Reviewerly/exHarmony huggingface dataset:
The Experimental Garden in the E-Commerce System of the Faculty of Agriculture (KPFP) at Udayana University

The marketing of agricultural products has long been a challenge for the Experimental Garden of the Faculty of Agriculture (KPFP), particularly in the business-to-customer (B2C) arena. To address this issue, KPFP, as one of the business units at Udayana University, has recently embarked on the development of an e-commerce system, recognizing its potential as an alternative medium for promotion, communication, and information dissemination. The objective of this study is to identify the characteristics of each KPFP field and to design an e-commerce system that caters to their respective needs. The analytical approach employed in this study was descriptive qualitative method, supported by simple tabulations and the Lean User Experience (Lean UX) approach. Our findings reveal that each KPFP field has performed its assigned tasks and responsibilities satisfactorily. The e-commerce system we have developed offers customers a choice of payment methods, including cash on delivery (COD), and ensures timely delivery, thus enabling customers to enjoy the products in a fresh condition without any loss of quality. Further research is needed to assess the system's effectiveness using the Success Rate as a usability parameter and to evaluate the efficiency of usage time, measured in seconds or minutes, required by customers to complete each task.
Artificial Intelligence, Blockchain, Machine Learning, and Customer Relationship Management

This article discusses the utilization of technology, particularly artificial intelligence (AI) and blockchain, in customer relationship management (CRM). The use of technology in CRM helps companies improve customer experience, maximize customer value, and make better business decisions. AI plays a crucial role in managing CRM by collecting, analyzing and personalizing customer data, predicting customer decisions, and creating chatbots. Blockchain improves the transparency, security and accuracy of customer information stored in CRM. Research data shows that the combination of AI and blockchain in CRM can lead to increased business efficiency, improved customer experience, revenue increase, improved data security and increased transparency of customer information.
Secure and Efficient Electronic Voting Machine Using IoT

Abstract: The Secured voting machine is developed to avoid all the apparel conditioning in the election process. Results are blazoned to every individual namer who suggested in the election, this point is lacking in the being electronic voting machine. Vote counting, affect assaying, affect publications are taking further time and also more homemade crimes happens in this process when we use current Electronic Voting Machine. We concentrated on the main issue that namer have to travel to their own native for advancing in the electronlike. The namer must go to the position where their namer ID is registered, also only they allow them to bounce. To develop a compact near field communication device for the choosers to poll their vote in order to have the voting process in a secured and effective introductory conception of this charge is to produce a digital voting system as a way to help to get relieve of defrauding of the homemade voting structures like paper- grounded voting system, in this there is a chance of casting vote further than formerly and prior. The proposed machine is which includes a couple of layers of verifications to insure the trust ability of the device. With the addition of biometric point detector, every namer is entered into the contrivance simplest after being diagnosed and checked with the given database of enlisted choosers. Once the corresponding point is matched with the records handed, the namer will be allowed to do for opting their preferred seeker from the panel of buttons. The final vote is also displayed onto a Liquid Cristal Display for the pride of choosers. The proposed design displays translucency and also carries the characteristic of being independent for the duration of the path of operation.
D-Drive: Decentralized Storage Space using Blockchain and IPFS Protocol

Abstract: Centralized cloud-based storage has received great attention and has been extensively used by many companies in recent years. However, these cloud based storage are not secure because of the involvement of a centralized entity or a third party. On the other hand, there is a need for blockchain based decentralized storage to maximize data privacy and security. This paper proposed D-Drive, an IPFS-based decentralized storage space to solve the problem. D-Drive is a software solution trying to prove that centralized cloud-based storage applications can be decentralized, more secure, and efficient. This paper proposed developing a web-based application that provides a user interface,from which the user can directly share their data or files. Then, the user file is encrypted and stored across a peer-to-peer network using IPFS protocol instead of HTTP protocol and a cryptocurrency will be used as a payment mechanism. D-Drive’s primary objective is to provide secure decentralized storage space.
Engaging Indonesian students in “read, reread, list, compose” strategy to enhance paraphrasing skill

