Reviewerly/exHarmony huggingface dataset:
Optimal distance query reconstruction for graphs without long induced cycles

Let $G=(V,E)$ be an $n$-vertex connected graph of maximum degree $\Delta$. Given access to $V$ and an oracle that given two vertices $u,v\in V$, returns the shortest path distance between $u$ and $v$, how many queries are needed to reconstruct $E$? We give a simple deterministic algorithm to reconstruct trees using $\Delta n\log_\Delta n+(\Delta+2)n$ distance queries and show that even randomised algorithms need to use at least $\frac1{200} \Delta n\log_\Delta n$ queries in expectation. The best previous lower bound was an information-theoretic lower bound of $\Omega(n\log n/\log \log n)$. Our lower bound also extends to related query models including distance queries for phylogenetic trees, membership queries for learning partitions and path queries in directed trees. We extend our deterministic algorithm to reconstruct graphs without induced cycles of length at least $k$ using $O_{\Delta,k}(n\log n)$ queries, which includes various graph classes of interest such as chordal graphs, permutation graphs and AT-free graphs. Since the previously best known randomised algorithm for chordal graphs uses $O_{\Delta}(n\log^2 n)$ queries in expectation, we both get rid off the randomness and get the optimal dependency in $n$ for chordal graphs and various other graph classes. Finally, we build on an algorithm of Kannan, Mathieu, and Zhou [ICALP, 2015] to give a randomised algorithm for reconstructing graphs of treelength $k$ using $O_{\Delta,k}(n\log^2n)$ queries in expectation.
A Novel Codebook Generation by Smart Fruit Fly Algorithm based on Exponential Flight

A codebook is a combination of vectors that represents a digital image best and very useful tool for compression. Besides the well-known techniques such as Linde-Buzo-Gray, C-Means, and Fuzzy C-Means the nature-inspired metaheuristic algorithms have also become alternate techniques for solving the codebook generation problem. Fruit Fly Optimization Algorithm (FFA) is a simple and efficient algorithm, but the capturing of an agent by a local minimum point is the main problem. Therefore, the fruit flies generally do not reach the global solution at the end of the iterations. In this study, the FFA is empowered with a smart exponential flight approach to finding out a global optimum codebook. In this approach, if a fruit fly agent is captured by a local minimum point accidentally, the smart exponential flight steps provide an opportunity to escape from it easily. In the experimental studies, successful compression results have been taken in terms of lower error rates. The numerical results prove that the proposed Smart Exponential flight-based Fruit Fly Algorithm (SE-FFA) is better than the variations of convolutional FFA by providing a global optimum codebook.
Statistical Analysis of Bias in ChatGPT Using Prompt Engineering

Abstract: ChatGPT is a leading Large Language Model trained on an extensive and diverse assortment of text data. However, the utilization of potentially biased training data from the internet corpora could lead to fundamental bias introduced in the model, which will subsequently reflect on its generated output. This paper quantifies bias present in GPT-3.0 model responses on various controversial topics using carefully engineered prompts. We measured raw bias in each generated response by leveraging the Bipartisan Press API. Using statistical methods such as the T-test and ANOVA on raw bias measurements, we tested our hypothesis. Our results demonstrate that there is statistically significant left leaning bias present in 9 out of the 11 controversial topics we tested. Further, ANOVA analysis shows that the bias present varies based on topics. We posit that our findings could be instrumental in guiding future efforts to mitigate training bias and address the larger alignment problem present in generative AI.
Handwritten Text Recognition from Image

