Reviewerly/exHarmony huggingface dataset:
Autoethnography of an Elementary School Teacher’s Experiences with Teaching Children with the Autism Spectrum Disorder

This study aims to investigate the content and meanings of a homeroom teacher’s experiences with teaching students in an inclusive class in elementary school based on the autoethnography of the investigator’s experiences of teaching children with the autism spectrum disorder for many years. Based on the analysis results of collected data, the content of experiences was mainly divided into lessons, life guidance, and relations with people around. In the aspect of lessons, there were the lack of professionalism in special education and the confusion over the essence of inclusive education. In the aspect of life, the teacher had experiences of helping children with the autism spectrum disorder adjust to the class by applying a repeated and focused behavior formation method based on the assistance of various people around her. In the aspect of relations with people around, the teacher had subtle tension and disagreement even amid cooperation with people around her under a single goal. These experiences held three meanings: the direction of inclusive education for children with the autism spectrum disorder was not clear in elementary school; there was no effective communication and support between the different systems providing inclusive education; and there were possibilities of inclusive education for children with the autism spectrum disorder. Based on these findings and their implications, the study made proposals for future studies.
With Special Reference To Opac, A Pinpointing Study Of Koha - Integrated Library Management Software

This paper focuses on a user-centered empirical examination of the Koha OPAC's usability. The library management requirements of every modern library can be satisfied by Koha automation software. Koha was chosen because it is user-friendly and open source software for this reason. Earlier, the NorthCap University (NCU) library used Libsys-10 software, but now it has successfully implemented Koha OSS for the NCU library. With a focus on OPAC, this paper will assist library professionals in effectively implementing and installing Koha in their libraries. Within a fairly short period of time, the author implemented and installed Koha software at the NorthCap University library under his supervision. As a result, the article analyzed the necessity and significance of library automation as well as the rationale behind choosing Koha library management
Mining Symbolic Rules to Explain Lung Cancer Treatments

Knowledge Graphs (KGs) represent the convergence of data and knowledge as factual statements; they allow for the enrichment of decision-making semantically. Symbolic inductive learning enables uncovering relevant patterns, expressed, for example, as Horn clauses. Albeit powerful, existing symbolic inductive learning frameworks may mine many rules, being difficult for a user to extract actionable insights. This demo illustrates a pipeline to analyze mined logical rules toward discovering meaningful insights. The demo puts into perspective the role of semantic types in guiding the exploration of mined rules. Participants will observe strategies to traverse the mined logical statements and how the outcomes reveal patterns in the prescription of lung cancer treatments. A video is available online ( https://www.youtube.com/watch?v= CN4a3kUjfJ4 &ab_channel=TIBSDMGroup ), a Jupyter notebook executes a live demos ( https://mybinder.org/v2/gh/SDM-TIB/DIGGER-ESWC2023Demo/HEAD?labpath=Mining%20Symbolic%20Rules%20To%20Explain%20Lung%20Cancer%20Treatments.ipynb ), and source-code is available in GitHub ( https://github.com/SDM-TIB/Mini ng_Symbolic_Rules_ESWC2023Demo ).
A User Interface Model for Digital Humanities Research: Case BookSampo – Finnish Fiction Literature on the Semantic Web

This paper presents the implementation of a new user interface (UI) for the Finnish fiction literature BookSampo knowledge graph. The UI utilizes possibilities of semantic web technologies to provide the end-user an enhanced search and browsing experience through faceted search and data-analytical tools for Digital Humanities research.
SAP-KG: Synonym Predicate Analyzer Across Multiple Knowledge Graphs

This demo paper presents SAP-KG, a knowledge graph agnostic tool, to illustrate the benefits of identifying the synonym predicates that provide complementary information; they are used for query rewriting to enhance query answer completeness. SAP-KG proposed a metric to compute the percentage of overlap between pairs of synonym predicate candidates and capture the most similar ones which can complement each other. We present a query processing technique that put in perspective the role of synonym predicates in query answer completeness. The demo code is available online in ( https://github.com/SDM-TIB/SAP-KG-ESWC2023Demo ) and can be run at ( https://mybinder.org/v2/gh/SDM-TIB/SAP-KG-ESWC2023Demo/main?labpath=SAP-KG.ipynb ).
DDAug: Differentiable Data Augmentation for Weakly Supervised Semantic Segmentation

