Reviewerly/exHarmony huggingface dataset:
Information Technology-Based Educational Video Model For Changes In Gingival Health Status In Adolescents

The health problem that is often experienced by adolescents is dental and oral hygiene, RISKESDAS data for 2018, 57.6% of Indonesian people have dental and oral health problems. Gingival problems are common in adolescents. Lack of dental and oral hygiene causes gingival inflammation. Plaque, bacteria, and calculus that accumulate on the tooth surface are the main causes of periodontal disease. Early detection that is carried out independently as well as providing solutions for prevention and treatment to patients is very necessary. Media that is currently developing among adolescents is technology-based media which is increasingly creative and can be a solution for preventing dental disease. Proving appropriate information technology-based educational videos and their effective application to changes in gingival health status in adolescents. The research method used is Research and Development (RnD) with Quasy Experiment pre-test post-test control group design. Educational video based on information technology is appropriate for changes in gingival health status with the results of expert validation of 83% and p-value 0.044 and its application is effective in increasing knowledge (p=0.000), attitude (p=0.002), skills (p=0.000), GI (p = 0.001). As well as indicated by the delta value of knowledge (2.14), attitude (1.29), skills (2.14), decreased GI score (0.44) compared to the control group. Educational videos have proven to be appropriate and effective in their application to changes in gingival health status in adolescents marked by a decrease in the gingival index score.
Perkembangan Pembelajaran Bahasa Indonesia dan Upaya Penguatannya dalam Sistem Pendidikan Nasional

This study aims to determine the development of Indonesian language learning and efforts to strengthen it in the national education system. This research is a type of qualitative descriptive research using the library research method (literature study), looking for sources of relevant literature according to the topics discussed, with the aim of finding scientific and theoretical studies. This study uses data analysis techniques which consist of three stages, namely (1) Organize, at this stage grouping the literature studied. Literature must first be reviewed before being used, so that it is in accordance with the subject matter. (2) Synthesize, at this stage, combines the results of grouping literature in a concise and cohesive manner. (3) Identify, at this stage identify issues that are relevant and important to study and analyze, in order to produce scientific paragraphs. From the description of the results of the data analysis, it can be concluded that the development of Indonesian language learning in Indonesia continues to change according to the curriculum used. Starting from the 1947 curriculum to the independent curriculum currently used in schools. Strengthening the Indonesian language in the national education system includes language that educates the life of the nation and the position and function of the Indonesian language.
Finding Partial Periodic and Rare Periodic Patterns in Temporal Databases

Most of the periodic pattern mining algorithms extract fully periodic patterns by strictly monitoring the cyclic behaviour of patterns in transactional as well as temporal databases. The most recent and preferred method for discarding non-periodic uninteresting patterns is partial periodic pattern mining, which has control over the strictness measure on cyclic repetitions of patterns. Recently, a variety of industries, including fraud detection, telecommunications, retail marketing, research, and medical have found applications for rare association rule mining, which uncovers unusual or unexpected combinations. A limited amount of literature demonstrated how periodicity is essential in mining low-support patterns. However, time of occurrence is also a vital phrase that is ignored which further aids in significant information retrieval. With this inspiration, a novel depth-first search framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3P-BitVectorMiner</i> , is proposed to extract entire partial periodic patterns from a temporal database. Experiments are carried out by varying support and periodicity thresholds for a variety of datasets. It is found that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3P-BitVectorMiner</i> consistently displays greater performance over the state-of-the-art algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3P-Growth</i> . Further, the scalability of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3P-BitVectorMiner</i> algorithm is also presented to demonstrate the efficiency over the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3P-Growth</i> algorithm on large temporal databases. In addition, two variations named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RFPP-BitVectorMiner</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R3P-BitVectorMiner</i> are proposed to mine rare fully periodic patterns and rare partial periodic patterns from temporal databases respectively. Different experiments carried out show that these proposed frameworks successfully capture periodic uncommon patterns in temporal databases.
Entanglement, Quantum Correlators, and Connectivity in Graph States

