Reviewerly/exHarmony huggingface dataset:
Decision Support System for Selection of Quality Palm Seeds with the Analytical Hierarchy Process Method

At PT. Gersindo, which is located in West Pasaman Regency, is a factory company that processes palm fruit into semi-finished oil or often called CPO (Crude Palm Oil). The main part that is managed in oil palm is the fruit, the fruit of the oil palm produces crude oil. PT. Gersindo is currently still using the manual method in determining the quality of the oil palm kernels, so that problems arise in the selection of the oil palm kernels at PT. Gersindo. At the factory PT. Gersindo in selecting quality palm kernels can be solved by developing an application in a decision support system. A decision support system is a system that is able to solve problems efficiently and effectively, which aims to help make decisions by choosing various alternative decisions. One technique that can be applied in developing a decision support system is to use the AHP ( Analytical Hierarchy Process ) method. The AHP method is a method in a decision support system for solving a very complex and unstructured problem into several component parts in a hierarchical arrangement by giving subjective values. The essence of the AHP method is to determine the weight value for each criterion. The results of this study can be determined Consistency Ratio (CR) is 0.0705 . And CR is less than 0.1 , then the matrix is declared consistent, so it is very effective and efficient in making decisions on selecting quality palm kernels.
Robust Zero-Watermarking Algorithm for Medical Images Based on AGAST-LATCH and DCT

Medical image transmission is an important aspect of digital healthcare. However, security and privacy are crucial issues during this process. Traditional watermarking techniques have become unreliable due to the damage they cause to pixel information in images. To solve this problem, zero-watermarking technology has emerged. Compared to traditional watermarking technology, zero-watermarking does not affect the visibility and quality of medical images and has higher robustness against attacks. Based on this, a robust zero-watermarking algorithm for medical images based on AGAST-LATCH and DCT is proposed in this paper. First, the AGAST algorithm and LATCH algorithm are combined to extract the feature matrix of the medical image. Then, the hash sequences of the medical image are obtained using Discrete Cosine Transform (DCT) and perceptual hashing algorithm on the feature matrix. The resulting hash sequence is combined with the encrypted watermark information, which is encrypted by the chaotic state of Logistic Map to enhance the robustness of the watermark information. Finally, a zero-watermarking technique is employed to create a watermark that is resistant to both conventional and geometric attacks.
How to Make the Most of Local Explanations: Effective Clustering Based on Influences

Machine Learning is now commonly used to model complex phenomena, providing robust predictions and data exploration analysis. However, the lack of explanations for predictions leads to a black box effect which the domain called Explainability (XAI) attempts to overcome. In particular, XAI local attribution methods quantify the contribution of each attribute on each instance prediction, named influences. This type of explanation is the most precise as it focuses on each instance of the dataset and allows the detection of individual differences. Moreover, all local explanations can be aggregated to get further analysis of the underlying data. In this context, influences can be seen as new data space to understand and reveal complex data patterns. We then hypothesise that influences obtained through ML modelling are more informative than the original raw data, particularly in identifying homogeneous groups. The most efficient way to identify such groups is to consider a clustering approach. We thus compare clusters based on raw data against those based on influences (computed through several XAI local attribution methods). Our results indicate that clusters based on influences perform better than those based on raw data, even with low-accuracy models.
Navigating Linguistic Challenges: Exploring Difficulties in English Comprehension among Rural Second Language Learners

The current study illuminates and identifies rural learners' English Grammar learning issues at lower standard. The current study may be effective in improving rural learners' English proficiency and removing learners' issues. It enables teachers to diagnose learners' learning barriers and facilitates remedial instruction to improve learners' English achievement. The study used the survey method as its methodology.The Sample: For the study, 200 - first year students and some teachers from School and College in the rural area served as the sample. Tools for the study included a survey seeking teacher opinions and a diagnostic exam created by the researcher. The first step in the study's methodology is the identification of problems through the use of diagnostic tests. 2nd Determining the learners' problems based on the teachers' perceptions. The learners struggle with pronunciation, prepositions, conjunctions, sentence, structure, and semantics, among other aspects of grammar.the implications for education Refreshing the English skills of rural students might be beneficial Keywords: Diagnostic Test, English, Learning Difficulties, College, Level.
The Influence of Teachers’ Classroom Transformational Leadership on Chinese College Students’ English Learning—Taking the Flipped Classroom as an Example

