Reviewerly/exHarmony huggingface dataset:
Comparison and Incorporation of Reasoning and Learning Approaches for Cancer Therapy Research

Representing knowledge in a comprehensible and maintainable way and transparently providing inferences thereof are important issues, especially in the context of applications related to artificial intelligence in medicine. This becomes even more obvious if the knowledge is dynamically growing and changing and when machine learning techniques are being involved. In this paper, we present an approach for representing knowledge about cancer therapies collected over two decades at St.-Johannes-Hospital in Dortmund, Germany. The presented approach makes use of InteKRator, a toolbox that combines knowledge representation and machine learning techniques, including the possibility of explaining inferences. An extended use of InteKRator's reasoning system will be introduced for being able to provide the required inferences. The presented approach is general enough to be transferred to other data, as well as to other domains. The approach will be evaluated, e. g., regarding comprehensibility, accuracy and reasoning efficiency.
On the Separability of Functions and Games

We study the notion of (additive) separability of a function of several variables with respect to a hypergraph (Hgraph). We prove the existence of a unique minimal H-graph with respect to which a function is separable and show that the corresponding minimal decomposition of the function can be obtained through a recursive algorithm. We then focus on (strategic form) games and propose a concept of separability for a game with respect to a forward directed hypergraph (FDHgraph). This notion refines and generalizes that of graphical game and is invariant with respect to strategic equivalence. We show that every game is separable with respect to a minimal FDHgraph. Moreover, for exact potential games, such minimal FDHgraph reduces to the minimal H-graph of the potential function. Our results imply and refine known results on graphical potential games and yield a new proof of the celebrated HammerselyClifford Theorem on Markov Random Fields.
IMPLEMENTASI FILTER SKETSA CITRA DIGITAL MELALUI OPERASI KONVOLUSI DENGAN MATRIK KERNEL LAPLACE OF GAUSS

Sketch filter is used usually as a preliminary process of the images due to further processing such as in pattern recognition of a certain object by deep learning method. Several types of sketch filter can be used to detect the edge of object. In this paper the implementation of convolution operation by the Laplace of Gauss kernel matrix is explored to be used as a sketch filter to detect the edge of object.
An Analysis of the Right of Freedom of Speech and Expression: A Case Study of Pakistan

Freedom of expression includes not just the right to free speech but also the freedom to practice one's religion, exercise unrestricted democratic rights, access the media, and live without prejudice. However, these freedoms are not well protected by the existing dictatorial statutory framework. Religious leaders and a few other institutions regularly exploit the exceptions provided by the right to free expression to incite intolerance, accuse Muslims and other minority groups of committing sacrilege and restrict access to information due to "public safety" concerns. This research article seeks to explore the actual situation of fundamental rights protected by the constitution of Pakistan especially freedom of speech. In the last few years, we can see that there is a steep rise in the number of victims targeted because they expressed their views freely. Freedom of speech does not extend to public speaking only it also includes columns, editorials, journals, books, blogs or even expressing themselves on social media is also included in freedom of speech and recently the government passed the PECA act which limit or put restriction on fundamental freedom secured by the constitution to intimidate the populace. The author also aims to show the reasons, how, and why freedom of speech is limited or repressed. The author will also investigate the possible consequences and solutions available to solve this issue. Indeed, sexual harassment will not halt by simply ignoring it. The author will even try to highlight the fact that the government must protect and secure fundamental liberties enjoyed by people and not limit them.
Evolution of the Content and Quality of Standardized Tests in Mathematics in Ukraine

The evolution of the content and quality of written state standardized final and entrance testing in mathematics in Ukraine, from the first attempt to implement in 1994 until now, is considered in the article. We describe the features and stages of development of Ukrainian standardized tests in mathematics (external independent assessment and state final attestation), analyze the causes of successes and failures and also prospects for their further improvement. Based on our own statistical experiment, we compare the quality of mathematical preparation of Ukrainian graduates who entered the pedagogical universities of Ukraine in 1994, 2005 and 2020. According to this experiment, we draw conclusions about the existing problems of ensuring the proper quality of mathematics education in Ukraine and give recommendations on possible solutions to these problems.
Predicting Fraud in U.S.-Listed Chinese Companies: An Empirical Analysis based on M-Score and F-Score Models

