education is a basic need that must be obtained by everyone. To measure student success, one of which can be seen through students' cognitive abilities. This study aims to determine the effect of family welfare on students' cognitive abilities using the test score approach. This study uses data from the Indonesian Family Life Survey (IFLS5). The statistical method used in this research is multiple linear regression analysis. This research revealed that family welfare plays a very important role in students' cognitive abilities. Diverse family welfare results in disparities between students. Therefore, a program is needed to overcome the welfare gap so students can obtain optimal quality education.
Augmented reality in teaching and learning English as a foreign language: A systematic review and meta-analysis

This study provides implications on the application of augmented reality (AR) in education through a review of research papers focused on a type of XR technology, identifying and describing its practical application using examples drawn from different contexts in facilitating learning English as a foreign language (EFL). Specifically, we conducted a systematic review and meta-analysis to bring together information on how available tools employing XR technology can be used to enhance English language learning. This study focuses on providing information for a better understanding of AR-based learning and its integration in both classroom and personal customized learning of EFL. The review aligns with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement (PRISMA) guidelines. Literature was searched from ACM Digital Library, IEEE Xplore, PubMed, ERIC, the Cochrane CENTRAL library, and Google Scholar. The studies reviewed identified three major AR game-based strategies used in language learning: augmented word spelling games, card-based word visualization activities, and word annotations or word spelling games. The meta-analysis yielded a random effect size of 8.46 [−1.93, 18.85] at a 95% confidence interval but found no significant effect (z = 1.60 (p = 0.11)). An AR system can effectively teach students of all ages since it enhances engagement in learning. Additional research is necessary to further inform learners and trainers on the full extent of the implementation of AR technology in learning.
The Relationship Between Primary School Teachers’ Perceptions of 21st Century Skills And Digital Literacy Level

In the 21st century, individuals are exposed to technology from the moment they are born. They are expected to master 21st century skills and digital literacy in order to use technology appropriately, safely, and effectively and keep up with the times. The 21st century skills and digital literacy of primary school teachers, who form the basis of individuals' learning and lead them, are of great importance. For this reason, in this study, primary school teachers’ perceptions of 21st century skills proficiency, their digital literacy levels, and the relationship between them were examined. A relational survey model was used. The “21st Century Skills and Competences Scale” and “Digital Literacy Scale” were used in data collection. The results showed that the participants’ 21st century skills proficiency and digital literacy levels were high. No significant difference was found in the 21st century skills proficiency perceptions of the participants with regard to gender or work experience. In addition, a significant difference was found in favor of male participants in digital literacy levels, whereas the digital literacy levels of the participants did not differ by work experience. Furthermore, there was a moderate, positive, and significant relationship between the participants’ perceptions of 21st century skills proficiency and their digital literacy levels. Finally, the results of the simple linear regression analysis, revealed that the digital literacy levels of the participants were an important predictor of 21st century skills.
Analysis Optimization of Game “X” Performance on The Android Platform

The preliminary research entitled “Game Prototype Performance Analysis on The Android Platform” stated that the game “X” (The name was disguised for development purposes), was not optimized very well. The game needs to be researched further to improve the process of optimization and to find the resu
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering (Extended abstract)

Recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs). However, two important issues have been overlooked. First, the accumulative error, inflicted by learning from noisy clustering assignments, degrades the model’s effectiveness. This problem is called Feature Randomness (FR). Second, reconstructing the adjacency matrix sets the model to learn irrelevant similarities for the clustering task. This problem is called Feature Drift (FD). To address these issues, we first propose a sampling operator that triggers a protection mechanism against FR. second, we propose an operator Υ that triggers a correction mechanism against FD by transforming the reconstructed graph. Experimental results validate that our operators alleviate these problems and bring significant clustering improvement.
Obstacle Avoidance System Using LiDAR on Robot Turtlebot3 Burger

The Burger Robot Turtlebot3 series is a standard platform from Robotics Corporation's ROS, which is used as a learning medium and as a prototype in a robot delivery project. The Turtlebot3 Burger robot is used as a learning medium in the application of the goods delivery robot system. In this resear
ZOMETAG: Zone-Based Memory Tagging for Fast, Deterministic Detection of Spatial Memory Violations on ARM