One crucial skill to master by university students related to academic writing activity is paraphrasing skill. However, in Indonesian context, the university students still find paraphrasing challenging. Therefore, this study is intended to explore the use of read, reread, list, compose (RRLC) strategy to enhace the students’ paraphrasing skill in a college context. This study utilizes a qualitative case study design where the data were obtained through observation. The study revealed that RRLC stages can be implemented in learning activity. It engaged the students in activating their prior knowledge, doing an independent reading, discussing, and sharing their understanding about the text before paraphrasing. It assisted them to have better comprehension of the text to be paraphrased. In addition, during the implementation of composing stage in guided practice, the students’ paraphrased works indicated that the students were able to create sentences in different structures—active or passive constructions, use the synonyms, and retain the original meaning. These three skills helped them reduce the similiarity of their paraphrased works from the original version. This study recommends that RRLC strategy be implemented in teaching paraphrasing at college or lower level with some adjustments to meet the students’ basic English skill.
A content analysis of the language quality of thematic textbooks for elementary school students

Primary school students rely heavily on textbooks for instruction. Using thevariety of textbooks, a description of the theme textbooks’ linguistic qualityis conspicuously absent, particularly for students in the fifth grade. Languagefit for students’ growth, communicative language, and coherence andcohesiveness were all included in this study’s analysis of textbooks’language quality. Qualitative content analysis was used in this study'smethodology. All texts from the heat and transfer-themed of fifth-gradetextbooks published by the Indonesian Ministry of Education and Culture,Yudhistira, and Erlangga publishers were included in the study.Documentation was utilized to collect the data. Sample, record, reduce,infer, and narrate were all data analysis approaches used. The findings of thisresearch indicated that the three publishers’ textbooks had excellentlanguage quality in terms of language compatibility for students’ growth andthe communicative language issued by the Indonesian Ministry of Educationand Culture and Erlangga was wonderful, but Yudhistira was awful. Therewere a lot of solid points made in the Ministry of Education and Culture’spublications. Both Yudhistira and Erlangga performed admirably. Thequality of textbook language was found to be satisfactory in this study.
Student’s commitments and preferences in online learning

During the COVID-19 pandemic, the learning was conducted by online system. The objective of the research was to understand the commitments and preferences of students in online learning. A total of 516 students participated in filling out an online questionnaire. The data were analysed descriptively referring to the critical success factors (CSFs). Based on the research result, there were three most dominant obstacles, they were: i) Internet interference (42.71%); ii) Limited quota (24.49%); and iii) Other activities (22.92%). There were students who did not attend full-time (22.45%). The weak commitment was boredom (2.04%) and feeling that they understood the module (5.10%). There were students who do not study full-time, only filling out the attendance list (53.1%). A small number of students (2.04%) fill out the attendance list and upload assignments as a top priority. According to this research, 46.7% of students liked online learning and 39.7% did not like it. In addition, 42.83% of students were bored. It means that about 60% of saturation comes from like-dislike preferences. The remaining 40% are influenced by other factors. The students’ commitments and preferences are influenced by many factors. Those who force themselves to learn ineffectively will be at risk of learning loss.
3D Printing Technology Supports the Learning of Geometry in Primary School Mathematics

The rapid prototyped, personalized independent design, and online and offline integration of 3D printing technology have been widely used in education. However, in mathematics, especially the application of geometry learning is not enough. Based on the theory of embodied cognition, this study attempts to integrate 3D printing technology into the learning process of primary school mathematical geometry. The results from implementation show that 3D printing technology can bring significant improvement to mathematical learning performance, such as learning from 3D products, learning geometric knowledge concepts, the after class design of 3D products positively correlated with post-test scores, and learners had a favorable attitude toward 3D printing technology; however, the intervention of 3D printing technology has made time constraints within the class more challenging.
The GPT-Comparator: Discovering and Reporting Spatial and Topical Biases in Generative Pre-Trained Transformers

Though highly-performant, the unsupervised training of Generative Pre-trained Transformers (GPTs) on internet corpora enabled them to assimilate the prejudices and other dangerous generalizations that pervade the web. Utilizing a reinforcement learning approach with human feedback (RLHF), newly-revised engines (InstructGPT) were introduced, placing an emphasis on aligning language model behavior to user-intent. In this study, we offer a tool to compare the efficacy of InstructGPT at overcoming affective biases in long text-generation, that utilizes contemporary as well as novel assessment metrics for evaluating text sentiment and subjectivity. Results indicated that post-RLHF, negative behaviors and actions of InstructGPT were still considerably prevalent. In offering a way to compare and benchmark tools on common inputs, the GPT-Comparator highlights the challenges that still exist related to applying transformer models to real-world applications. We close with a discussion of implications and recommended best practices for using the GPT-Comparator.
A Federated Multi-Server Knowledge Graph Embedding Framework For Link Prediction