M-Score and F-Score are financial fraud prediction models that are commonly used in Western capital markets. In this paper, we calculate M-Scores and F-Scores for each dataset of 15 fraudulent Chinese companies (including Hong Kong companies) listed in the U.S. from 2006 to 2020 and compare their M-Scores and F-Scores with 15 companies in the same industry that are not fraudulent. Firstly, we used Student's t-test on data from both models respectively. The result shows that both of them are able to predict fraud well for Chinese stocks listed in the U.S. With the result, we further compare and test the differences between the financial fraudulent samples and the comparison samples in the dimensions of each variable in the M-Score and F-Score models. Finally, we draw a result that they are effective in predicting whether a Chinese company committed financial fraud.
Algoritma Deep Learning dalam Memprediksi Hasil Panen Padi di Kabupaten Lamongan

Based on data, bps.go.id harvest from the 2nd year 018 to 2019 decreased about an 7.76%. The government must constantly analyze the rice yields of farmers in Indonesia to determine whether these crops can meet the Indonesian people's primary food needs. Research this will predict rice yields in Lamongan. This study aims to assist the government in overcoming the occurrence of significant food shortages in Lamongan. A system that can be used as a reference tool to assist in policy or rule in the district Lamongan. This research proposes deep learning algorithms to predict the harvest based on the land area (m2), spacing (cm), the type of rice, the number of times to fertilize, fertilizer, and crop yields (quintals). The dataset used in the study was collected through questionnaires. Questionnaires were distributed via a google form and contained as many as 390 rows of data. Some of the data produced were incorrect, so the processing was carried out. The results of data processing, the data that can be used are 380 rows. The proposed architectural model's test results show that the loss values of MSE, MAE, or MAPE are the same. The MSE, MAE, and MAPE values are 2939977.418, 301,788, and 83,798, respectively.
BI-FERH: Blockchain-IoT based framework for securing smart hotel

IoT devices and applications are growing rapidly as a result of the advancement of IoT technology. In the case of smart hotels with many IoT devices, the majority of the data generated by those devices contains the private information of users, which is susceptible to being changed and leaked during transmission and storage. To overcome it, this paper proposes a blockchain-IoT based Framework for securing smart hotels(BI-FERH) to enhance the security of hotel information systems. The high performance BI-FERH architecture takes advantage of real-time data transmission capabilities offered by IoT devices. Sensitive data generated by IoT devices is protected in BI-FERH, enhancing tamper-proof capabilities. The results of the experiment demonstrate that BI-FERH can increase the security of smart hotel systems while preserving operational efficacy. An innovative and safe solution for the information management system of smart hotels is offered by the BI-FERH framework.
An Error-Based Measure for Concept Drift Detection and Characterization

Continual learning is an increasingly studied field, aiming at regulating catastrophic forgetting for online machine learning tasks. In this article, we propose a prediction error measure for continual learning, to detect concept drift induced from learned data input before the learning step. In addition, we check this measure’s ability for characterization of the drift. For these purposes, we propose an algorithm to compute the proposed measure on a data stream while also estimating concept drift. Then, we calculate the correlation coefficients between this estimate and our measurement, using time series analysis. To validate our proposal, we base our experiments on simulated streams of metadata collected from an industrial dataset corresponding to real conversation data. The results show that the proposed measure constitutes a reliable criterion for concept drift detection. They also show that a characterization of the drift relative to components of the stream is possible thanks to the proposed measure.
Cultural Contents in Digital Authentic Greeting Cards as English Teaching Materials for Junior High Schools

This study aimed to discover the cultural contents in the digital authentic greeting cards as English teaching materials for junior high schools. The research data were the cultural values in the form of words, phrases, clauses, or sentences that are contained in the texts on the digital authentic greeting cards. The data sources were taken from several websites providing free digital greeting cards to be downloaded. The data were analyzed with the framework of the cultural dimensions by Moran (2001) and the cultural sources by Chao (2011) by using a qualitative content analysis method. The findings revealed 101 cultural contents that were classified into the four cultural dimensions and three sources of culture. The cultural contents were mostly identified in the Universality across Culture and represented by the Practices dimension through activities and language expressions. It was indicated that a large proportion of the cultural contents were included in universal contents that are not specific to any particular country and were represented by actions and interactions carried out by the cultural members.
Hierarchical Adversarial Inverse Reinforcement Learning