Many educators have recognized that a teacher-centered approach to teaching and learning frequently results in students becoming passive in the classroom. The flipped education model has resulted in students becoming more motivated and participating in the classroom, particularly in English (Doman &amp; Webb, 2017). In addition, in a flipped university English classroom, the teacher as a leader can first stimulate students’ interest in the flipped classroom; set motivational goals to stimulate students’ interest in learning the language; set specific and manageable goals, plans, requirements, and assessment criteria; provide personalized guidance to students; set an example for students through the teacher’s own behavior; and create a pleasant and relaxed learning environment (Suo &amp; Hou, 2017). All of these changes in teaching styles reflect the transformational leadership of teachers in the classroom, while this change in classroom paradigm has also influenced university students’ learning of English.
Kualitas Air Bersih pada Sumur Bor di Desa Sumber Rejo Kabupaten Banyuasin

According to WHO calculations, in developed countries each person needs between 60-120 liters of water per day. One of the most important uses of water is the need for drinking. Based on field observations, the area in Sumber Rejo Village, Banyuasin Regency is a water area that allows the water quality of drilled wells in the area to not meet the standard, therefore this study aims to determine the iron content (Fe) in drilled wells. This research is descriptive quantitative. The population in this study were 12 drilled wells in the village of Sumber Rejo, Banyuasin Regency and the sampling technique was purposive sampling. The data analysis used in this study was univariate. This research was carried out 7july-7august 2021. The results showed that the content of iron (Fe) in the drilled well contained 1 of 12 samples that did not meet the requirements, the taste of the water 100% met the requirements for clean water, the turbidity of the water 100% met the requirements for clean water, and the color in the drilled well there is 1 of 12 samples that do not meet the requirements based on the Regulation of the Minister of Health of the Republic of Indonesia NO. 416/MENKES/PER/Lx/1990. It was concluded from 12 samples of drilled well water, it can be concluded that 90% of drilled well water has met the requirements of PERMENKES RI No. 416 of 1990. It is recommended that in the future the community has or uses water filters/filters before using bore well water to make it cleaner. &#x0D;
A Blockchain based Provably Secure AnonymousAuthentication for Edge Computing-enabled

Abstract Edge computing server deployed at the edge of the network has promoted thedevelopment of the Internet of Things (IoT) but also brings some security prob-lems. Traditional IoT security solutions have not met the requirements of thecurrent rapid growth of equipment, real-time and lightweight. Therefore, thedevelopment of new authentication and key negotiation is becoming an importantmeans to solve the security problems of the IoT. To address this challenge, anauthentication framework for edge computing enabled-Internet of Things environ-ment is proposed to realize the establishment of secure communication betweendevices and edge servers, devices, and devices. Meanwhile, the formal and infor-mal security analysis shows that the proposed protocol meets a wide range ofsecurity attributes and resists many security threats. In addition, the computa-tion consumption and transmission consumption of the protocol are analyzed andcompared. The results show that the proposed protocol has good performance inboth calculation and transmission.
A Study on the Relationship among Senior High School English Learners’ Prefabricated Chunks, Writing Anxiety and Writing Achievements

English writing can be affected by students&amp;rsquo; affective factors and their prefabricated chunks. Recently, many researchers have studied the relationship between writing anxiety and English writing achievements, prefabricated chunks and writing anxiety, and prefabricated chunks and English writing achievements. However, there are few studies on the relationship among these three variables. This research aims to answer the following questions: 1. What is the current situation of senior high English learners&amp;rsquo; prefabricated chunks, writing anxiety and writing achievements? 2. What are the correlations among their prefabricated chunks, writing anxiety and writing achievements? 3. Does writing anxiety play a mediating role between prefabricated chunks and writing achievements? If so, to what extent? Based on the affective filter hypothesis and prefabricated chunks theory, this study chose 174 senior high English learners to complete the English writing anxiety questionnaire and a mid-term test. The results show that: 1. Senior high English learners&amp;rsquo; prefabricated chunks are at a middle level. Their writing anxiety is at a relatively high level. And their writing achievements are at a medium level. 2. There is a negative correlation between prefabricated chunks and writing anxiety, between writing anxiety and writing achievements. And prefabricated chunks and writing achievements are positively correlated with each other. 3.Writing anxiety has a mediating effect between prefabricated chunks and writing achievements. The mediating effect is 9.82%. Finally, some pedagogical suggestions are advanced for the teaching of English writing in senior high schools. And the limitations and some suggestions to the future studies are discussed.
Media DAO: A blockchain-based model for the transformation and innovation of the publishing and media industry

The publishing media industry faces many challenges such as outdated innovation mechanisms, strict information control, low quality of content creation, unfair distribution of content, and poor management of content rights, etc. Media decentralized autonomous organization (DAO), a blockchain-based media organization, offers innovative solutions to these problems through its decentralized nature, new organizational form, and governance. This paper compares the differences between the traditional publishing media industry and media DAOs, discusses the changes that media DAOs have brought to the publishing media industry, and explores future trends and challenges in the publishing media industry. The goal is to guide the publishing media industry to break away from stereotypical thinking and further explore the room for development.
Comparison Performance of K-Medoids and K-Means Algorithms In Clustering Community Education Levels