Abstract: A computer vision program called Handwritten Text Recognition (HTR) attempts to recognize and translate handwritten text from scanned or photographed images. In this project, we suggest implementing an HTR system using Tesseract and OpenCV. English, Chinese, and Arabic are all supported by the popular open-source optical character recognition (OCR) engine known as Tesseract. It is employed to find and identify printed text within photographs. On the other hand, OpenCV is a well-liked computer vision library that offers several tools for processing and analyzing images. The pre-processing step of the proposed system uses OpenCV to increase the input image's quality and OCR accuracy. After that, Tesseract receives the pre-processed image for text recognition. The extracted text is then saved in a text file after being identified. To enhance the quality of the input image, the project will use several pre-processing techniques, including deskewing, noise removal, and binarization. With the help of a sizable dataset of handwritten photographs, the Tesseract OCR engine is taught to recognize handwritten text more accurately. The HTR system can be used in a variety of fields, including document analysis, historical manuscript digitalization, and postal automation. It can also be applied in academic settings to help students translate their notes and assignments. Therefore, it is anticipated that the proposed HTR system employing Tesseract and OpenCV will offer a reliable and effective method for identifying and transcribing handwritten text from photographs.
Comprehensive risk assessment and analysis of blockchain technology implementation using fuzzy cognitive mapping

Identifying and assessing potential risks of implementing new technologies is critical for organizations to respond to them efficiently during the technology life cycle. Blockchain has been introduced as one of the emerging and disruptive technology in the field of information technology in recent years, which system developers have noted. In this study, a comprehensive set of risks have been identified and categorized based on the literature findings to identify the risks of blockchain implementation. Critical risks are defined by performing a two-stage fuzzy Delphi method based on the experts' opinions. Then, possible causal relationships between considered risks are identified and analyzed using the fuzzy cognitive mapping method. Finally, the most important risks are ranked based on the degree of prominence and the relationships between them. Industry enterprise resource planning system based on blockchain technology has been studied as a case study. The obtained results indicate that the technology's immaturity has the most impact, the high investment cost is the most impressive risk, and privacy has a critical role in risks relationships. In addition, the high investment cost has the highest priority among other risks and the privacy and issues with contract law are ranked second and third, respectively.
Development and Quality Analysis of Web-Based Library Information System based on ISO 9126 Standard

Management of book collection at the Department of Electrical Engineering, State University of Malang is less effective and efficient because the management process is carried out manually and centrally on a single computer (stand alone). This research purposes is to describe, develop, and test a reference book management information system based on Model View Controller (MVC) in the Department of Electrical Engineering, State University of Malang. The development model that used in this research is waterfall model which consists of analysis, design, implementation, and testing step. Testing is carried out to determine the quality of the developed system. Testing of the product in this research is using ISO 9126 indicators with aspects of functionality, and usability. The results showed that: (1) the reference book management information system was developed using the Codeigniter framework and the waterfall development model consisting of the needs analysis, design, implementation, and testing step, and (2) the test results on the functionality aspect obtained a value of 1 ( good). Usability testing obtained a result of 4.03 (very feasible) obtained from the results of the average assessment of students and librarian responden.
Spatial Pyramid Pooling and Adaptively Feature Fusion based Yolov3 for Traffic Sign Detection

Traffic sign detection is a key part of intelligent assisted driving, but also a challenging task due to the small size and different scales of objects in foreground and closed range. In this paper, we propose a new traffic sign detection scheme: Spatial Pyramid Pooling and Adaptively Spatial Feature Fusion based Yolov3 (SPP and ASFF-Yolov3). In order to integrate the target detail features and environment context features in the feature extraction stage of Yolov3 network, the Spatial Pyramid Pooling module is introduced into the pyramid network of Yolov3. Additionally, Adaptively Spatial Feature Fusion module is added to the target detection phase of the pyramid network of Yolov3 to avoid the interference of different scale features with the process of gradient calculation. Experimental results show the effectiveness of the proposed SPP and ASFF-Yolov3 network, which achieves better detection results than the original Yolov3 network. It can archive real-time inference speed despite inferior to the original Yolov3 network. The proposed scheme will add an option to the solutions of traffic sign detection with real-time inference speed and effective detection results.
Quantum Internet Addressing

