Reviewerly/exHarmony huggingface dataset:
Pengukuran Prestasi Belajar Matematika Siswa dengan Menggunakan Tes Model Testlet ditinjau dari Status Sekolah dan Gender

This study aims to determine the differences in students' mathematics learning achievement as measured using the testlet model test in terms of school status and gender in class VII SMP/MTs students. This study uses a quantitative approach in the form of ex post facto research. This study took samples from 2 public schools and two private schools with a total sample of 109 students. The sampling technique used is purposive sampling. Data collection techniques using test techniques and instruments using a testlet model test based on local wisdom. The instrument's validity uses content validity with the Aiken formula, while the reliability, level of difficulty of distinguishing power, and the effectiveness of distractors are analyzed by the ITEMAN 3.0 application. The data analysis technique used was two-way Analysis of Variance (ANOVA). Based on the results of the data analysis conducted, it can be seen that there is no difference in students' mathematics learning achievement as measured by using the testlet model test when viewed from school status or in terms of gender. The analysis results also show that school status and gender are not related to improving students' mathematics learning achievement when measured using the testlet model test.
The Implementation of Electronic Medical Record (EMR) in The Development Health Care System in Indonesia

The development of information technology is now growing rapidly, including in the health sector. According to WHO, medical record is an important compilation of facts about a patient's life and health. The development of information technology in medical records is the electronic medical record (EMR). Developed countries, such as the United States and Korea have implemented EMR for a long time. In developing countries such as Indonesia, the development of EMR is still in progress because its implementation requires many factors to build a system or replace from manual medical records. Eventually, it is hoped that in the future all health care will use the EMR to resume patient datas from admission to discharge. The purpose of this study is to analyse the implementation and preparation of EMR in health care in Indonesia. This study is a literature review on the implementation and preparation of EMR in health care in Indonesia. The review is dome from 28 literature sources (Google-Scholar database). Total of 8 articles were obtained from 2017 to 2021. The results show that there are benefits after switching to EMR, even though some health care only used EMR in certain units. The highest benefit is reducing the cost of duplicating paper for printing. Also there is still limited human resources and tools for implementing EMR in Indonesia. The implementation of this EMR will enable the improvements of the service quality of the health care itself, especially in Indonesia.
Security from Phishing Attack on Internet using Evolving Fuzzy Neural Network

In recent years with the increase of cyber-attacks, data defense plays an essential part.The protecting of data has been the toughest obstacles now a days.Different countries and businesses take a wide range of steps to combat such cyberattacks.The rise of online technologies has resulted in unceasingly creative challenges to surveillance critical infrastructure.A few of these severe risks would be the use of phishing to deprive clients of web servers by using counterfeit email or URLs.Hence it is essential for employers to focus on application server sensitivity in the mitigation of phishing attacks.The intellectual ransom ware safety of internet study was based on mathematical methods, using fuse algorithms and a variety of resources that collect functions.The knowledgeable method to phishing protection was strengthened.The results demonstrate that phishing websites can be more reliably identified by the parameter estimation from consolidated databases.This would be a very difficult challenge to identify and delete the phishing pages, as the approaches usually involve different strategies and methodologies.This article explores how easily we use the neural network to deal with fake websites and to apply it by means of fuzzy logic techniques.
Scope and Sense of Explainability for AI-Systems

Certain aspects of the explainability of AI systems will be critically discussed. This especially with focus on the feasibility of the task of making every AI system explainable. Emphasis will be given to difficulties related to the explainability of highly complex and efficient AI systems which deliver decisions whose explanation defies classical logical schemes of cause and effect. AI systems have provably delivered unintelligible solutions which in retrospect were characterized as ingenious (for example move 37 of the game 2 of AlphaGo). It will be elaborated on arguments supporting the notion that if AI-solutions were to be discarded in advance because of their not being thoroughly comprehensible, a great deal of the potentiality of intelligent systems would be wasted.
Skeptical Reasoning with Preferred Semantics in Abstract Argumentation without Computing Preferred Extensions

We address the problem of deciding skeptical acceptance wrt. preferred semantics of an argument in abstract argumentation frameworks, i.e., the problem of deciding whether an argument is contained in all maximally admissible sets, a.k.a. preferred extensions. State-of-the-art algorithms solve this problem with iterative calls to an external SAT-solver to determine preferred extensions. We provide a new characterisation of skeptical acceptance wrt. preferred semantics that does not involve the notion of a preferred extension. We then develop a new algorithm that also relies on iterative calls to an external SAT-solver but avoids the costly part of maximising admissible sets. We present the results of an experimental evaluation that shows that this new approach significantly outperforms the state of the art. We also apply similar ideas to develop a new algorithm for computing the ideal extension.
AN IMPROVED PATTERN MINING TECHNIQUE FOR GRAPH PATTERN ANALYSIS USING A NOVEL BEHAVIOR OF ARTIFICIAL BEE COLONY ALGORITHM