The federated framework is actively applied in knowledge graph fusion research to obtain a complete knowledge graph without exposing data privacy. It can help local clients learn the knowledge graph embeddings in other clients without revealing data privacy. However, current federated-based knowledge graph embedding frameworks cannot exploit both entity and relation embeddings and may not prevent partial triples from being reconstructed. This paper proposes a novel framework named Federated Multi-server knowledge graph embedding (FedM), which creatively utilizes uploaded entity and relation embeddings while preventing privacy leakage. Expressly, we first set up two central servers for entity and relation embeddings to aggregate and share client-uploaded embeddings. Secondly, we design a knowledge graph secure aggregation algorithm to address the potential privacy concerns in FedM. We conduct comparative experiments on an empirical dataset (divided into three federated datasets) with four commonly-used knowledge graph embedding methods to evaluate the performance of our proposed framework. In addition, our proposed FedM framework is generally superior to the latest baseline frameworks on both privacy preservation and link prediction tasks.
Joint entity relation extraction of Chinese electronic medical record based on graph convolutional neural network and words for relationship discovery

Considering the characteristics of Chinese medical records, such as many short sentences, weak long-distance relations, and serious relation overlap, a joint entity relation extraction method based on relation discovery word and graph convolution is proposed. This method is based on the relationship discovery word set. Through the word attention mechanism, different weights are assigned to the context description words of the relationship to achieve semantic enhancement. Based on the text-embedded representation obtained from the pre-trained language model, a feature fusion gating mechanism was designed to realize the organic fusion of the output features of the bidirectional short and long-duration memory network and the feature of relation discovery words. The weighted dependency graph, which is encoded by a graph convolutional neural network and pruned by an attentional mechanism, is used to obtain local features of sentence grammatical structure. The proposed method was applied to CMeIE and CMRT-RE data sets, and compared with other mainstream methods, the effectiveness of the proposed method in joint extraction of medical entity-relationship was verified experimentally.
Trainable complex ISTA variants for radar multitarget detection

In this work, we implement two complex trainable versions of the well-known iterative shrinkage thresholding algorithm (ISTA) method—this is, complex learnable–ISTA (C-LISTA) and complex analytical–LISTA (C-ALISTA)— and apply them on radar signals. We describe how the original algorithms can be modified to handle the complex nature of the signals and discuss the implementation details required for successfully training such models. By means of numerical experiments, we evaluate the performance of the presented methods in terms of probability of detection and benchmark them against the traditional ISTA method. Our simulations show that it is possible to achieve a performance comparable to ISTA but with much lower computational cost, signaling the potential of trainable methods for online sparse reconstruction in computationally demanding surveillance radar tasks.
Invariant extended Kalman filter for tracking the bishop frame using radar measurements

This paper proposes a target model for radar state estimation based on the Bishop or Parallel Transport frame, extending upon previous work using the Frenet-Serret frame to describe target motion. The Bishop frame is embedded into an element of the SE(3) Lie group which allows for the partial implementation of the invariant extended Kalman filter (IEKF) to estimate the the target pose, unit norm velocity and Bishop curvatures. The performance of the proposed filter is compared to the original Frenet-Serret IEKF and industry standard algorithms. Results demonstrate comparable performance to the Frenet-Serret IEKF and the choice of kinematic model is discussed.
Dynamic graph CNN point clouds classification network based on channel attention and SoftPool

In recent years, related studies on point clouds have become the hot topic in the field of deep learning due to the rich 3D spatial information. Among classification networks, Dynamic Graph CNN (DGCNN) is efficient, which can deal with point clouds directly and has good performance. Squeeze-and-Excitation Networks (SENet) is a channel attention module that can highlight important information while reducing useless information. Compared with max pooling or average pooling, SoftPool is able to retain more information during the downsampling process. In this paper, we design a novel network called Channel Attention-based Dynamic Graph Convolutional Neural Network (CA-DGCNN). CA-DGCNN combines SENet and SoftPool with DGCNN to improve the accuracy of classification. The experimental results on public ModelNet40 show that CA-DGCNN increases classification accuracy from 92.2% to 93.44% and average class accuracy from 90.2% to 91.02% compared to DGCNN.
CLSD: Continual Learning for Lane Line Segmentation Across Domains