Against spatial memory violations threatening a vast amount of legacy software, various safety solutions have been suggested for decades. However, their practical uses have been impeded by diverse reasons, such as significant overheads and mandatory modifications of existing architectures. Accordingly, there has been a clear need for a practical safety solution that is fast enough and yet runs on commodity systems for its wide applicability in the field. As an effort to meet this need, a major processor vendor, ARM, recently announced a hardware extension, called Memory Tagging Extension (MTE), that helps engineers to implement efficient safety solutions. However, due to lack of hardware tags to isolate all data objects, MTE either resorts to a probabilistic memory safety guarantee, which is susceptible to a security loophole, or suffers from severe performance degradation to guarantee deterministic security. The aim of our work is to develop a MTE-based deterministic spatial safety solution, called ZOMETAG, with high efficiency by capitalizing on salient architectural features. Our key idea for fast, deterministic safety is to somehow assign permanently all objects unique tags throughout program execution. For this, ZOMETAG first divides the data memory into a number of small regions, called zones, and distributes data objects over the zones subject to certain constraints (to be discussed later). Then, we extend the notion of a tag in a way that each object stored with MTE tag <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> in zone <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> is uniquely assigned the zone-tag pair < <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> > as a new tag. To work with this new tag assignment, we devise a novel mechanism, called two-layer isolation, that is basically a combination of MTE-based tagging (for one-layer of isolation) with zone-based tagging (for the other) both of which collaborate together to ensure spatial safety for all objects by preventing a pointer currently assigned one zone-tag pair from erroneously referring to objects assigned different pairs. Our experimental results are quite encouraging. ZOMETAG enforces deterministic spatial safety with overheads of 35% in SPEC CPU2006 and merely of 6% in real world applications like nginx.
Analisis Efektivitas Daya Tampung Embung Taman Firdaus Universitas Sriwijaya

Floods occur due to reduced water catchment areas due to increased land use, both for settlements and economic activities. The purpose of this research is to analyze the storage capacity of the embankment. The Firdaus Taman Firdaus Reservoir of Sriwijaya University (UNSRI) is located in the Ogan Ilir district and is useful as a place for rainwater storage and flood control. Embung has many benefits to meet the water needs of UNSRI. The UNSRI reservoir has an area of ​​24 hectares, the west reservoir is 8 m from the bottom and 50 m long. Results of data analysis, the volume of the reservoir with a depth of 5 m with an area of ​​pond 1 covering an area of ​​423804.79 m2 can accommodate water with a storage volume capacity of 2119023.95 m3. The results of testing the average water content in the original soil (w) = 87.38 %. The test results for the average soil specific gravity (Gs) are 2.19. The results of the Atterberg limit test, the liquid limit value in soil (LL) = 57.00 %, the plastic limit in soil (PL) = 48.37 %, and the plasticity index in soil (IP) = 8.63 %. Based on the AASTHO classification, the soil in the UNSRI Firdaus Taman Embung belongs to group A-2-5, as silt or loamy gravel and sand. Meanwhile, based on the USCS classification, it is classified as Silts and Clays (CH). The results of the permeability test are the permeability value (K) = 3.034191 x 10-5 cm/s, which is low permeability.
Automation and integration in SCADA system of the wastewater treatment plant in Cantemir, Moldova

In this paper, the problem was the realization of a SCADA system (Supervisory Control and Data Acquisition) and an automation system for the system of the wastewater treatment plant in Cantemir, Moldova. The use of innovative treatment methods based on revolutionary industrial principles, such as Imhoff, reduces maintenance costs, increases energy efficiency and contributes to improving the quality of the water that is fed by the people who live in this city. Following the implementation of the SCADA automated system, water remains free of microorganisms, parasites or substances that, by number or concentration, constitute a potential danger to human health, and energy efficiency in an automation system is an important point that is analyzed and implemented in each electromechanical system separately, to reduce system maintenance costs and increase flexibility.
GAN-based intrusion detection model using MLP encoder