Rising data complexity and volume in the network has attracted researchers towards substructure analysis. Subgraph mining is an area that has gained remarkable attention in the last couple of years to offer an intelligent analysis of more massive graphs and complicated data structures. It has been observed that graph pattern mining faces issues regarding the matching ruleset and complex instruction set execution problem. This paper introduces modern-day intelligence architecture based on Swarm Intelligence that is cross-validated by supervised Machine learning mechanisms. A new behavior incorporated with a new inter and intra hive behavior is incorporated in Swarm based Artificial Bee Colony. The proposed work model is evaluated over two different datasets with more than 4900 nodes in the graph. The proposed framework is evaluated using True Detection Rate, False Detection Rate, precision, and F-Measure, demonstrating an average improvement of 9.8%, 8.35%, 8.35% and 9.15% against existing GraMi work that represent an enhanced performance of the proposed pattern mining technique.
A Study on Surveillance Service for Vehicles based on Cryptography using Attributes

Massive amounts of data are sent and received in a vehicle traffic network, data involving countless vehicles, movements of people in the hundreds of millions, and their data, which get bundled with external environment data.The gathered data must at least be shared with legitimate users and depending on the situation data access privileges also need to be controlled.Because data between vehicles and the fog server may be subject to personal privacy infringement, a security technique must be introduced, such as encryption.To ensure secure sharing and use of data by users of a service, an encryption scheme is needed.Furthermore, delegation and revocation of decryption privileges are needed with this encryption scheme, concerning data encrypted based on user attributes.In this paper, we proposed a system that is safe from open recruitment attacks using an attribute-based authority delegation management method.The proposed system proved safe against publicly recruited attacks by malicious users using a semantic safety model.The proposed system can be applied to protect against smart car attacks.In the future, the encryption and calculation load will be reduced.
Automatic extraction of causal chains from text

Background.Automatic extraction of causal chains is valuable for discovering previously unknown and hidden connections between events.However, there is only a handful of works devoted to automatic extraction of causal chains from text.Objective.To develop a method for automatic extraction of causal chains from text.Method.A new approach based on linguistic templates is suggested for causal chain extraction.It is domain-independent, not restricted to extraction from single sentences and unfolded on big data.For implementation, a sequence of four modules was deployed.These are verb restriction, part-of-speech tagging, extracting causal relations, and unification and matching events.Results.14,821 causal chains (with length=2) have been extracted from 100,000 English Wikipedia articles.Contributions.The extracted causal chains can contribute to developing commonsense knowledge bases, reasoning resources, problem-solving, and generally in discovering previously unknown relationships between entities/events.
Fully Connected Entanglement-based Quantum Communication Network without Trusted Node

We proposed a fully connected quantum communication network architecture based on multi-user entanglement distribution by space multiplexing and wavelength multiplexing technologies. A fully connected QKD network with 40 users was demonstrated experimentally.
Kurdish Text Segmentation using Projection-Based Approaches

An optical character recognition (OCR) system may be the solution to data entry problems for saving the printed document as a soft copy of them. Therefore, OCR systems are being developed for all languages, and Kurdish is no exception. Kurdish is one of the languages that present special challenges to OCR. The main challenge of Kurdish is that it is mostly cursive. Therefore, a segmentation process must be able to specify the beginning and end of the characters. This step is important for character recognition. This paper presents an algorithm for Kurdish character segmentation. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. The algorithm works through the vertical projection of a word and then identifies the splitting areas of the word characters. Then, a post-processing stage is used to handle the over-segmentation problems that occur in the initial segmentation stage. The proposed method is tested using a data set consisting of images of texts that vary in font size, type, and style of more than 63,000 characters. The experiments show that the proposed algorithm can segment Kurdish words with an average accuracy of 98.6%.
Safe Weakly Supervised Learning