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Weakly supervised semantic segmentation</i> (WSSS) with image-level labels has witnessed promising advances with the help of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">class activation maps</i> (CAM). However, CAM is always confined to small discriminative seed regions due to its simple classification loss guided training manner. To handle this problem, recent works introduced specifically designed regularizations and modules to expand the CAM seed regions, serving as the final segmentation masks. In this paper, we surprisingly find that the classification loss could suppress the gains from these regularization and modules in the late training phase, thereby limiting the further growth of CAM, which we call as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit supervision disturb</i> (ESD) issue. Interestingly, we find that specific <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data augmentation</i> (DA) operations (e.g., CutMix) can relieve such ESD issue, and the benefits introduced by different DA operations vary a lot. To maximize the benefits, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differentiable data augmentation</i> (DDAug) to automatically search for the proper DA policy. Specifically, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-level search space</i> to sequentially sample DA operations with different properties. Extensive experiments demonstrate that the proposed DDAug can alleviate the ESD issue and introduce consistent improvements to various popular WSSS methods, achieving the state-of-the-art performance on the MS COCO 2014 and PASCAL VOC 2012 datasets.
SurfaceNet: Fault-Tolerant Quantum Networks with Surface Codes

Quantum networks serve as the means to transmit information, encoded in quantum bits or qubits, between quantum processors that are physically separated. Given the instability of qubits, the design of such networks is challenging, necessitating a careful balance between reliability and efficiency. Typically, quantum networks fall into two categories: those utilize quantum entanglements for quantum teleportation, and those directly transfer quantum message. In this paper, we present SurfaceNet, a quantum network in the second category that employs surface codes as logical qubits for preserving and transferring message. Our approach of using surface codes can fault-tolerantly correct both operational and photon loss errors within the network. We propose a novel one-way quantum communication procedure, designed to better integrate surface codes into our network architecture. We also propose an efficient routing protocol that optimizes resource utilization for our communication procedure. Simulation results demonstrate that SurfaceNet significantly enhances the overall communication fidelity.
A Study of the English Version The Story of the Stone from the Perspective of Memetics

Meme; Memetics; The story of the Stone; Translation principles
Pembelajaran penjasorkes Pada Masa Pandemi Covid-19 Di SMA Negeri 1 Pecangaan Kabupaten Jepara Tahun 2021

&#x0D; &#x0D; &#x0D; &#x0D; This study aims to describe and review the planning, implementation, evaluation of learning of fishermen during the covid-19 pandemic. The methods used in this study are qualitative with a descriptive approach. Data is obtained through interviews, observations. and documentation conducted at SMA Negeri 1 Pecangaan Jepara Regency, then analyzed through data reduction, descriptive presentation of data and drawing conclusions. The results of this study are known that in SMA Negeri 1 Pecangaan in learning planning during the covid-19 pandemic still uses syllabus and rpp offline, but prepares teaching materials, media, sources, learning methods, and assessments that have been adjusted to the conditions of the covid-19 pandemic. The implementation of learning is carried out online in the form of asynchronous through google classroom, whatsapp, LMS according to the PJJ schedule. Evaluation of learning during the covid-19 pandemic only evaluates learning outcomes using two aspects only, namely affective and cognitive. Assessment of the affective aspect is obtained by looking at the discipline of students in accessing learning and collecting assignments. Assessment of the cognitive aspect is usually done by giving assignments at the end of learning and PTS / PAS.&#x0D; &#x0D; &#x0D; &#x0D;
Quantum Resourrection: Quantum Algorithm with Complex Conjugation Reverses Phases of the Wave Function Components

Abstract Light is a powerful tool to manipulate matter, but existing approaches often necessitate focused, highintensity light that limits the manipulated object’s shape, material and size. The breakthrough development could lead to spacecraft without fuel. What if a spacecraft could travel through our solar system powered and accelerated using only light? That’s the goal of new research coming out of Caltech. More federal spending on directed energy weapon research & development has some stakeholders looking for operational systems to deploy in the next two or three years. The weapons which use focused energy in the forms of lasers, microwaves and other methods against targets ranging from drone swarms to ballistic missiles — have long drawn the interest of the Department of Defense and its military services but have previously been relegated largely to the arena of the theoretical. They have artificially created a state that evolves in a direction opposite that of the thermodynamic arrow of time. Scientists have reversed the direction of time with a quantum computer. Keywords: Quantum Resourrection, Quantum Algorithm, Complex Conjugation, Quantum State, photonic levitation, propulsion of nano structured macroscopic objects, Quantum Entanglement Entropy, Artificial Photosynthesis, Directed Energy Weapons.
Design of Android-Based Augmented Reality Physics Book for Grade XII of High School Chapter 9 Digital Technology

