Reviewerly/exHarmony huggingface dataset:
Navigation System in Space Environment Under Internet of Things Era

With the increasing development of life aesthetics, the forms of guide system design are becoming more and more diversified. It is no longer a single design and production project defined by materials, shapes, or processing types, but a “people-oriented” system design that integrates into People’s Daily life to meet the aesthetic requirements and combines with the space environment. In modern city life, the information between people and the environment is more and more closely related to its requirements are also more and more high, small to an office building, large to a region or even a city are required to have a scientific and humane guide system design.
PENGARUH PERHATIAN ORANG TUA TERHADAP MOTIVASI BELAJAR PESERTA DIDIK SEKOLAH DASAR

The problem in this study is the low learning motivation of the fifth grade students of State Elementary School 2 Campang Raya in the 2021/2022 academic year. The purpose of this study was to examine the significant effect of parental attention on the learning motivation of fifth grade students. This study used an ex-post facto method with quantitative research. A sample of 69 students is taken using a probability sampling teachnique, namely proportionate stratified random sampling. Data collection techniques in the from of questionnaires and document studies. The istrument has previously been tested for validity an reliability. The data analysis technique used a simple linier regression test. The results showed that there was a significant effect of parental attention on the learning motivation of fifth grade students at State Elementary School 2 Campang Raya in the 2021/2022 academic year.
The Effect of Educational Software on Academic Achievement in Computer Curriculum for Basic Seventh-Grade Students in King Abdullah II Schools for Excellence in Jordan

This study explores the effect of educational software on academic achievement in computer curriculum for basic seventh-grade students in king Abdullah II schools for excellence in Jordan. The study follows a quasi-experimental design by dividing the students into an experimental group and a control group of 50 per group. The former was taught through the instructional software, whereas the latter was taught through in-class instruction only. Having completed the implementation of the instructional software, the researcher applied the achievement test to both groups in order to verify the potential effect of the instructional software on students' achievement. The findings reveal a number of results, the most significant of which are that there are statistically significant differences in the students' mean scores attributed to the teaching method in favor of the experimental group which was taught through the instructional software The findings also reveal that there is no statistically significant differences in the mean scores of the seventh grade students in the computer subject attributed to either gender (male, female), or the interaction between the teaching method and gender. A number of implications and recommendations are put forth, the most important of which is the necessity to work on the utilization of modern strategies and methods in teaching computer due to its crucial effect on improving the students' academic achievement. Keywords: instructional software, seventh grade, computer, achievement. DOI: 10.7176/JEP/13-18-13 Publication date: June 30 th 2022
Investigating the Factors Affecting Iran's Real Exchange Rate according to Selected Indicators of Sixth Development Plan Using the Vector Error Correction Model

Investigating the Factors Affecting Iran's Real Exchange Rate according to Selected Indicators of Sixth Development Plan Using the Vector Error Correction Model
Exploratory Data Analysis on Indian economy using Python

Abstract: The research aims to study the dataset of overall country's economy considering factors such as GDP (gross domestic product), FDI (foreign direct investment), import value, export value and to analyse the data using exploratory data analysis technique. The data analysis is done using Python. The mentioned parameters are compared and analysed using statistical tools and visual analysis. Since Python is a high-level language, they give more feasible approach to the statistical analysis. Technically for data computing and plotting statistical analysis were tested by scatter plotting. The data are compared and plotted in graphs using python. GDP growth of India from the year 1960 to 2020 were analysed and studied in the form of graph. Variations of parameters like FDI, Trade balance, Export, import with respect to GDP were analysed from the year 1960 to 2020. State-wise GDP contribution for top 5 and least 5 states were discussed from the year 2012 to 2019. Sector-wise FDI contribution were studied for various important sectors were studied from the year 2013 to 2017. Line graph, Scatter plot, Bar graphs were used to interpret the data and analysis results.
Development of Web-Based Online Exam Portal

Abstract: System information like RAM, storage, IP address of the device, operating system, software’s installed are Pandemic outbursts during the last couple of years had made a huge impact on everyone’s life, especially in the education system, and made us rely on online platforms. Both teachers and students had minimal time to get adjusted to the online teaching by acting quickly and getting adapted to the new learning ways[1]. With this, the global use of online examination raised rapidly by moving the programs into completely online mode. In educational institutions and universities, instructors might not have time and suitable circumstances to make and define the test to the students and validate and provide feedback based on the individual’s performance as required. This computerized web-based online exam portal will permit educators to make the test and the students will be able to take up the test[2]. It is a simplified process for both students and teachers with minimal manual effort and no paperwork involved. It also has the capacity to simplify the overall examination activity.
AI based contactless attendance monitoring and management system