Weakly supervised learning (WSL) refers to learning from a large amount of weak supervision data. This includes i) incomplete supervision (e.g., semi-supervised learning); ii) inexact supervision (e.g., multi-instance learning) and iii) inaccurate supervision (e.g., label noise learning). Unlike supervised learning which typically achieves performance improvement with more labeled data, WSL may sometimes even degenerate performance with more weak supervision data. It is thus desired to study safe WSL, which could robustly improve performance with weak supervision data. In this article, we share our understanding of the problem from in-distribution data to out-of-distribution data, and discuss possible ways to alleviate it, from the aspects of worst-case analysis, ensemble-learning, and bi-level optimization. We also share some open problems, to inspire future researches.
Towards Efficient Exploration in Unknown Spaces: A Novel Hierarchical Approach Based on Intrinsic Rewards

Exploration in unknown environments using deep reinforcement learning (DRL) often suffers from sampling inefficiency due to notoriously sparse extrinsic rewards and complex spatial structures. To this end, we present a hierarchical and modular spatial exploration model that integrates the recently popular concept of intrinsic motivation (IM). The approach addresses the problem in two levels. On the higher level, a DRL based global module learns to determine a distant but easily reachable target that maximizes the current exploration progress, once such a target is needed by the local controller. On the lower level, a classical path planner is used to produce locally smooth movements between targets based on the known areas and free space assumption. This segmented and sequential decision-making paradigm, with an informative intrinsic reward signal, dramatically reduces training difficulty. Experimental results on diverse and challenging 2D maps show that the proposed model has consistently better exploration efficiency and generality than a state-of-the-art IM based DRL and some other heuristic methods.
Bounded Predicates in Description Logics with Counting

Description Logics (DLs) support so-called anonymous objects, which significantly contribute to the expressiveness of these KR languages, but also cause substantial computational challenges. This paper investigates reasoning about upper bounds on predicate sizes for ontologies written in the expressive DL ALCHOIQ extended with closed predicates. We describe a procedure based on integer programming that allows us to decide the existence of upper bounds on the cardinality of some predicate in the models of a given ontology in a data-independent way. Our results yield a promising supporting tool for constructing higher quality ontologies, and provide a new way to push the decidability frontiers. To wit, we define a new safety condition for Datalog-based queries over DL ontologies, while retaining decidability of query entailment.
Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic

In a recent paper Belle and Lakemeyer proposed the logic DS, a probabilistic extension of a modal variant of the situation calculus with a model of belief based on weighted possible worlds. Among other things, they were able to precisely capture the beliefs of a probabilistic knowledge base in terms of the concept of only-believing. While intuitively appealing, the logic has a number of shortcomings. Perhaps the most severe is the limited expressiveness in that degrees of belief are restricted to constant rational numbers, which makes it impossible to express arbitrary belief distributions. In this paper we will address this and other shortcomings by extending the language and modifying the semantics of belief and only-believing. Among other things, we will show that belief retains many but not all of the properties of DS. Moreover, it turns out that only-believing arbitrary sentences, including those mentioning belief, is uniquely satisfiable in our logic. For an interesting class of knowledge bases we also show how reasoning about beliefs and meta-beliefs after performing noisy actions and sensing can be reduced to reasoning about the initial beliefs of an agent using a form of regression.
Safety Analysis of Deep Neural Networks

Deep Neural Networks (DNNs) are popular machine learning models which have found successful application in many different domains across computer science. Nevertheless, providing formal guarantees on the behaviour of neural networks is hard and therefore their reliability in safety-critical domains is still a concern. Verification and repair emerged as promising solutions to address this issue. In the following, I will present some of my recent efforts in this area.
Perencanaan Strategis Sistem Informasi untuk Optimalisasi Layanan Sekolah Menggunakan Ward and Peppard

Abstract— The use of technology and information systems is an obligation that must be applied by every agency or organization to simplify and optimize operational implementation. Many agencies are actively using information systems so that the agency's performance can run well, systematically, and controlled, which will impact the credibility of their agency. A good information system can be created because of good planning. Strategic planning of information systems allows producing a good information system. This is because the output produced is in the form of strategic plans and documents that methodically describe the needs of information systems. Torsina Private Vocational School is a vocational school that seeks to implement information systems in every operation to improve schools' services. Therefore, strategic planning is needed in the development of IS/IT at Torsina Private Vocational Schools so that the implementation of information systems becomes more controlled and on target. IS/IT strategic planning in this study uses the Ward and Peppard model, while the analytical tools used are Critical Success Factors (CSF's), SWOT Analysis, Value Chain, and McFarlans Strategic Grid. The existence of an analytical tool is used to support the Ward and Peppard model to determine the internal and external environmental conditions of the organization. The results of this study are strategic planning of information systems at Torsina Private Vocational Schools in the form of business strategy, IT strategy, IS/IT management strategy, and an information system portfolio that is mapped based on the level of importance.
Deep Bucket Elimination

Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly. However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning for inference tasks, in this paper, we will use neural networks to approximate BE. The resulting Deep Bucket Elimination (DBE) algorithm is developed for computing the partition function. We provide a proof-of-concept empirically using instances from several different benchmarks, showing that DBE can be a more accurate approximation than current state-of-the-art approaches for approximating BE (e.g. the mini-bucket schemes), especially when problems are sufficiently hard.
Robust Affine Invariant Shape Descriptors

With the increasing number of available digital images, there is an urgent need of image content description to facilitate content based image retrieval (CBIR). Besides colour and texture, shape is an important low level feature in describing image content. An object can be photographed from different distances and angles. However, we often want to classify the images of the same object into one class, despite the change of perspective. So, it is desired to extract shape features that are invariant to the change of perspective. The shape of an object from one viewpoint to another can be linked through an affine transformation, if it is viewed from a much larger distance than its size along the line of sight. Those invariant shape features are known as affine invariant shape representations. Because of the change of perspective, it is more difficult to develop affine invariant shape representations than normal ones. The goal of this work is to develop affine invariant shape descriptors. Through shape retrieval experiments, we find that the performance of the existing affine invariant shape representations are not satisfactory. Especially, when the shape boundary is corrupted by noise, their performance degrades quickly. In this work, two new affine invariant contour-based shape descriptors, the ICA Fourier shape descriptor (ICAFSD) and the whitening Fourier shape descriptor (WFSD) have been developed. They perform better than most of the existing affine invariant shape representations, while having compact feature size and low computational time requirement. Four region-based affine-invariant shape descriptors, the ICA Zernike moment shape descriptor (ICAZMSD), the whitening Zernike moment shape descriptor (WZMSD), the ICA orthogonal Fourier Mellin moment shape descriptor (ICAOFMMSD), and the whitening orthogonal Fourier Mellin moment shape descriptor (WOFMMSD), are also proposed, in this work. They can be applied to both simple and complex shapes, and have close to perfect performance in retrieval experiments. The advantage of those newly proposed shape descriptors is even more apparent in experiments on shapes with added boundary noise: Their performance does not deteriorate as much as the existing ones.
Education, work and creation: ways for developing technological education of schoolchildren in Russia

The authors touch upon the problem of the development of additional technological education in Russian schools by comparing it with the situation in Western countries. Aim: strong requirements for inventions of schoolchildren should be established, a clear sequence of their teaching in invention and creation should be developed. Methods: methodology for teaching schoolchildren to invent and create something that has not existed before in different age groups is presented; a patent for a utility model developed jointly with schoolchildren participating in the system of additional technological education in Lipetsk seem to be important results of this study. Results: the authors considered ways to improve the effectiveness of teaching schoolchildren to invent in the field of additional technological education, taking into account current problems and trends. The systematic training of schoolchildren from elementary grade to middle and senior grades normally gives a positive result during the formation of divergent thinking, which translates into the child's ability to generate a variety of ideas for improving technical objects and creation as such. Attracting the capabilities of enterprises to organize an authentic educational process is relevant, too, as long as the project activities are designed to form the skills of independent work, creative and research activities.
Sistem Informasi Bimbingan Skripsi Menggunakan Metode Rapid Application Development Berbasis User Centered Design

Thesis is the result of a scientific research paper written and adapted to the scientific rules that apply in writing and the process of working on a thesis, usually students are guided by two supervisors. Because during the pandemic and the difficulty of consulting with supervisors and the establishment of health protocols to avoid the impact of the spread of the COVID-19 virus, the author intends to build a Thesis Guidance Information System application that can be accessed online and based on a website and facilitates the thesis implementation process. Features contained in the system include students who can submit ideas and thesis titles and can see notifications of ideas and thesis titles being rejected or not. Students who carry out theses can be ensured that they are supervised by their supervisors, therefore supervisors can guide their students using the application and can carry out online sessions and seminars. The thesis guidance information system is designed using the Model View Controller architecture which separates the user interface, data and control. This object-oriented system development model uses the Unified Modeling Language model which aims to analyze and model applications designed using Model View Controller-based architecture. The software development method used in the development of this system is Rapid Application Development because it has relatively simple features. The User Centered Design approach is that users in every stage of system development are also involved, so that the system formed can be used according to system needs.