Reviewerly/exHarmony huggingface dataset:
Penerapan Metode TOPSIS Dalam Penentuan Wali Kelas

The homeroom teacher has a function as a teacher who is responsible as a guardian or substitute for the parents of students while in the school area and also helps students in the academic field. As a teacher or homeroom teacher is one of the determining factors for the success of any educational effort. The teacher's ability to plan and implement learning is a major factor in achieving teaching objectives. Mastery skills in the learning process are closely related. Every year students who will enter higher education or junior high school (SMP), need to be guided and prepared carefully, so that these students can enter junior high school (SMP) according to their wishes and expectations. Therefore, it is necessary to prepare a grade 6 homeroom teacher who can guide and prepare students as well as possible during preparation for entering junior high school (SMP). So far, the determination of the 6th grade guardian is still done subjectively, the subjective referred to is an assessment based on proximity to the principal, in this case the principal also does not have definite criteria in determining the 6th homeroom teacher. To overcome the problems that occur, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method will be applied as a tool in determining homeroom 6th graders in elementary schools. This method will determine the assessment criteria with the following criteria: Pedagogics (Knowing the characteristics of students, mastering learning theory and learning principles, assessing and evaluating learning), attendance and tenure. From the results of the application of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, it is to determine the 6th grade homeroom teacher in elementary school with 3 names in the top positions who will become 6th grade homeroom teachers in elementary schools. With the percentage of eligibility for information systems experts of 95% and the percentage of user eligibility of 94%.
Penerapan Algoritma N-GRAM Dan WINNOWING Untuk Deteksi Plagiarisme Usulan Dokumen Perguruan Tinggi

Plagiarism which is a disgraceful act can occur in any series of documents, including one of them is a feasibility study book document as is often made by universities, foundations of organizing bodies, to consultants related to the interests of establishing universities as well as proposals for opening new study programs submitted to the Ministry of Education and Culture and Higher Education (Kemendikbud Dikti). Consultants, especially those with legal entities, will determine the smoothness of proposals to rejection of proposals due to flaws in the text, the most common of which is plagiarism, which requires consultants to remain skilled in ensuring the authentication of documents made to clients as well as to the institution or institution that is the evaluator. This plagiarism can be prevented by building a plagiarism detection system with a programming algorithm approach such as; The second N-Gram Algorithm and Winnowing Algorithm were successfully built by researchers using the Research and Development (R&D) method to detect plagiarism in the proposed college documents, in this case training documents and test documents in the form of systems and applications. The application is made web-based by utilizing an interface to make it easier for leaders to ensure the similarity and authentication of documents made by consultants. The n-gram algorithm is a document processing method that is usually used in spelling correction, word prediction and other processing and Winnowing Algorithm is one of the algorithms in the document fingerprinting method. Both are used to detect Plagiarism of Proposed College Documents with the Feasibility Study Book document object resulting in a level of accuracy and effectiveness with the parameters of similarity between documents as stated in the percentage of similarity (similarity percentage) which can be concluded the value of plagiarism between documents.
Cross-Domain Anomaly Detection using Unsupervised Representation Learning and Domain Adaption

Aiming at the urgent demands of industrial fault detection, cross-domain detection is a promising strategy for overcoming the obstacle of the premise of data identical-distribution. This paper proposes a cross-domain anomaly detection method based on unsupervised representation learning and domain adaptation. In order to learn effective features from the original signals, the multidimensional scale loss and an improved instance-based discriminative loss are combined. The first one is for retaining structural information of the data and the second one is for obtaining domain-invariant characteristic. The proposed method is validated in two detection cases including manipulator and bearing. Detection results show that the proposed method has superior performance than several widely used detection methods.
Random Transpositions on Contingency Tables

Contingency tables are useful objects in statistics for representing 2-way data. With fixed row and column sums, and a total of $n$ entries, contingency tables correspond to parabolic double cosets of $S_n$. The uniform distribution on $S_n$ induces the Fisher-Yates distribution, classical for its use in the chi-squared test for independence. A Markov chain on $S_n$ can then induce a random process on the space of contingency tables through the double cosets correspondence. The random transpositions Markov chain on $S_n$ induces a natural `swap' Markov chain on the space of contingency tables; the stationary distribution of the Markov chain is the Fisher-Yates distribution. This paper describes this Markov chain and shows that the eigenfunctions are orthogonal polynomials of the Fisher-Yates distribution. Results for the mixing time are discussed, as well as connections with sampling from the uniform distribution on contingency tables, and data analysis.
Super-resolution 3D Human Shape from a Single Low-Resolution Image