Many senior high school students still rely on printed books for their learning, but these books often only provide 2D images with written explanations. This made Students difficult to fully ubnderstand the 3D nature of the images, especially in subjects like physic. To address this issue, the author developed an application using Augmented Reality (AR) Technology Which focused on Chapter IX of the physics textbook, mainly on "Digital Technology". This AR application students with more interesting and interactive learning experiences by showcasing animation and 3D designs. Based on he questionnaire result, from 120 students of SMK Negeri 1 Langsa it was found that 96.17% agreed that this AR app was an effective learning tool and 3.83% disagreed. So it can be concluded that this AR application can be an alternative for students and teachers as a learning media and it easier to understand, interesting, and fun in the teaching and learning process.
Analisis Pembelajaran Daring Mata Kuliah Praktek dengan Uji T Berpasangan dan Matriks Ipa

Online learning is an alternative to classroom learning, especially during the pandemic that occurred in early 2020. With physical distancing that allow for direct interaction, to stop the transmission of COVID-19. Online learning is used in UIN Walisongo Semarang. It is undoubtedly not difficult to adapt to some study programs that are dominated by theoretical or material courses. On the other hand, changes are required for study programs that include practical courses, the following in the Study Program of Ilmu Seni dan Arsitektur Islam (ISAI). According to the background, a paired t-test will be used to compare the performance and importance for implementing online learning, as well as ISAI students' impressions of online learning using the IPA (Importance Performance Analysis) matrix. 31 students were chosen at random for the sample. Using five service dimensions, perceptions of student satisfaction are assessed (Empathy, Assurance, Responsiveness, Reliability, Tangibles). The results showed that there was a difference between the importance and performance of students on the quality of online learning. The outcomes of the paired t test with a sig value of 0,000&lt;0,05 and also t_hitung=11,396&gt;t_(0,05;17)=2,109. There are several indicators at Quadrant I of IPA matrix that must receive attention because the student satisfaction is still low, including: attention from lecturers to students (item P1); lecturers' responses to student difficulties (item P9); and reliability of lecturers in online learning (item P13). Therefore, the perception of the quality of students' online learning can be improved by improving the attention, responsiveness, and reliability of lecturers.
Convolutional Models with Multi-Feature Fusion for Effective Link Prediction in Knowledge Graph Embedding

Link prediction remains paramount in knowledge graph embedding (KGE), aiming to discern obscured or non-manifest relationships within a given knowledge graph (KG). Despite the critical nature of this endeavor, contemporary methodologies grapple with notable constraints, predominantly in terms of computational overhead and the intricacy of encapsulating multifaceted relationships. This paper introduces a sophisticated approach that amalgamates convolutional operators with pertinent graph structural information. By meticulously integrating information pertinent to entities and their immediate relational neighbors, we enhance the performance of the convolutional model, culminating in an averaged embedding ensuing from the convolution across entities and their proximal nodes. Significantly, our methodology presents a distinctive avenue, facilitating the inclusion of edge-specific data into the convolutional model’s input, thus endowing users with the latitude to calibrate the model’s architecture and parameters congruent with their specific dataset. Empirical evaluations underscore the ascendancy of our proposition over extant convolution-based link prediction benchmarks, particularly evident across the FB15k, WN18, and YAGO3-10 datasets. The primary objective of this research lies in forging KGE link prediction methodologies imbued with heightened efficiency and adeptness, thereby addressing salient challenges inherent to real-world applications.
The Application of Artificial Intelligence in the Integrated English Course Teaching Mode

This article aims to explore the teaching mode of the integrated English course based on artificial intelligence. First, thearticle introduces the application of artificial intelligence in the field of education and explains why artificial intelligencecan bring innovation to English language teaching. Then, the article explores the advantages of AI-based integratedEnglish curriculum teaching mode. Finally, it shows how AI can be used to improve students’ listening, speaking,reading, writing, and translating skills and promote students’ participation and personalized learning. This article isintended to provide a valuable reference for English education advocates and educational technology practitioners.
Methods to Reduce the Dimension of Multivariate Models

High computational requirement is one of the limitations of multivariate analysis especially with large data sets. The number of effects in multivariate analyses tend to increase linearly with the number of traits considered. Therefore, methodologies to model higher-dimensional data more parsimoniously are of considerable interest both for parameter estimation and genetic evaluation. In this chapter, methods such as canonical transformation and Cholesky decomposition, which reduce the dimensionality of multivariate analysis through transformation of the vector of observations in addition to the residual and genetic covariance matrices are initially discussed. Subsequently, the application of factorial and principal component analyses to reduce the dimension of multivariate analysis with a large number of traits with high genetic correlations among the traits is examined. Neither of these two methods involve the transformation of the vector of observations but are efficient approaches in reducing the rank of the genetic covariance matrix in multivariate analysis.
Preferential treatment moderating role on parental social economic status impact on students achievement in computer studies