Education is a mandatory right of all citizens and the key to the nation's superiority in global competition that must get top priority to be examined critically and comprehensively. It is known that compulsory education is at least 12 years, but not all people can do it because of minimal economic conditions. In past years, COVID-19 has also had an impact on the economy, school dropout rates, and falling academic achievement, for example in Central Kalimantan. The size of Central Kalimantan, however, makes it difficult for the government to identify the areas with the worst levels of education. To determine which regions fall into the low and high education categories, it is required to group the province's educational levels. This study also compares two algorithms by measuring their accuracy. By looking at which algorithm has the lowest Davies Bouldin Index (DBI) value, the best degree of performance can be ascertained. To process the data from as many as 1,565 sources, data mining techniques, including the clustering method, were used. K-Means and K-Medoids algorithms were employed in this work as clustering techniques. Based on the outcomes of the cluster created, both algorithms are also put to the test for performance. The results of this study obtained 6 clusters in K-Means with the lowest DBI value of -0.439, while the results in K-Medoids were in 3 clusters with the lowest DBI of -0.866. Based on accuracy testing using DBI, it is known that K-Means results are more optimal with the lowest DBI value in the grouping of education levels compared to K-Medoids. It is also known from the formation of 6 clusters of the K-Means algorithm that the low education level is in cluster_0 which is 1484 villages and the higher education level is as many as 3 villages in cluster_3.
FedGA-Meta: Federated Learning Framework using Genetic Algorithms and Meta-Learning for Aggregation in Industrial Cyber- Physical Systems

In Industry 4.0, factories encounter significant challenges in making informed decisions to maintain or enhance their industry standing. By utilizing machine learning (ML), they can improve product quality, decrease production downtime, and boost operational efficiency. However, acquiring datasets with sufficient variation and diversity to train a robust neural network centrally is a challenge within the industrial sector. Consequently, federated learning (FL) offers a decentralized approach that safeguards data privacy, enabling smart infrastructures to train collaborative models locally and independently while retaining local data. In this paper, we present FedGA-Meta framework, which combines FL, meta-learning, and domain adaptation to enhance model performance and generalizability, particularly when training across distributed factories with varying network and data conditions. The results obtained demonstrate the effectiveness and efficiency of our FedGA-Meta framework.
Inductive Learning of Declarative Domain-Specific Heuristics for ASP

Domain-specific heuristics are a crucial technique for the efficient solving of problems that are large or computationally hard. Answer Set Programming (ASP) systems support declarative specifications of domain-specific heuristics to improve solving performance. However, such heuristics must be invented manually so far. Inventing domain-specific heuristics for answer-set programs requires expertise with the domain under consideration and familiarity with ASP syntax, semantics, and solving technology. The process of inventing useful heuristics would highly profit from automatic support. This paper presents a novel approach to the automatic learning of such heuristics. We use Inductive Logic Programming (ILP) to learn declarative domain-specific heuristics from examples stemming from (near-)optimal answer sets of small but representative problem instances. Our experimental results indicate that the learned heuristics can improve solving performance and solution quality when solving larger, harder instances of the same problem.
Efficient Low Light Video Enhancement Based on Improved Retinex Algorithms

Videos shot in low-light environments suffer from low contrast and high noise. In this paper, an improved zero-reference low-light enhancement technique for videos based on the Retinex model is presented. The proposed method improves the existing Retinex approaches in several aspects. First, the image features extracted by the VGG network are employed as a part of the input to the generator of the Retinex parameters for increasing temporal stability. Second, a deformable convolution kernel is used to enhance the spatial correlation. Third, the optical flow between frames is approximated as a combination of affine linear transformations for reducing complexity. Compared with the state-of-the-art low-light enhancement algorithms, the proposed method achieves more favorable and stable image qualities in PSNR and SSIM with short processing time.
Simulation Of Optimized Cluster Based PBFT Blockchain Validation Process

Recently, blockchain technology has emerged as a revolutionary innovation with its distributed ledger feature that allows applications to store and transmit data in a secure, transparent, and immutable manner. One of the main ideas of blockchain technology is the consensus mechanism for reaching an agreement on the state of the distributed ledger. In this context, Practical Byzantine Fault Tolerance (PBFT) is one of the most popular algorithms. However, it has a high communication overhead and poor scalability. To overcome these limits, this paper presents the effectiveness of our algorithm Random Cluster PBFT (RC-PBFT) through an evaluation of its performance under various network conditions. Our algorithm runs on randomly selected clusters, and the results are then broadcast on the blockchain network. The effectiveness of RC-PBFT is demonstrated by implementations on the NS-3 network simulator, and the tests performed show that our approach achieves significant improvements compared to the original PBFT algorithm.
Continual Egocentric Activity Recognition via Foreseeable-Generalized Multi-Modal Representations