The design of the Quantum Internet protocol stack is at its infancy and early-stage conceptualization. And different heterogeneous proposals are currently available in the literature. The underlying assumption of the existing proposals is that they implicitly mimic classical Internet Protocol design principles: "A name indicates what we seek. An address indicates where it is. A route indicates how to get there''. Hence the network nodes are labeled with classical addresses, constituted by classical bits, and these labels aim at reflecting the node location within the network topology. In this paper, we argue that this twofold assumption of classical and location-aware addressing constitutes a restricting design option, which prevents to scale the quantumness to the network functionalities, beyond simple information encoding/decoding. On the contrary, by embracing quantumness within the node addresses, quantum principles and phenomena could be exploited for enabling a quantum native functioning of the entire communication network. This will unleash the ultimate vision and capabilities of the Quantum Internet.
Impact of Terrorism on Foreign Direct Investment in South Asian Countries: Evidence from Heterogeneous Panel Analysis

This research investigates the impact of terrorism and peace on foreign direct investment (FDI) in South Asian countries comprising eight economies.The study initially examines the effects of terrorism, GDP, inflation, and trade openness on the FDI of South Asian countries.Subsequently, it explores the influence of peace, GDP, inflation, and trade openness on the FDI of the same economies.The sample data covers a time frame from 2011 to 2021.The panel-regression framework is employed as the primary technique for data analysis.Moreover, to examine the integration among the variables under consideration, Pedroni cointegration and FMOLS regression methods are applied.The outcomes of the articles support the initial aim of the study by noting down a considerable nexus of both peace and terrorism as well as relative economic proxies in driving FDI of South Asian economies.The result strongly suggests that terrorism in these countries is one of the pioneering factors for predicting FDI.The study concludes that an increase in terrorism declines the flow of FDI, while an improvement in peace enhances FDI.Likewise, other indicators such as inflation, trade openness, and GDP also contribute to changes in FDI.
Preface

The Geography Education Program, Faculty of Teacher Training and Education, Sebelas Maret University has successfully held an “International Conference on Anthropocene, Global Environmental Change and Powerful Geography (ICoAGPG)” via the online zoom system on August 27 th , 2022. ICoAGPG aims to disseminate research results, various ideas on controlling environmental degradation and climate change for a better life and providing input to the government and other policy-making institutions in environmental management to anticipate climate change. The theme of 2022 is the relational understanding of extractive industries, climate change and exacerbation of natural disasters and social injustice. ICoAGPG invited great speakers who are very competent in the global environmental impacts, and they are Prof. Tania Murray Li (University of Toronto, Canada), Dr. Janice Lee (Nanyang Technology University, Singapore), Dr. Erma Yulihastin (Research Center for Climate and Atmosphere, National Research and Innovation Agency), Dr. Yasin Yusup, M.Si (Sebelas Maret University). Topic issues for this seminar are land degradation and land conservation, biodiversity and ecosystem services, forest-based adaptation, hydro-meteorological, biological, and geographysical disaster, water resource management, integrated coastal zone management, air pollution, greenhouse effect and Urban Heat Island (UHI), mitigation and adaptation to climate change, climate change policy, social safety nets and livelihood diversification, environmental economic and law, education for sustainable development, regional planning for disaster management, disaster risk management, resilient cities and communities, green infrastructure and sustainability, sustainable land use and urban planning. There were 86 articles submitted according to the topic above, and after being selected, there were 62 articles chosen to be presented and submitted to the IOP-EES Proceedings. Article authors consist of lecturers, practitioners, teachers and students. The participants who joined at the time of the seminar was 166 participants. Thanks to all of the speakers, authors and participants. See you at the second ICoAGPG. Best Regards Dr. Pipit Wijayanti, S.Si., M.Sc Chair of the committee List of Committee, International Conference on Anthropocene, Global Environmental Change and Powerful Geography (ICoAGPG) 2022 are available in the pdf.
Twitter Mining for Detecting Home Violence