Parental Social Economic Status will continue to change from time to time and there has been inconsistency on its impact on students’ academic performance. This study investigated the moderating effect of preferential treatment on the impact of Parental Social Economic Status on students’ academic performance in Ogun state. The study used descriptive survey research design with 392 students as sample, selected through multi stage sampling approach. Parental Social economic Status Scale (PSESS, 0.77), Preferential Treatment Scale (PTS0.82) and Students Academic Perfroamnce Proformat were used for data collection. Percentage and regression were used for data analysis. Finding of the study showed that PSES and preferential treatment has significant impact on students academic performance ((F(1,390) = 173.734 P 0.05, F(1,390) = 7.250 P 0.05). The combination of PSES and preferential treatment also produced significant outcome ((F(1,389) = 87.956 P 0.05). The moderating effect of preferential treatment on the impact of PSES on students’ academic performance was also significant (t = 12.869, p 0.05). It is concluded that there is significant moderating effect of preferential treatment on impact of PSES on students’ academic performance in senior secondary schools in Ogun State
FSDA: Tackling Tail-Event Analysis in Imbalanced Time Series Data (ECML PKDD 2023 - LIDTA Slides)

Financial time series data is often imbalanced, with the majority of data points representing normal conditions and a small minority representing tail events, such as market crashes. This imbalance can make it difficult to train machine learning models to accurately predict tail events.FSDA (Feature Selection and Data Augmentation) is a novel approach to tackling tail-event analysis in imbalanced time series data. FSDA first uses a feature selection algorithm to identify the most predictive features for tail data. These features may exhibit different sensitivities compared to the rest of the dataset, so it is important to select them carefully.Once the most predictive features have been identified, FSDA uses data augmentation to generate synthetic tail data. Data augmentation is a conventional technique for handling imbalanced data, and it can be used to enhance the accuracy of machine learning regression methods.FSDA has been evaluated on a financial time series dataset, and it has been shown to consistently outperform feature selection (FS) and data augmentation (DA) methods across all percentiles ranging from 85 to 99. This demonstrates the efficacy of FSDA in managing imbalanced time series data and improving predictive accuracy.
Fuzzy Logic and Neuro-Fuzzy Control: DC Motor Position Control

In this chapter, a brief explanation is given about fuzzy logic and neuro-fuzzy systems. DC motor position control is realized to explain usage of neuro-fuzzy controller. DC motor model is given in the form of mathematical equations and transfer function. First, a tuned by trial-error method PID controller is applied to DC motor system and responses are collected as a dataset to train neural networks of neuro-fuzzy system. Trained neuro-fuzzy system is used to design an adaptive neuro-fuzzy controller to control position of a DC Motor. Matlab Neuro-Fuzzy Designer Toolbox and Matlab/Simulink is used to design neuro-fuzzy and simulate the control system. As a results, neuro-fuzzy controller achieved successfully in the position control of DC motor. The results also shows that neuro-fuzzy controller can support energy consumption.
The Research on the Production and Sale of Online Game Hacks Behavior Conviction

The endless emergence of online game hacks affects the development environment of the entire online games, damaging the interests of the developers and operators while also hitting the enthusiasm of social innovation. This paper will adopt empirical research method and comparative research method based on the data of previous court judgements and combined with the current relevant legal regulations. The objective of this research is to expose the problems in judicial practice by analysing the existing case judgments on the criminal law system of making and selling online game hacks, distinguishing different crimes according to the legal benefits, and providing corresponding suggestions. The results of the study reveal that there are different definitions of online game plug-ins, mismatches between legal and technical knowledge, unclear thresholds of offence, and confusing identification of crimes in the current judicial practice. To sum up, firstly, we can learn from the way of dealing with this problem in other countries. Secondly, the offence should be identified more accurately by making an accurate distinction between specific legal interests in practice. Finally, the above problems can be solved by analysing the nature of infringement from a technical perspective and combining it with the law in depth.
Cognitive Systems Engineering at 40, Part I: Deriving Theory from Practice

This expert panel is the first of a two-panel series marking the 40 th anniversary of “Cognitive Systems Engineering: New Wine in New Bottles” by Hollnagel and Woods (1983) and, arguably, the beginning of Cognitive Systems Engineering (CSE). These experts were there at (or near) the beginning, devising new methods, expanding and creating new theories, and revealing a new perspective on how complex systems sustain performance and fail. They also wrestled and struggled with these new ideas to propose and implement solutions to improve performance in a number of high-consequence industries. Whether in graduate school or as early-career professionals, they saw the surprises that served as signals that the thinking that brought us to that point would not, alone, be the thinking and doing that would take us further. They will each answer the question, “What ideas and perspectives are important about Cognitive Systems Engineering, and why?”