The traditional methods of attendance marking need to be replaced with an Artificial Intelligence based attendance monitoring system. The process of checking attendance using the existing methods is time consuming and open to easy fraud. The usage of Bio-metrics poses the threat of viruses. A contact-less Attendance Management System (CAMS) is proposed to overcome the above challenges. AI based attendance monitoring system will record the students attendance automatically while they enter the class, by recognizing their faces. The in-time and out-time of the students are also recorded on a regular basis. The Holistic Face Matching algorithm is used to compare the facial images. The total time duration, the students were present inside the class can be easily calculated. There is no chance of fake attendance as each student has to record his or her facial identity. The main objective is to achieve reliability, accessibility and security in a very efficient way. The results showed that by interfacing a face recognition system with a server, a real-time attendance system can be built and be monitored automatically. Finally, the student’s attendance details can be updated in the academic management system.
KID-Review: Knowledge-Guided Scientific Review Generation with Oracle Pre-training

The surge in the number of scientific submissions has brought challenges to the work of peer review. In this paper, as a first step, we explore the possibility of designing an automated system, which is not meant to replace humans, but rather providing a first-pass draft for a machine-assisted human review process. Specifically, we present an end-to-end knowledge-guided review generation framework for scientific papers grounded in cognitive psychology research that a better understanding of text requires different types of knowledge. In practice, we found that this seemingly intuitive idea suffered from training difficulties. In order to solve this problem, we put forward an oracle pre-training strategy, which can not only make the Kid-Review better educated but also make the generated review cover more aspects. Experimentally, we perform a comprehensive evaluation (human and automatic) from different perspectives. Empirical results have shown the effectiveness of different types of knowledge as well as oracle pre-training. We make all code, relevant dataset available: https://github.com/Anonymous4nlp233/KIDReview as well as the Kid-Review system: http://nlpeer.reviews.
Efficient Deep Learning for Multi Agent Pathfinding

Multi Agent Path Finding (MAPF) is widely needed to coordinate real-world robotic systems. New approaches turn to deep learning to solve MAPF instances, primarily using reinforcement learning, which has high computational costs. We propose a supervised learning approach to solve MAPF instances using a smaller, less costly model.
Application of Mind Mapping Model on the Ability to Write Description Paragraphs of Students of SMPN 9 Denpasar

In learning English, a person is required to master language skills. In the language skill itself, there are 4 components, namely listening skills, speaking skills, reading skills, and writing skills. Each of the skills mentioned has a very close relationship with one another. The four skills are basically a unit in learning a language. This research aimed to describe (1) the writing skill of descriptive paragraph before applying Mind Mapping, (2) improving writing skill of descriptive paragraph after applying Mind Mapping and (3) improving the ability to use word classes after applying Mind Mapping. The method and technique of presenting data in this research is in the description and table form so that it can be meaningful and well organized. The results showed that the last post-test in cycle II reached the indicators of success determined in this classroom action research. An increase in the ability to use word classes also occurred after the application of the Mind Mapping learning model, where in the first cycle students were able to write as many as 10 sentences in a paragraph and an increase also occurred in the use of words (adjectives), while in the second cycle there was also a significant increase. quite satisfactory where the participants were better able to write 12 sentences and were more creative by using more words such as nouns, adjectives, and verbs.&#x0D; Keywords: descriptive paragraph, word classes, Mind Mapping
Deciding Unsolvability in Temporal Planning under Action Non-Self-Overlapping

The field of Temporal Planning (TP) is receiving increasing interest for its many real-world applications. Most of the literature focuses on the TP problem of finding a plan, with algorithms that are not guaranteed to terminate when the problem admits no solution. In this paper, we present sound and complete decision procedures that address the dual problem of proving that no plan exists, which has important applications in oversubscription, model validation and optimization. We focus on the expressive and practically relevant semantics of action non-self-overlapping, recently proved to be PSPACE-complete. For this subclass, we propose two approaches: a reduction of the planning problem to model-checking of Timed Transition Systems, and a heuristic-search algorithm where temporal constraints are represented by Difference Bound Matrices. We implemented the approaches, and carried out an experimental evaluation against other state-of-the-art TP tools. On benchmarks that admit no plans, both approaches dramatically outperform the other planners, while the heuristic-search algorithm remains competitive on solvable benchmarks.
Use of Blockchain Based Techniques in Healthcare: A Study