Home Violence (HV) is any pattern of conduct intended to obtain or retain power and control over an intimate partner in any relationship. Identifying the HV occurrences will be critical in reducing them. Social Media (SM) is a vital and information-rich tool. HV-related information could well be detected using SM. The research proposed a method to detect HV-related Tweets. More than 20,000 Tweets were retrieved between May 2019 to May 2020 using Twitter API. Data preprocessing and word embedding were done, respectively. Then, to construct the model, the data set was split into training and testing datasets to detect HV-related Tweets; a deep learning model, LSTM, with different word embedding techniques, was used in this research (LSTM+TF-IDF, LSTM+BOW, LSTM+Word2Vec, LSTM+GloVe and LSTM+BERT). With an accuracy of 89.56%, the LSTM+BERT model surpassed the other implemented models.
PERBEDAAN HASIL BELAJAR MATEMATIKA SISWA MENGGUNAKAN METODE PEMBELAJARAN DARING DIMASA PANDEMI COVID-19

This study aims to determine learning outcomes using the Google Classroom and Webinar Zoom platformsduring the Covid-19 pandemic. The type of research used is experimental research. The results of this study indicate that the average pretest value of the experimental class is 18.75 and the average value of the pretest experimental class 2 is 17.50, while the posttest average value of the experimental class 1 is 40.83 and the posttest average value experimental class 2 that is equal to 48.75. Furthermore, from the results of the independent sample t-test in the equal variances assumed section, it is known that the t-count value is -1.331, which means the average value in the experimental class 1 is lower than the average value of the experimental class 2, this indicates that there are differences in learning outcomes. mathematics students who use the Google Classroom and Webinar Zoom platforms during the Covid-19 pandemic for class XI IPA students at SMA Negeri 6 Padangsidimpuan.
Impact of Human Capital Index on Economic Growth in Africa

This study investigates the impact of the Human Capital Index (HCI) on economic growth (EG) in Ghana, Kenya, and Tanzania using various statistical techniques. The results indicate that the relationship between HCI and EG is complex and varies by country. The coefficient for TZA is positive, while GHA and KE are negative. However, these coefficients are not statistically significant, and the overall coefficient for all countries is negative and not statistically significant. This suggests that other factors such as investment in infrastructure, education, and innovation may contribute to EG. Policymakers should consider a range of factors beyond HCI when formulating policies to promote EG in these countries. Further research is necessary to identify potential predictors of EG and explore the relationships among these factors in more detail.&#x0D; &#x0D;
ANALISIS KEMAMPUAN LITERASI NUMERASI DALAM MENYELESAIKAN SOAL PROGRAMME FOR INTERNATIONAL STUDENT ASSESSMENT (PISA)

This study aims to reveal deeper numeracy literacy skills in solving the Program for International Student Assessment (PISA) questions. This research was conducted at SMAS Cerdas Murni Tembung in the odd semester of the 2022/2023 academic year. The research subjects consisted of 28 students of class X MIPA-1. The research uses qualitative methods with a descriptive approach. The research instruments used were PISA test questions and interviews. The results showed that 44% of student were able to fulfill the ability indicators in using syimbols, numbers, and calculate mathematical operations contained in questions at levels 1, 4, and 5. In the indicator of the ability to analyze information from tables, graphs, diagrams, schematics, etc., it was obtained that 57,1% of students were able to solve PISA level 3. As well as indicators of skills in applying concepts as well as making decisions contained in level 2 and level 6 questions, 42,3% of students were able to meet these indicators. So that, the overall numeracy literacy ability of class X MIPA-1 students can be categorized as lacking in solving the Program For International Student Assessment (PISA) questions.
Tsunami Memories in Poetry and the Potential of “Dark Tourism”