Imitation learning (IL) has been proposed to recover the expert policy from demonstrations. However, it would be difficult to learn a single monolithic policy for highly complex long-horizon tasks of which the expert policy usually contains subtask hierarchies. Therefore, hierarchical IL (HIL) has been developed to learn a hierarchical policy from expert demonstrations through explicitly modeling the activity structure in a task with the option framework. Existing HIL methods either overlook the causal relationship between the subtask structure and the learned policy, or fail to learn the high-level and low-level policy in the hierarchical framework in conjuncture, which leads to suboptimality. In this work, we propose a novel HIL algorithm-hierarchical adversarial inverse reinforcement learning (H-AIRL), which extends a state-of-the-art (SOTA) IL algorithm-AIRL, with the one-step option framework. Specifically, we redefine the AIRL objectives on the extended state and action spaces, and further introduce a directed information term to the objective function to enhance the causality between the low-level policy and its corresponding subtask. Moreover, we propose an expectation-maximization (EM) adaption of our algorithm so that it can be applied to expert demonstrations without the subtask annotations which are more accessible in practice. Theoretical justifications of our algorithm design and evaluations on challenging robotic control tasks are provided to show the superiority of our algorithm compared with SOTA HIL baselines. The codes are available at https://github.com/LucasCJYSDL/HierAIRL.
Robust OFDM System for Drilling Data Transmission Using Acoustic Wave

Drilling data communication mainly includes mud pulse telemetry and electromagnetic wave telemetry. These methods have the advantages of economy and convenience, but they also have the disadvantages of low data transmission rate and large signal attenuation. A data transmission system while drilling was designed based on the key technologies and basic principles of Orthogonal Frequency Division Multiplex (OFDM). The hardware of the system includes the Universal Software Radio Peripheral (USRP) motherboard and daughter board, power amplifier circuit and piezoelectric transducer. The software used in the system is C and Python. Compared with traditional methods, new methods using acoustic signals have many advantages. This new wireless transmission system was designed by combining down-hole data transmission with wireless communication. This method has advantages in oil exploration and development.
UnderRL Tagger: a free software for Under-Resourced Languages POS tagging

This chapter presents a free software program that can be used for POS tagging in a multiplicity of languages that do not have automatic taggers. The program aims to facilitate the work with corpora in these languages through Natural Language Processing. Its operation allows the manual tagging process to be gradually automated thanks to a system that makes it possible to recall and reuse tags, as well as to handle large amounts of text and to generate output files in XML format with tags based on the EAGLES system. En este capítulo se presenta un software libre que puede utilizarse para el etiquetado de POS en una multiplicidad de lenguas que no cuentan con etiquetadores automáticos. El programa busca facilitar el trabajo con corpus en estas lenguas a través de la lingüística computacional. Su funcionamiento permite que el proceso manual de etiquetado se convierta poco a poco en automático gracias a un sistema que permite recordar y reutilizar las etiquetas, de la misma manera en que permite manejar grandes cantidades de textos y generar archivos de salida en formato XML con etiquetas basadas en el sistema EAGLES.
Exploring Factors Influencing Cryptocurrency Adoption: A Comprehensive Modeling Based on Fuzzy Cognitive Maps Approach

Cryptocurrencies, with their decentralized nature, are gaining rapid international adoption as a means of payment or a valuable digital asset, independent of the economic policies of governments and without the need for a supervisory institutions such as banks. However, limited research has been conducted on the adoption of cryptocurrencies, most of which employ a general technology acceptance/ adoption model with a positivist approach. The main problem with previous studies is that they have been limited to the structure of general adoption models and only examined a few constructs due to the increasing complexity of the model. On the other hand, due to cryptocurrencies' unique nature and rapid developments, it is necessary to create new comprehensive models that include different dimensions. This paper aims to identify influential factors in the adoption of cryptocurrency technology, understand their interrelationships, and ultimately develop a comprehensive model. With a constructivist approach, this study uses the most important research of the past decade in the field of cryptocurrency adoption and creates a cognitive model of their constructs through a systematic approach. The focal point of our approach is constructivism, accompanied by considering the impact of constructs on each other using fuzzy cognitive maps, which has not been previously done in cryptocurrency adoption. The results of the proposed model indicate that perceived usefulness, attitude, financial value, and perceived ease of use are the most significant constructs that influence the creation of positive intention toward the use and adoption of cryptocurrencies.
PERENCANAAN PUSAT INFORMASI KEBUDAYAAN DI TENGGARONG DENGAN PENERAPAN DOUBLE SKIN FACADE

The cultural information center is a building specially built as a place for cultural information as well as a place for discussion, learning and as a place to accommodate cultural festival events, especially in the Kutai Kartanegara district in the city of Tenggarong. Climate problems and also the aesthetics of buildings needed in the current era of globalization make cultural information center buildings also need to pay attention to the needs of the current generation of social media, the application used is the Double Skin Facade system. Activities that are accommodated in the Cultural Information Center with the application of the Double skin facade do not yet exist in Tenggarong, so the importance of the role of the cultural information center building so that cultural activities in Tenggarong City can be facilitated, for example: Erau traditional festival which in this building has several buildings including building for main, supporting, main office and service activities, in this case the main activities (tourism education information and training) and other supporting activities.
Recurrence-Driven Summations in Automated Deduction