&lt;p&gt;The rapid development of wearable sensors promotes convenient data collection in human daily life. Human Activity Recognition (HAR), as a prominent research direction for wearable applications, has made remarkable progress in recent years. However, existing efforts mostly focus on improving recognition accuracy, paying limited attention to the model's functional scalability, specifically its ability for continual learning. This limitation greatly restricts its application in open-world scenarios. Moreover, due to storage and privacy concerns, it is often impractical to retain the activity data of different users for subsequent tasks, especially egocentric visual information. Furthermore, the imbalance between visual-based and inertial-measurement-unit (IMU) sensing modality introduces challenges of lack of generalization when employing conventional continual learning techniques. In this paper, we propose a motivational learning scheme to address the limited generalization caused by the modal imbalance, enabling foreseeable generalization in a visual-IMU multimodal network. To overcome forgetting, we introduce a robust representation estimation technique and a pseudo-representation generation strategy for continual learning. Experimental results on the egocentric multimodal activity dataset UESTC-MMEA-CL demonstrate the effectiveness of our proposed method. Furthermore, our method effectively leverages the generalization capabilities of IMU-based modal representations, outperforming general and state-of-the-art continual learning methods in various task settings.&lt;/p&gt;
Bottom-Up Stratified Probabilistic Logic Programming with Fusemate

This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive for a number of reasons, e.g., for dynamically creating distributions of varying support sizes, it makes it harder to control the amount of ground clauses generated. We address this problem by interleaving grounding with a query-guided relevance test which prunes rules whose bodies are inconsistent with the query. % This is done We present our method in detail and demonstrate it with examples that involve "time", such as (hidden) Markov models. Our experiments demonstrate competitive or better performance compared to a state-of-the art probabilistic logic programming system, in particular for high branching problems.
Design and Comparative Analysis of Quantum Hashing Algorithms using Qiskit

&lt;p&gt;Paper is on hashing algorithms designed for Quantum computers.&lt;/p&gt;
Self-Efficacy and Self-Awareness in Scientific Translators' Education: A Preliminary Study

In this paper, we present the results of a preliminary study on the evolution of self-efficacy and self-awareness among scientific translation students throughout one semester using data collected for three academic years. The analysis was based on the two components of self-efficacy: the perception that students have of their own capabilities to perform specific activities related to scientific and technical translation and their actual capabilities to perform those activities. To assess the self-efficacy beliefs of students, we analyzed the evolution of the self-perception of students based on the results of two questionnaires, an initial assessment questionnaire on their translation habits and problems, and a final psychometric questionnaire on the perceived usefulness of the pedagogical method used and on their self-efficacy beliefs. To assess the actual capabilities of students, we analyzed the evolution of the number of errors in each translation assignment and of the academic scores of students. The comparative analysis of the diagnostic assessment questionnaire and the diagnostic assessment translation assignment revealed low self-awareness at the beginning of the semester, as evidenced by a poor correspondence between students’ perceptions and performance. In contrast, a high degree of correspondence was found between self-efficacy beliefs and academic performance at the end of the semester, which suggests that the implemented method helped students develop realistic self-efficacy beliefs.
Unsupervised Domain Adaptation with Debiased Contrastive Learning and Support-Set Guided Pseudo Labeling for Remote Sensing Images

&lt;p&gt;Here, we address the challenge of generalizing object detection and labeling in remote sensing data from one dataset to another. It is the variability in different altitudes, geographical variances, weather conditions, and object size across datasets that state-of-the-art DNN largely fails to generalize. Contrastive-based unsupervised domain adaptation attempts to bridge the gap by producing discriminating features for frames and instances across different datasets, and we have shown some progress on the domain alignment on local, global, and instance-aware features for remote sensing data. In this research, we propose using support-guided pseudo-labeling on the target domain instances to enable instance domain adaptation on the source and target datasets. Next, we introduce the contrastive loss function with multiple positive examples to make the model more generalized of the variable appearance of a particular class over images and domains. Also, we proposed debiased contrastive learning based on class probabilities to address the challenge of false negatives in the unsupervised framework. We show the advantages of the proposed model on satellite (DIOR and DOTA2.0) and drone (Visdrone and UAVDT) image datasets. &lt;br&gt; &lt;/p&gt;