This paper will discuss about a major disaster that has occurred in Aceh, Indonesia.Gigantic tsunami that once occurred in 2004 has left a horrifying and gloomy memory to those who went through it.The objective of writing this paper is to analyze how the poets has recorded the reminiscences of disaster in their poems.This main issue has not been documented by poets in Indonesia, but also done by famous poets from Malaysia through beautiful verses and emotional impact.The second objective is to study the effects that occurred after the disaster in Indonesia.Based on that, this study will illustrate a question that there is still a positive impact on the local community as a result of the disaster.Even though the general associate this disaster with all forms of pain and suffering?Thus, this paper will look at the potential of "Dark Tourism" in Aceh that was created after the disaster.The method of research will be composed from dark tourism conceptual framework to analysis the selective poetry and also to answer the objective and problem statement of the study.
Road conditions monitoring using semantic segmentation of smartphone motion sensor data

Many studies and publications have been written about the use of moving object analysis to locate a specific item or replace a lost object in video sequences.Using semantic analysis, it could be challenging to pinpoint each meaning and follow the movement of moving objects.Some machine learning algorithms have turned to the right interpretation of photos or video recordings to communicate coherently.The technique converts visual patterns and features into visual language using dense and sparse optical flow algorithms.To semantically partition smartphone motion sensor data for any video categorization, using integrated bidirectional Long Short-Term Memory layers, this paper proposes a redesigned U-Net architecture.Experiments show that the proposed technique outperforms several existing semantic segmentation algorithms using z-axis accelerometer and z-axis gyroscope properties.The video sequence's numerous moving elements are synchronised with one another to follow the scenario.Also, the objective of this work is to assess the proposed model on roadways and other moving objects using five datasets (self-made dataset and the pothole600 dataset).After looking at the map or tracking an object, the results should be given together with the diagnosis of the moving object and its synchronization with video clips.The suggested model's goals were developed using a machine learning method that combines the validity of the results with the precision of finding the necessary moving parts.Python 3.7 platforms were used to complete the project since they are user-friendly and highly efficient platforms.
Generating Embedding Features Using Deep Learning for Ethnics Recognition

Although significant advances have been made recently in the field of ethnics recognition through face recognition, there is still a lack of studies of ethnics recognition through facial recognition. This study is concerned with ethnics recognition through facial representation using a few images used as samples for any selected group of ethnics using a deep neural network with a Variational Feature Learning (VFL) loss function that has been used to increase the performance accuracy during the evaluation process. The output of a deep neural network is an embedding of 128 bytes for each face image in each group of ethnics. After that, all embeddings of every face in each group of ethnics pass to a machine learning classification method like a Support Vector Machine (SVM). We achieved state-of-the-art ethnic recognition. The system achieved a classification accuracy of 97.3% on a collected group of image dataset collected from three different countries.
MANAJEMEN PENGELOLAAN MASJID JAMI’ KERATON ISMAHAYANA LANDAK

Mosque management is very important to do in order to create ideal conditions in an institution or mosque, so the management pattern must be arranged as well as possible in order to create effectiveness in the organizational structure. This study discusses the management of the Jami' Keraton Ismahayana Landak Mosque to find out whether the implementation has implemented an effective management pattern. The results of the study can be seen that the implementation of the management of the Jami' Keraton Ismahayana Landak mosque there is a management application, the activity programs that have been made have also gone through the planning stage which is the main factor if these activities are to be realized. So the things that are needed, from determining the implementing committee, the equipment to be used, maintaining the cleanliness of the mosque environment and preparing worship facilities and infrastructure have gone through the planning stage through a meeting held by the mosque management and also involving the Ismahayana Landak Palace. This study also aims to find out how the management functions are managed by the administrators of the Jami' Keraton Ismahaya Landak Mosque, and also want to know the relationship between the supporting and inhibiting factors in the prosperity of the Jami' Keraton Ismahayana Landak Mosque. The method used in this research is using qualitative research with a management approach, more specifically using descriptive qualitative research. While the technique used in this research is the method of observation, interview method and method of documentation.&#x0D;