In this work, we present a comprehensive exploration of the entanglement and graph connectivity properties of graph states. We quantify the entanglement in pseudo graph states using the entanglement distance, a recently introduced measure of entanglement. Additionally, we propose a novel approach to probe the underlying graph connectivity of genuine graph states, using quantum correlators of Pauli matrices. Our findings also reveal interesting implications for measurement processes, demonstrating the equivalence of certain projective measurements. Finally, we emphasize the simplicity of data analysis within this framework. This work contributes to a deeper understanding of the entanglement and connectivity properties of graph states, offering valuable insights for quantum information processing and quantum computing applications. In this work, we do not resort to the celebrated stabilizer formalism, which is the framework typically preferred for the study of this type of state; on the contrary, our approach is solely based on the concepts of expectation values, quantum correlations and projective measurement, which have the advantage of being very intuitive and fundamental tools of quantum theory.
Challenges in Rendering Arabic Text to English Using Machine Translation: A Systematic Literature Review

The Arabic text can be translated into English using a variety of machine translation techniques. The translation of Arabic text into English still poses a number of challenges in contemporary Arabic. To identify these challenges that encounter while translating Arabic text into English using machine translation, a systematic literature review (SLR) approach is used. The SLR steps—protocol creation, first and final selection, quality assessment, data extraction and synthesis—are used. Nineteen challenges are reported during the SLR process based on fifty-six research papers. The four most important problems are carefully examined, and the possible solutions of other researchers are discussed. Word sense disambiguation, Arabic named entity, rich and complex morphology and low resource are the four critical challenges during rendering Arabic text to English text. Other challenges are also reported in this article.
Web 3.0 Revolution in the Finance Industry

The emergence of Web 3.0, or the decentralized web, has the potential to transform the finance industry. Web 3.0 introduces new technologies such as blockchain, decentralized apps, and smart contracts that enable secure, transparent, and decentralized financial transactions. This can reduce the need for intermediaries, decrease transaction costs, and enhance the speed and efficiency of financial processes. Additionally, Web 3.0 allows for the creation of new financial products and services that were previously impossible, such as decentralized exchanges, NFTs, peer-to-peer lending platforms, and decentralized autonomous organizations. These innovations could democratize access to financial services and provide new opportunities for individuals and businesses alike. However, Web 3.0 also poses challenges for regulators and financial institutions in terms of risk management, compliance, and adapting to new business models.
Blockchain for Social Impact

As customer demand and regulations drive the coffee industry towards greater traceability and sustainability, coffee farmers in producing countries continue to face challenges in capturing the intangible value-added in their products. This chapter examines the implementation of blockchain technology to address economic and social challenges faced by producers in the coffee industry. The findings demonstrate that blockchain implementation holds promise in reducing information asymmetry in the supply chain, establishing stronger customer-farmer connections, and generating various economic and social benefits for coffee farmers. However, blockchain-enabled third-party verification remains relatively uncommon in current practice. Several challenges must be effectively addressed to realize the full potential of blockchain and foster a more transparent, equitable, and sustainable coffee supply chain.
Physical-Property Guided End-to-End Interactive Image Dehazing Network

Single image dehazing task predicts the latent haze-free images from hazy images corrupted by the dust or particles existed in atmosphere. Notwithstanding the great progress has been made by the end-to-end deep dehazing methods to recover the texture details, they usually cannot effectively preserve the real color of images, due to lack of constraint on color preservation. In contrast, atmospheric scattering model based dehazing methods obtain the restored images with relatively rich real color information due to unique physical property. In this paper, we propose to seamlessly integrate the properties of physics-based and end-to-end dehazing methods into a unified powerful model with sufficient interactions, and a novel Physical-property Guided End-to-End Interactive Image Dehazing Network (PID-Net) is presented. To make full use of the physical properties to extract the density information of haze maps for deep dehazing, we design a transmission map guided interactive attention (TMGIA) module to teach an end-to-end information interaction network via dual channel-wise and pixel-wise attention. This way can refine the intermediate features of end-to-end information interaction network, and do it a favor to obtain better detail recovery by sufficient interaction. A color-detail refinement sub-network further refines the dehazed images with abundant color and image details to obtain better visual effects. On several synthetic and real-world datasets, our method consistently outperforms other state-of-the-arts for detail recovery and color preservation.
A New Multi-Class Rebalancing Framework for Imbalance Medical Data