Abstract Many problems in mathematics and computer science involve summations. We present a procedure that automatically proves equations involving finite summations, inspired by the theory of holonomic sequences. The procedure is designed to be interleaved with the activities of a higher-order automatic theorem prover. It performs an induction and automatically solves the induction step, leaving the base cases to the theorem prover.
A New Decision Making Method for Selection of Optimal Data Using the Von Neumann-Morgenstern Theorem

The quality of the input data is amongst the decisive factors affecting the speed and effectiveness of recurrent neural network (RNN) learning. We present here a novel methodology to select optimal training data (those with the highest learning capacity) by approaching the problem from a decision making point of view. The key idea, which underpins the design of the mathematical structure that supports the selection, is to define first a binary relation that gives preference to inputs with higher estimator abilities. The Von Newman Morgenstern theorem (VNM), a cornerstone of decision theory, is then applied to determine the level of efficiency of the training dataset based on the probability of success derived from a purpose-designed framework based on Markov networks. To the best of the author’s knowledge, this is the first time that this result has been applied to data selection tasks. Hence, it is shown that Markov Networks, mainly known as generative models, can successfully participate in discriminative tasks when used in conjunction with the VNM theorem. The simplicity of our design allows the selection to be carried out alongside the training. Hence, since learning progresses with only the optimal inputs, the data noise gradually disappears: the result is an improvement in the performance while minimising the likelihood of overfitting.
Pengaruh Fasilitas Laboratorium Akuntansi, Pengantar Akuntansi, dan Computer Knowledge Terhadap Hasil Belajar Komputer Akuntansi Dengan Intensitas Belajar Sebagai Variabel Moderasi

This study aimed to analyze learning intensity can moderate the influence of accounting laboratory facilities, introductory understanding of accounting, and computer knowledge on learning outcomes of accounting computers for class XI AKL AKL SMKN 2 Buduran. This type of research is including quantitative research. The samples used were 108 students of class XI AKL SMKN 2 Buduran using saturated sampling. Data collection techniques using questionnaires, interviews, and documentation. The data analysis technique used Structural Equation Model Partial Least Square (SEM PLS) with SmartPLS 3.0 software. The results of the study show that: (1) There is a significant influence of accounting laboratory facilities on the learning outcomes of accounting computers. (2) There is a significant influence of the introductory understanding of accounting on the learning outcomes of accounting computers. (3) There is no significant influence of computer knowledge on the learning outcomes of accounting computers. (4) Learning intensity strengthens the influence of accounting laboratory facilities on the learning outcomes of accounting computers. (5) Learning intensity strengthens the influence of an introductory understanding of accounting on the learning outcomes of accounting computers. (6) Learning intensity weakens the influence of computer knowledge on the learning outcomes of accounting computers.
The Effectiveness of E-Books in Learning: An Analysis of Trends in Elementary Schools

Less than optimal student learning outcomes are a problem experienced by teachers in elementary schools. It is evidenced by the student learning outcomes that have not been maximized. The objective of this study is to provide a comprehensive description of the effectiveness of incorporating e-books into the learning process. This study used a quantitative approach with a time series design. The research subjects consisted of elementary school students and teachers. The student subjects involved came from the fifth-grade level. Data collection was done by testing and observation. Data analysis was performed by testing four trend models. These are the Linear Trend Model, Quadratic Trend Model, Growth Curve Model, and S-Curve Trend Model. The research findings indicate that e-books are a viable tool for facilitating learning in elementary school settings. It is based on the positive trend shown in student learning outcomes after applying the e-book as a learning tool. It strengthens the recommendation regarding implementing e-books by teachers to maximize student learning outcomes.
Enhancing Credit Card Fraud Detection Using P-XGBoost: A Comparative Study Classical Machine Learning Techniques

Abstract: Ability of debit card companies to detect and prevent fraudulent transactions is crucial to safeguard customers from unauthorized charges. Data Science, particularly Machine Learning, plays a pivotal role in addressing this challenge. This project aims to demonstrate the application of machine learning in Credit debit Fraud Detection by modeling a dataset of past credit card transactions, distinguishing fraudulent ones from legitimate ones. The objective is to achieve a fraudulent transactions while minimizing false positives. Debit Card Fraud Detection is a classic classification issue. A research focuses on data analysis, preprocessing, and the utilization of XGBoost on Credit Card Transaction data. To prevent overfitting, grid search is employed to fine-tune the models' hyperparameters. The performed of XGBoost and P-XGBoost is compared with further usual machine learning techniques. Surprisingly, P-XGBoost best XGBoost in fraud detection, presenting a viewpoint for effectively identifying fraudulent behavior while ensuring the privacy of clients.