Lane line segmentation is a fundamental task in the field of autonomous driving, and it’s intuitive that automatic vehicles should have the ability of processing data from different domains. The usual assumption is that training data of all the domains is available at a time, however, it’s more common to collect data continually and train the visual system incrementally. To date, many works focus on segmenting lane line in a single domain, but no work considers continually segmenting lane line across domains. To tackle with this task, we presents a method, named as CLSD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> which leverages data from new domain while avoiding catastrophic forgetting. The core of CLSD are a training framework that effectively transfer knowledge from the previous model to the new model and a frustrating simple yet useful self-training strategy. We conduct extensive experiments based on TSD datasets and showcase that on daytime our training framework increases the miou by 10% on the fog domain data, and the self-straining increases the miou by 11% on the fog domain data and 47% in night domain data. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Continual Learning for Lane Line Segmentation Across Domains
EFFECT OF GLOBALIZATION ON PRIMARY HEALTHCARE SERVICES IN NASARAWA STATE, NIGERIA

Health sector in Nigeria, despite growth opportunities presented by the rudiments of globalization, remains decrepit, completely dysfunctional, or functioning at less than average capacity both in the primary, secondary, and tertiary levels. This has gone a long way to affect the delivery of health services particularly as it relates to primary healthcare. This study therefore examined the effect of globalization on primary healthcare services in Nasarawa State using the survey designed method. A sample of 308 respondents was statistically obtained from a population of 1356 using the Taro Yamane formula. The data for the study was collected through a quantitative method using the questionnaire as a data collection instrument. The study findings revealed that information and communication technology, electronic record keeping and electronic payment system has a significant effect on primary healthcare delivery services in Nasarawa State. It was recommended that the government should make policies, particularly primary health care, to enhance the use of technology for patient medical history and records which can be easily accessible. It was also recommended that all-important technological infrastructures should be provided to enhance the use of electronic payment systems particularly in the rural areas where these services are very poor.
Analisis Kombinasi Metode Pengukuran Terestrial Dan Fotogramteri Pada Penyusunan Master Plan Sekolah NU Kota Tarakan

Survey and mapping work can basically be done by various methods. Like terrestrial, photogrammetric and extraterrestrial methods, which generally have the same goal of projecting the earth surface into a topographic map at a certain scale. In this study, an exploration was carried out regarding the collaboration between the terrestrial method and photogrammetry in the depiction of topographic maps, which would provide more representative results. The object of this research is the land belonging to the PCNU of Tarakan City, which will be built as a school, where this land has a fairly steep surface relief. This study also provides comparison results from the application of the two measurement methods, which are applied to extreme land. Based on the analysis carried out, several research results were obtained, namely: The average percentage of the X,Y and Z coordinate of the two methods was 12,71% where a significant difference occurred in the altitude or Z component, which was 38,12% while the coordinate component X and Y have a difference value of 0,00%. The shape of the depiction of the soil relief produces an image that is close to the real condition, this can be seen from the contour shape that follows the shape of the soil relief on the sand cliff and tends to be random. The method that is suitable for use in the case of the object of this research is the photogrammetric method, because the steep terrain and irregular soil relief make it difficult to apply the terrestrial method, besides that the terrestrial method with the application of sampling point coordinates is not accurate to produce relief of the ground surface conditions actually.
Patriotism development in Russia: educational dimension

The article considers the actual problem of patriotism development in various historical periods of Russian history. Relying on experience of the past, the author shows modern ways of patriotism theme practical implementation in the educational process. The article has practical significance both for senior students, postgraduates and for teachers, educators at schools and educational organizations at all stages of education. The material of the article will help teachers to implement scientific research results into practical educational activities, creating conditions for patriotic feelings growth among the younger generation.
Pengaruh Jurusan Asal Sekolah Terhadap Motivasi Belajar Matematika Mahasiswa Menggunakan Model Pembelajaran SAVI

This research is an experimental research using the Mix Methode approach involving 68 students of the Petroleum Engineering Study Program, Balongan Indramayu Institute of Petroleum Technology.The sampling technique used is Simple Random Sampling, two classes were selected as samples, namely class 20 TPA as experimental class I and class 20 TPB as experimental class II.The tools or instruments in this study test mathematics learning outcomes in the form of description questions, interviews, motivational questionnaires and questionnaires from school majors.The data analysis of this study is using the average bandage test, ANAVA test and correlation test.The results of this study are: (1) The SAVI learning model used in student learning motivation is better than the expository learning model, ( 2) The SAVI learning model used in student learning motivation from the origin of the science department is better than the expository model.The use of the SAVI learning model is good for use in mathematics learning becausestudents are more active and interested in learning and can be a reference for teachers / lecturers in increasing student learning motivation.