As the number of network attacks continues to rise, effective intrusion detection systems (IDSs) have become crucial for maintaining network security. Traditional machine learning (ML) methods have achieved good results in intrusion detection, but are often limited by insufficient training data and vulnerability to adversarial attacks. To address these limitations, we propose a neural network model based on Generative Adversarial Networks (GAN). GAN have achieved remarkable success in detecting such adversarial attacks, mainly due to the adversarial training of generators and discriminators in an attempt to bypass each other and thereby improve their own capabilities and accuracy. Furthermore, the generator of GANs can generate fake samples that are similar to real data, thereby alleviating the problem of insufficient training data. Our experimental results demonstrate that our proposed IDS model effectively detects network attacks with high accuracy. We believe that this approach represents a promising direction for future research in the field of network security.
Counterfactual learning in enhancing resilience in autonomous agent systems

Resilience in autonomous agent systems is about having the capacity to anticipate, respond to, adapt to, and recover from adverse and dynamic conditions in complex environments. It is associated with the intelligence possessed by the agents to preserve the functionality or to minimize the impact on functionality through a transformation, reconfiguration, or expansion performed across the system. Enhancing the resilience of systems could pave way toward higher autonomy allowing them to tackle intricate dynamic problems. The state-of-the-art systems have mostly focussed on improving the redundancy of the system, adopting decentralized control architectures, and utilizing distributed sensing capabilities. While machine learning approaches for efficient distribution and allocation of skills and tasks have enhanced the potential of these systems, they are still limited when presented with dynamic environments. To move beyond the current limitations, this paper advocates incorporating counterfactual learning models for agents to enable them with the ability to predict possible future conditions and adjust their behavior. Counterfactual learning is a topic that has recently been gaining attention as a model-agnostic and post-hoc technique to improve explainability in machine learning models. Using counterfactual causality can also help gain insights into unforeseen circumstances and make inferences about the probability of desired outcomes. We propose that this can be used in agent systems as a means to guide and prepare them to cope with unanticipated environmental conditions. This supplementary support for adaptation can enable the design of more intelligent and complex autonomous agent systems to address the multifaceted characteristics of real-world problem domains.
Disentangling private classes through regularization

Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. However, little attention has been devoted to connected legal aspects. In 2016, the European Union approved the General Data Protection Regulation which entered into force in 2018. Its main rationale was to protect the privacy and data protection of its citizens by the way of operating the so-called “Data Economy”. As data is the fuel of modern Artificial Intelligence, it is argued that the GDPR can be partly applicable to a series of algorithmic decision-making tasks before a more structured AI Regulation enters into force. In the meantime, AI should not allow undesired information leakage deviating from the purpose for which is created. In this work, we propose DisP, an approach for deep learning models disentangling the information related to some classes we desire to keep private, from the data processed by AI. In particular, DisP is a regularization strategy de-correlating the features belonging to the same private class at training time, hiding the information about private class membership. Our experiments on state-of-the-art deep learning models show the effectiveness of DisP, minimizing the risk of extraction for the classes we desire to keep private.
Depth-Constrained Network for Multi-Scale Object Detection

Challenges such as complex backgrounds, drastic variations in target scales, and dense distributions exist in natural scenes. Some algorithms optimize multi-scale object detection performance by combining low-level and high-level information through feature fusion strategies. However, these methods overlook the inherent spatial properties of objects and the relationships between foreground and background. To fundamentally enhance the multi-scale detection capability, we propose a depth-constrained multi-scale object detection network that simultaneously learns object detection and depth estimation through a unified framework. In this network, depth features are merged into the detection branch as auxiliary information and constrained and guided to obtain better spatial representations, which enhances discrimination between multi-scale objects. We also introduce a novel cross-modal fusion (CmF) strategy that utilizes depth awareness and low-level detail clues to supplement edge information and adjust attention weight preferences. We find complementary information from RGB and high-quality depth features to achieve better multi-modal information fusion. Experimental results demonstrate that our method outperforms state-of-the-art methods on the KINS dataset, with an improvement of 3.0% in AP score over the baseline network. Furthermore, we validate the effectiveness of our proposed method on the KITTI dataset.
Organic Molecules for Photonic Quantum Technologies: an Insight into Two-Photon Interference from Distinct Emitters

The time trace of two-photon interference from distinct molecular emitters in the solid-state is analyzed to extract their spectral difference without resorting to fitting. This opens the path towards robust on-line monitoring of quantum emitters.