Class imbalance exists in many data domains, posing numerous challenges to the data research community. Medical datasets, in most cases are predominantly imbalanced in nature. Through tackling multi-class issues, most researchers preferred the conventional method of decomposing it into binary classes for a more convenient solution. This method is not applicable for solving sensitive and crucial domains, such as medical data. Classifying medical datasets require all the classes to retain their form and maintain clinical validity. In this article, we develop a rebalancing framework for the multi-classification of imbalanced medical data using SCUT (SMOTE and Cluster-based Undersampling Technique) to rebalance the imbalanced class distribution, a feature selection method using a combination of SHapley Additive exPlanations (SHAP) and Recursive Feature Elimination (RFE), and DES-MI (Dynamic Ensemble Selection for multi-class) for improved multi classification performance. There are two novelties that contributes to the performance of our framework: improvised SCUT by implementing two clustering algorithms, and our proposed pool classifier selection for DES-MI. The performance of the proposed framework was compared with other state-of-the-art imbalanced framework using eight imbalanced datasets, each with varying degrees of imbalance. The experimental results indicate that our proposed framework performed better with average performance of 81.77%, 73.57%, and 75.87% in terms of Macro Average accuracy, extended G-mean, and Macro Average AUC, respectively. Our framework drastically increases the overall performances, owing to its ability to significantly handles the multi-class imbalance problem.
Hybrid Attention Mechanism and Forward Feedback Unit for RGB-D Salient Object Detection

RGB-D saliency object detection (SOD) is an important pre-processing operation for various computer vision tasks and has received much attention in recent years. However, how to extract more effective features and how to effectively fuse RGB and depth modality features are still challenges that restrict the development of SOD. In this paper, we propose an effective network architecture called FFMA-Net: 1) We replace the backbone network of the baseline with a ResNet34 model to extract more effective features from the input data; 2) We design the HAM module to refine the features extracted by the ResNet34 model at different stages to ensure the effectiveness of features from each stage; 3) We propose the FFU module to perform multi-scale fusion of features from different stages, resulting in more semantic-rich features that are crucial for the decoding stage of the model. Finally, our model performs better than the latest methods on six RGB-D datasets on all evaluation metrics, especially in terms of F-measure metric, which shows significant improvement with approximately 5% on both SSD and LFSD datasets.
Investigating the Transferability of YOLOv5-Based Water Surface Object Detection Model in Maritime Applications

Object detection on the water surface is crucial for unmanned surface vehicles in maritime environments. Despite the challenges posed by variable lighting and ocean conditions, advancements in this field are necessary. In this paper, we investigate the transferability of YOLOv5-based water surface object detection models in cross-domain scenarios. The evaluation is based on publicly available datasets and two newly proposed datasets, Taihu Trial Dataset(TTD) and Fuxian Trial Dataset(FTD), which contain similar target classes but distinct scene and features. Results from extensive experiments indicate that zero-shot transfer is challenging, but a limited number of samples from the target domain can greatly enhance model performance.
PEER: Empowering Writing with Large Language Models

Abstract The emerging research area of large language models (LLMs) has far-reaching implications for various aspects of our daily lives. In education, in particular, LLMs hold enormous potential for enabling personalized learning and equal opportunities for all students. In a traditional classroom environment, students often struggle to develop individual writing skills because the workload of the teachers limits their ability to provide detailed feedback on each student’s essay. To bridge this gap, we have developed a tool called PEER (Paper Evaluation and Empowerment Resource) which exploits the power of LLMs and provides students with comprehensive and engaging feedback on their essays. Our goal is to motivate each student to enhance their writing skills through positive feedback and specific suggestions for improvement. Since its launch in February 2023, PEER has received high levels of interest and demand, resulting in more than 4000 essays uploaded to the platform to date. Moreover, there has been an overwhelming response from teachers who are interested in the project since it has the potential to alleviate their workload by making the task of grading essays less tedious. By collecting a real-world data set incorporating essays of students and feedback from teachers, we will be able to refine and enhance PEER through model fine-tuning in the next steps. Our goal is to leverage LLMs to enhance personalized learning, reduce teacher workload, and ensure that every student has an equal opportunity to excel in writing. The code is available at https://github.com/Kasneci-Lab/AI-assisted-writing .
The idempotency in the genetic algebras generated by QSO on infinite state space

In this paper, we recall the notion of a quadratic stochastic operator (QSO) generated by a special distribution on infinite state space.Also, we consider the concept of genetic algebras generated by these QSOs.This paper aims to study the idempotency in the genetic algebras generated by QSO and defined by special distributions, including geometric, Poisson, mixture geometric, mixture Poisson and heterogeneous mixture geometric and Poisson distributions.
Optimal Kalman Filter With Information-Weighted Consensus