We propose a novel framework to reconstruct super-resolution human shape from a single low-resolution input image. The approach overcomes limitations of existing approaches that reconstruct 3D human shape from a single image, which require high-resolution images together with auxiliary data such as surface normal or a parametric model to reconstruct high-detail shape. The proposed framework represents the reconstructed shape with a high-detail implicit function. Analogous to the objective of 2D image super-resolution, the approach learns the mapping from a low-resolution shape to its high-resolution counterpart and it is applied to reconstruct 3D shape detail from low-resolution images. The approach is trained end-to-end employing a novel loss function which estimates the information lost between a low and high-resolution representation of the same 3D surface shape. Evaluation for single image reconstruction of clothed people demonstrates that our method achieves high-detail surface reconstruction from low-resolution images without auxiliary data. Extensive experiments show that the proposed approach can estimate super-resolution human geometries with a significantly higher level of detail than that obtained with previous approaches when applied to low-resolution images.
Survival Mixture Density Networks

Survival analysis, the art of time-to-event modeling, plays an important role in clinical treatment decisions. Recently, continuous time models built from neural ODEs have been proposed for survival analysis. However, the training of neural ODEs is slow due to the high computational complexity of neural ODE solvers. Here, we propose an efficient alternative for flexible continuous time models, called Survival Mixture Density Networks (Survival MDNs). Survival MDN applies an invertible positive function to the output of Mixture Density Networks (MDNs). While MDNs produce flexible real-valued distributions, the invertible positive function maps the model into the time-domain while preserving a tractable density. Using four datasets, we show that Survival MDN performs better than, or similarly to continuous and discrete time baselines on concordance, integrated Brier score and integrated binomial log-likelihood. Meanwhile, Survival MDNs are also faster than ODE-based models and circumvent binning issues in discrete models.
Few-Shot Table-to-Text Generation with Prefix-Controlled Generator

Neural table-to-text generation approaches are data-hungry, limiting their adaptation for low-resource real-world applications. Previous works mostly resort to Pre-trained Language Models (PLMs) to generate fluent summaries of a table. However, they often contain hallucinated contents due to the uncontrolled nature of PLMs. Moreover, the topological differences between tables and sequences are rarely studied. Last but not least, fine-tuning on PLMs with a handful of instances may lead to over-fitting and catastrophic forgetting. To alleviate these problems, we propose a prompt-based approach, Prefix-Controlled Generator (i.e., PCG), for few-shot table-to-text generation. We prepend a task-specific prefix for a PLM to make the table structure better fit the pre-trained input. In addition, we generate an input-specific prefix to control the factual contents and word order of the generated text. Both automatic and human evaluations on different domains (humans, books and songs) of the Wikibio dataset show substantial improvements over baseline approaches.
Ultra-high-resolution unpaired stain transformation via Kernelized Instance Normalization

While hematoxylin and eosin (H&E) is a standard staining procedure, immunohistochemistry (IHC) staining further serves as a diagnostic and prognostic method. However, acquiring special staining results requires substantial costs. Hence, we proposed a strategy for ultra-high-resolution unpaired image-to-image translation: Kernelized Instance Normalization (KIN), which preserves local information and successfully achieves seamless stain transformation with constant GPU memory usage. Given a patch, corresponding position, and a kernel, KIN computes local statistics using convolution operation. In addition, KIN can be easily plugged into most currently developed frameworks without re-training. We demonstrate that KIN achieves state-of-the-art stain transformation by replacing instance normalization (IN) layers with KIN layers in three popular frameworks and testing on two histopathological datasets. Furthermore, we manifest the generalizability of KIN with high-resolution natural images. Finally, human evaluation and several objective metrics are used to compare the performance of different approaches. Overall, this is the first successful study for the ultra-high-resolution unpaired image-to-image translation with constant space complexity. Code is available at: https://github.com/Kaminyou/URUST
How good are deep models in understanding the generated images?