Blockchain is among the most significant discoveries and innovative trend that plays a critical role in the professional world today. Blockchain will redesign the way patients' electronic health records. Patient’s health records are shared, pre-served and secured in distributed peer-to-peer network in healthcare industries. A systematic organized analysis was undertaken to analyse the Blockchain and healthcare literature to further encourage understanding of Blockchain technologies and to recognize current issues and unanswered questions. This paper's vital contribution is to incorporate preliminary findings from a study of different applications in different areas on the implementation of Blockchain.
Prior-Guided Transfer Learning for Enhancing Item Representation in E-commerce

Item representation learning is crucial for search and recommendation tasks in e-commerce. In e-commerce, the instances (e.g., items, users) in different domains are always related. Such instance relationship across domains contains useful local information for transfer learning. However, existing transfer learning based approaches did not leverage this knowledge. In this paper, we report on our experience designing and deploying Prior-Guided Transfer Learning (PGTL) to bridge this gap. It utilizes the instance relationship across domains to extract prior knowledge for the target domain and leverages it to guide the fine-grained transfer learning for e-commerce item representation learning tasks. Rather than directly transferring knowledge from the source domain to the target domain, the prior knowledge can serve as a bridge to link both domains and enhance knowledge transfer, especially when the domain distribution discrepancy is large. Since its deployment on the Taiwanese portal of Taobao in Aug 2020, PGTL has significantly improved the item exposure rate and item click-through rate compared to previous approaches
Capsule Graph Neural Network for Multi-Label Image Recognition (Student Abstract)

This paper studies the problem of learning complex relationships between multi-labels for image recognition. Its challenges come from the rich and diverse semantic information in images. However, current methods cannot fully explore the mutual interactions among labels and do not explicitly model the label co-occurrence. To overcome these shortcomings, we innovatively propose CGML that consists of two crucial modules: 1) an image representation learning module that aims to complete the feature extraction of an image whose features are expressed in the form of primary capsules; 2) a label adaptive graph convolutional network module that leverages the popular graph convolutional networks with an adaptive label correlation graph to model label dependencies. Experiments show that our approach obviously outperforms the existing state-of-the-art methods.
Aspect-Opinion Sentiment Alignment for Cross-Domain Sentiment Analysis (Student Abstract)

Cross-domain sentiment analysis (SA) has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for deep neural network based methods. However, exiting unsupervised cross-domain SA models ignore the relation between the aspect and opinion, which suffer from the sentiment transfer error problem. To solve this problem, we propose an aspect-opinion sentiment alignment SA model and extensive experiments are conducted to evaluate the effectiveness of our model.
FEATURES OF MEASURING THE «SELF» CONCEPT OF STUDENTS IN THE CONDITIONS OF PROFESSIONAL TRAINING

The article examines the history of the study of the «Self» concept of personality and the process of professional training of students as a pedagogical problem of higher education. The features of using the methods of M. Kuhn and T. McPartland «Who am I» and A. A. Rean and V. A. Yakunin «Studying the motives of students’ educational activity» in the study of the «Self» concepts and their research capabilities, classification of modifications and methods of interpretation are revealed. The results obtained allow us to conclude about the effectiveness of these methods used in combination with other methods in the study of the «Self» concept of students in terms of vocational training. The sample consisted of 237 students of Pavlodar Pedagogical University and Kh. Dosmukhamedov Atyrau University aged from 17 to 23 years, from different courses of study. The subjects were divided into experimental and control study groups. The results of mathematical and statistical processing were obtained using the SPSS Statistics 21 software. The results obtained allow us to conclude about the availability, reliability and validity of these methods when studying the «Self» concept of a student in the conditions of professional training of higher education.
Solar Cell Effects on Electrical Power Networks

Recently, interest in solar energy has increased for many reasons, however, connecting solar power stations in distribution networks may cause many problems, so it was necessary to study the impact of connecting photovoltaic systems to distribution networks to provide solutions feasible in practice. This study includes analyzing the performance of a solar power plant with a capacity of 17.2 kWh connected to the low voltage network of the distribution networks according to Egyptian PV-LV code-2014, simulation using MATLAB/Simulink, and cost analysis for connecting SSPV 17.2 kW to LV distribution networks. The system model was built on MATLAB/Simulink and simulated under daily weather conditions to test the system's operational performance. Power quality parameters are measured using C.A. 8336 power quality analyzer at the inverter output side for the PV system at the Minya Electricity Distribution Sector in Minya Governorate, Egypt. The system produces a 29.314 MWh and reaches its initial cost in 7.2 years. The electrical energy produced by the system is directly injected into the low voltage network for loads of the Minya Electricity Distribution Sector building without batteries or storage devices (on-grid).
User Interface Aplikasi Datangin Customer Berbasis Mobile