The use of wireless sensor networks for distributed state estimation has been a popular research topic in the signal processing community. However, there is a distinct lack of emphasis on formal derivation and optimality of distributed state estimation algorithms in the literature. Furthermore, many existing algorithms utilize unweighted average consensus filtering, which has been shown to lead to poor estimation performance in the presence of sensor agents that cannot make measurements due to environmental obstructions or sensor limitations. In this article, a novel distributed minimum mean-squared error estimator is developed by generalizing the Kalman consensus filter to incorporate consensus on a weighted directed graph. By employing weighted consensus, the algorithm is able to achieve a directional flow of information in heterogeneous sensor networks, leading to improved performance in the presence of sensors that have low observability. Unlike several existing algorithms, the proposed algorithm does not rely on approximations or ad hoc parameter tuning and achieves optimal performance in a fully distributed setting. Through numerical simulations, it is demonstrated that the proposed algorithm has a smaller mean-squared estimation error and is robust in the aforementioned scenarios.
Evolution and Trends of Cloud on Industrial OT Networks

The current industrial automation landscape faces considerable challenges due to the increasing growth of Industrial Internet of Things (IIoT) devices, cloud services, IT/OT convergence, along with evolving hyper-scaler technologies such as Kubernetes and distributed computing. This paper provides an in-depth review of the existing ISA-95 Model and its current role in supporting manufacturing systems and their interactions. Additionally, it examines how emerging technologies are impacting the security, design, and management of Operational Technology (OT) networks. As existing perimeter-based models, such as ISA-95, are pushed to their limits, the concepts of Zero Trust architectures, and policy based or software-defined networks are being explored as the next generation of OT network design. This paper aims to provide a high-level introduction to the concepts and disruptive technologies, and introduce the potential implementation risks and challenges of these principles within traditional IT/OT converged solutions.
A Comparative Study of Smart Contracts-Based Blockchain

Smart contracts are altering traditional industries and business processes. Smart contracts, which are entrenched in blockchains, allow the contractual requirements of a contract to be enforced automatically without the participation of a trustworthy third party. As a significance, smart contracts can minimise administration and service costs, improve business process efficiency, and reduce risks. Although smart contracts have the potential to spark a new innovation wave in corporate operations, they face a number of complications. This chapter offers an overview of smart contracts. This chapter provides an overview of smart contracts. The authors initiate by discussing blockchains as well as smart contracts. The problems of smart contracts are then discussed, in addition to contemporary technological breakthroughs. In addition, the authors assess common smart contract platforms also to provide a classification of smart contract applications, together with some exemplary instances.
End-to-End Optimization for a Compact Optical Neural Network Based on Nanostructured 2 × 2 Optical Processors

Recent research in silicon photonic chips has made huge progress in optical computing owing to their high speed, small footprint, and low energy consumption. Here, we employ nanostructured 2×2 optical processors in an optical neural network for implementing a binary classification task efficiently. The proposed optical neural network is composed of five linear layers including ten optical processors in each layer, and nonlinear activation functions. 2×2 optical processors are designed based on digitized meta-structures which have an extremely compact footprint of 1.6×4 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . A brand-new end-to-end design strategy based on Deep Q-Network is proposed to optimize the optical neural network for classifying a generated ring data set with better generalization, robustness, and operability. A high-efficient transfer matrix multiplication method is applied to simplify the calculation process in traditional optical software. Our numerical results illustrate that the maximum and mean accuracy on the testing data set can reach 90.5% and 87.8%, respectively. The demonstrated optical processors with a significantly compact area, and the efficient optimization method exhibit high potential for large-scale integration of whole-passive optical neural network on a photonic chip.
Genetic Algorithm for Program Synthesis

A deductive program synthesis tool takes a specification as input and derives a program that satisfies the specification. The drawback of this approach is that search spaces for such correct programs tend to be enormous, making it difficult to derive correct programs within a realistic timeout. To speed up such program derivation, we improve the search strategy of a deductive program synthesis tool, SuSLik, using evolutionary computation. Our cross-validation shows that the improvement brought by evolutionary computation generalises to unforeseen problems.
Foster Systematic Agile Transitions with SAFe® and efiS® Oriented Team Evaluations

Agile transitions are complex changes within organizations and their employees. Typically, established frameworks are used as a kind of implementation target picture for the first milestones of transitions. To evaluate the progress of the adoption of the target frameworks the organizations with their teams need feedback. The proposed approach address these feedback for transitions with a SAFe® and efiS® framework focus. This includes Scrum with the SAFe® Scrum derivation. The approach was designed to facilitate the transition journey of product teams and organizations in large enterprises. The presented case study of the Volkswagen Group IT shows the facilitation of the journey in direction to a more agile and product oriented organization.