My goal in this paper is twofold: to study how well deep models can understand the images generated by DALL-E 2 and Midjourney, and to quantitatively evaluate these generative models. Two sets of generated images are collected for object recognition and visual question answering (VQA) tasks. On object recognition, the best model, out of 10 state-of-the-art object recognition models, achieves about 60\% and 80\% top-1 and top-5 accuracy, respectively. These numbers are much lower than the best accuracy on the ImageNet dataset (91\% and 99\%). On VQA, the OFA model scores 77.3\% on answering 241 binary questions across 50 images. This model scores 94.7\% on the binary VQA-v2 dataset. Humans are able to recognize the generated images and answer questions on them easily. We conclude that a) deep models struggle to understand the generated content, and may do better after fine-tuning, and b) there is a large distribution shift between the generated images and the real photographs. The distribution shift appears to be category-dependent. Data is available at: https://drive.google.com/file/d/1n2nCiaXtYJRRF2R73-LNE3zggeU_HeH0/view?usp=sharing.
Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation

Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning methods require large amounts of labeled data for training. Manual annotation is expensive, while simulators can provide accurate annotations. However, the performance of the semantic segmentation model trained with the data of the simulator will significantly decrease when applied in the actual scene. Unsupervised domain adaptation (UDA) for semantic segmentation has recently gained increasing research attention, aiming to reduce the domain gap and improve the performance on the target domain. In this paper, we propose a novel two-stage entropy-based UDA method for semantic segmentation. In stage one, we design a threshold-adaptative unsupervised focal loss to regularize the prediction in the target domain, which has a mild gradient neutralization mechanism and mitigates the problem that hard samples are barely optimized in entropy-based methods. In stage two, we introduce a data augmentation method named cross-domain image mixing (CIM) to bridge the semantic knowledge from two domains. Our method achieves state-of-the-art 58.4% and 59.6% mIoUs on SYNTHIA-to-Cityscapes and GTA5-to-Cityscapes using DeepLabV2 and competitive performance using the lightweight BiSeNet.
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation

Dynamic contextualised word embeddings (DCWEs) represent the temporal semantic variations of words. We propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model (MLM) using time-sensitive templates. Given two snapshots $C_1$ and $C_2$ of a corpus taken respectively at two distinct timestamps $T_1$ and $T_2$, we first propose an unsupervised method to select (a) \emph{pivot} terms related to both $C_1$ and $C_2$, and (b) \emph{anchor} terms that are associated with a specific pivot term in each individual snapshot. We then generate prompts by filling manually compiled templates using the extracted pivot and anchor terms. Moreover, we propose an automatic method to learn time-sensitive templates from $C_1$ and $C_2$, without requiring any human supervision. Next, we use the generated prompts to adapt a pretrained MLM to $T_2$ by fine-tuning using those prompts. Multiple experiments show that our proposed method reduces the perplexity of test sentences in $C_2$, outperforming the current state-of-the-art.
Unaware Unethical Behavior and ‘Learning from Error’ through Knowledge Sharing

&#x0D; The paper focus on fraudulent or unethical behaviors in which there is no awareness, reasoning, or intention, but an incorrect action that results from lack of knowledge in terms of action errors. Using error management framework, we focus on the potential positive effects of errors, to link the errors with the more general goal of learning. Literature has shown that errors are often related to communication failures and of sharing of information. To enrich our understanding about fraudulent or unethical behaviors, this article contributes to the extant literature by investigating how knowledge sharing behaviors impact on the process of learning from error within the firm, to preventing future fraudulent or unethical behavior. The study reports a case study for examining the characteristics of the knowledge sharing behaviors, and how these behaviors characteristics impact the process of learning from error within the organization, to preventing future fraudulent or unethical behavior&#x0D;
Developing User Personas as a way of Managing Knowledge About Ecolabnet Project Participants