The development of technology today has greatly helped and facilitated human work. Almost everything can be associated with internet technology, especially in terms of business, such as on-demand services and marketplaces. PT. Come Solusi Bersama is a technology start-up company engaged in this business. So to simplify and expand the reach of its business, a mobile-based application is needed. With the Datangin Customer application, it will be easier for customers to order services/services that exist on Datangin. In making this mobile-based Datangin Customer application, of course, you have to go through the design stage. Both in terms of system and appearance design. Design thinking is especially useful when dealing with unclear or unknown problems, reframing problems in a human-centered way, brainstorming, using a hands-on approach to prototyping and testing, and trying different concepts and ideas. In this study, the author uses the Design Thinking method and tools in the form of Figma in solving existing problems. It is easier for customers to access the services available on Datangin, unlike before this application, and the interface is user-friendly, attractive, and interactive. Usability testing that the designed user interface can be accepted with a value of 83.33%
EFL Students’ Perception on the Use of Social Media Platforms as Learning Tools

The Covid-19 pandemic has spread to all parts of the world including Indonesia. The existence of the Covid 19 problem has changed many aspects of human life, including in the field of education. Therefore, the use of social media devices has become an alternative learning tool in providing material to students during the Covid-19 pandemic. With this new challenge, every human being is challenged to change old habits into new ones, especially in the world of education. This study aims at determining the EFL students' perceptions regarding the use of various social media tools to support online learning during the Covid-19 pandemic. The method used in this research was qualitative using a descriptive approach. The sampling technique used in this study was random sampling by distributing questionnaires via google form and conducting interviews with 25 selected students to explore students' perceptions about the use of social media in English learning. The data that has been collected is analyzed in an elaborated way. In the application of learning using social media, there are many positive responses given by students, but there are also students who experience some problems in the application of learning using social media. Even among them, they also experience various difficulties in using it, especially in internet access. The results show that the use of social media platforms provides several advantages in its implementation, such as the flexibility of time, the effectiveness of its use, ease, interactive, fun use. However, the challenges are very monotonous learning activities, spending quotas, boredom and difficulty expressing their creativity, bad responses and an erratic schedule.
Solving Visual Analogies Using Neural Algorithmic Reasoning (Student Abstract)

We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs. This program synthesis task can be easily solved via symbolic search. Using a variation of the ‘neural analogical reasoning’ approach, we instead search for a sequence of elementary neural network transformations that manipulate distributed representations derived from a symbolic space, to which input images are directly encoded. We evaluate the extent to which our ‘neural reasoning’ approach generalises for images with unseen shapes and positions.
Adaptive Safe Behavior Generation for Heterogeneous Autonomous Vehicles Using Parametric-Control Barrier Functions (Student Abstract)

Control Barrier Functions have been extensively studied to ensure guaranteed safety during inter-robot interactions. In this paper, we introduce the Parametric-Control Barrier Function (Parametric-CBF), a novel variant of the traditional Control Barrier Function to extend its expressivity in describing different safe behaviors among heterogeneous robots. A parametric-CBF based framework is presented to enable the ego robot to model the neighboring robots behavior and further improve the coordination efficiency during interaction while enjoying formally provable safety guarantees. We demonstrate the usage of Parametric-CBF in behavior prediction and adaptive safe control in the ramp merging scenario.
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages (Student Abstract)

Developing Named Entity Recognition (NER) systems for Indian languages has been a long-standing challenge, mainly owing to the requirement of a large amount of annotated clean training instances. This paper proposes an end-to-end framework for NER for Indian languages in a low-resource setting by exploiting parallel corpora of English and Indian languages and an English NER dataset. The proposed framework includes an annotation projection method that combines word alignment score and NER tag prediction confidence score on source language (English) data to generate weakly labeled data in a target Indian language. We employ a variant of the Teacher-Student model and optimize it jointly on the pseudo labels of the Teacher model and predictions on the generated weakly labeled data. We also present manually annotated test sets for three Indian languages: Hindi, Bengali, and Gujarati. We evaluate the performance of the proposed framework on the test sets of the three Indian languages. Empirical results show a minimum 10% performance improvement compared to the zero-shot transfer learning model on all languages. This indicates that weakly labeled data generated using the proposed annotation projection method in target Indian languages can complement well-annotated source language data to enhance performance. Our code is publicly available at https://github.com/aksh555/CL-NERIL.