One of the vital elements that decide about a success, or a failure of a project is adjusting its offer to the requirements of the beneficiaries. Various methods and tools are used in this respect to make the project’s offer consistent with these requirements and guarantee that they are satisfied. One of the methods used in this area is application of user personas. The idea is extensively used in the field of computer-human interactions, as it allows to improve user experience as well as the whole communication in the process of developing IT systems. However, the use of personas is not limited only to designing computer-based systems and solutions. The concept of personas has been also exploited in the Ecolabnet project, financed by Interreg, and implemented in six partner countries in the years 2019-2021. The process of knowledge management in the project assumed gathering the knowledge on its future beneficiaries - manufacturing SMEs from the Baltic Sea Region – and adjusting the offer of the project consortium (RDIs and Intermediary Organisations) to the signalled needs of these enterprises. To facilitate the management of knowledge on the enterprises investigated in the project the concept of persons was used. The underlying objective of the paper is to present the process of transforming the knowledge on project participants into the concept of personas. It consists of two parts. The first part, the theoretical one includes the results of a literature review that provides essential knowledge on utilising the concept of personas in design and management processes. The second part, the empirical one, is based on the empirical research that was conducted at the initial stage of the project. The answers provided in the survey questionnaire allowed for identification of primary needs and barriers in the area of eco-innovations development. This knowledge has led to determining main personas of the project. The analysis of the process of mapping the project users’ needs and objective is supposed to answer the following research question: Does the application of personas concept in service design positively contributes to management of knowledge in international projects?
Solving Royal Game of Ur Using Reinforcement Learning

Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received. While RL has outperformed existing state-of-the-art methods used for playing simple video games and popular board games, it is yet to demonstrate its capability on ancient games. Here, we solve one such problem, where we train our agents using different methods namely Monte Carlo, Qlearning and Expected Sarsa to learn optimal policy to play the strategic Royal Game of Ur. The state space for our game is complex and large, but our agents show promising results at playing the game and learning important strategic moves. Although it is hard to conclude that when trained with limited resources which algorithm performs better overall, but Expected Sarsa shows promising results when it comes to fastest learning.
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis

Graph neural networks (GNNs) are highly effective on a variety of graph-related tasks; however, they lack interpretability and transparency. Current explainability approaches are typically local and treat GNNs as black-boxes. They do not look inside the model, inhibiting human trust in the model and explanations. Motivated by the ability of neurons to detect high-level semantic concepts in vision models, we perform a novel analysis on the behaviour of individual GNN neurons to answer questions about GNN interpretability, and propose new metrics for evaluating the interpretability of GNN neurons. We propose a novel approach for producing global explanations for GNNs using neuron-level concepts to enable practitioners to have a high-level view of the model. Specifically, (i) to the best of our knowledge, this is the first work which shows that GNN neurons act as concept detectors and have strong alignment with concepts formulated as logical compositions of node degree and neighbourhood properties; (ii) we quantitatively assess the importance of detected concepts, and identify a trade-off between training duration and neuron-level interpretability; (iii) we demonstrate that our global explainability approach has advantages over the current state-of-the-art -- we can disentangle the explanation into individual interpretable concepts backed by logical descriptions, which reduces potential for bias and improves user-friendliness.
Effect in the spectra of eigenvalues and dynamics of RNNs trained with Excitatory-Inhibitory constraint

In order to comprehend and enhance models that describes various brain regions is important to study the dynamics of trained recurrent neural networks. Including Dales law in such models usually presents several challenges. However, this is an important aspect that allows computational models to better capture the characteristics of the brain. Here we present a framework to train networks using such constraint. Then we have used it to train them in simple decision making tasks. We characterized the eigenvalue distributions of the recurrent weight matrices of such networks. Interestingly, we discovered that the non-dominant eigenvalues of the recurrent weight matrix are distributed in a circle with a radius less than 1 for those whose initial condition before training was random normal and in a ring for those whose initial condition was random orthogonal. In both cases, the radius does not depend on the fraction of excitatory and inhibitory units nor the size of the network. Diminution of the radius, compared to networks trained without the constraint, has implications on the activity and dynamics that we discussed here.
Cyber Ranges Implementation Methodology

Abstract Cyber Ranges represent complex, multi-component informatic systems, mainly used in the cyber security field. This domain is not the only operational scope of the Cyber Range concept. The applicability of this kind of system presents great benefits in domains like education, research, military etc. The entire scope of Cyber Range systems may be classified into three main categories: training and simulation, research and e-learning. The complexity of a Cyber Range system involves a time and financial consuming implementation. Depending on the area of operation, implementation may vary because each domain involves specific components. As a result, there must be steps prior to implementation in which the whole set of required pieces must be determined precisely. The existence of an implementation methodology may increase the performance and decrease the implementation costs - in the manner of time and financial resources. This paper aims to lay the foundations of a methodology for implementing cyber range systems in the main fields (training and simulation, research and e-learning). Starting from this methodology, detailed configuration and implementation procedures can be developed in each branch of each field.