Reviewerly/exHarmony huggingface dataset:
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters

High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure. However, most of the existing HO-GNNs are hop-based, i.e., they rely on the powers of the transition matrix. As a result, these architectures are not fully reactive to the classification loss and the achieved structural filters have static supports. In other words, neither the filters' supports nor their coefficients can be learned with these networks. They are confined, instead, to learn combinations of filters. To address the above concerns, we propose Diffusion-jump GNNs a method relying on asymptotic diffusion distances that operates on jumps. A diffusion-pump generates pairwise distances whose projections determine both the support and coefficients of each structural filter. These filters are called jumps because they explore a wide range of scales in order to find bonds between scattered nodes with the same label. Actually, the full process is controlled by the classification loss. Both the jumps and the diffusion distances react to classification errors (i.e. they are learnable). Homophiliation, i.e., the process of learning piecewise smooth latent spaces in the heterophilic regime, is formulated as a Dirichlet problem: the known labels determine the border nodes and the diffusion-pump ensures a minimal deviation of the semi-supervised grouping from a canonical unsupervised grouping. This triggers the update of both the diffusion distances and, consequently, the jumps in order to minimize the classification error. The Dirichlet formulation has several advantages. It leads to the definition of structural heterophily, a novel measure beyond edge heterophily. It also allows us to investigate links with (learnable) diffusion distances, absorbing random walks and stochastic diffusion.
Statistically Enhanced Learning: a feature engineering framework to boost (any) learning algorithms

Feature engineering is of critical importance in the field of Data Science. While any data scientist knows the importance of rigorously preparing data to obtain good performing models, only scarce literature formalizes its benefits. In this work, we will present the method of Statistically Enhanced Learning (SEL), a formalization framework of existing feature engineering and extraction tasks in Machine Learning (ML). The difference compared to classical ML consists in the fact that certain predictors are not directly observed but obtained as statistical estimators. Our goal is to study SEL, aiming to establish a formalized framework and illustrate its improved performance by means of simulations as well as applications on real life use cases.
Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning

Compositionality is a critical aspect of scalable system design. Reinforcement learning (RL) has recently shown substantial success in task learning, but has only recently begun to truly leverage composition. In this paper, we focus on Boolean composition of learned tasks as opposed to functional or sequential composition. Existing Boolean composition for RL focuses on reaching a satisfying absorbing state in environments with discrete action spaces, but does not support composable safety (i.e., avoidance) constraints. We advance the state of the art in Boolean composition of learned tasks with three contributions: i) introduce two distinct notions of safety in this framework; ii) show how to enforce either safety semantics, prove correctness (under some assumptions), and analyze the trade-offs between the two safety notions; and iii) extend Boolean composition from discrete action spaces to continuous action spaces. We demonstrate these techniques using modified versions of value iteration in a grid world, Deep Q-Network (DQN) in a grid world with image observations, and Twin Delayed DDPG (TD3) in a continuous-observation and continuous-action Bullet physics environment. We believe that these contributions advance the theory of safe reinforcement learning by allowing zero-shot composition of policies satisfying safety properties.
Towards Grammatical Tagging for the Legal Language of Cybersecurity

Legal language can be understood as the language typically used by those engaged in the legal profession and, as such, it may come both in spoken or written form. Recent legislation on cybersecurity obviously uses legal language in writing, thus inheriting all its interpretative complications due to the typical abundance of cases and sub-cases as well as to the general richness in detail. This paper faces the challenge of the essential interpretation of the legal language of cybersecurity, namely of the extraction of the essential Parts of Speech (POS) from the legal documents concerning cybersecurity. The challenge is overcome by our methodology for POS tagging of legal language. It leverages state-of-the-art open-source tools for Natural Language Processing (NLP) as well as manual analysis to validate the outcomes of the tools. As a result, the methodology is automated and, arguably, general for any legal language following minor tailoring of the preprocessing step. It is demonstrated over the most relevant EU legislation on cybersecurity, namely on the NIS 2 directive, producing the first, albeit essential, structured interpretation of such a relevant document. Moreover, our findings indicate that tools such as SpaCy and ClausIE reach their limits over the legal language of the NIS 2.
Would Friedman Burn your Tokens?

Cryptocurrencies come with a variety of tokenomic policies as well as aspirations of desirable monetary characteristics that have been described by proponents as 'sound money' or even 'ultra sound money.' These propositions are typically devoid of economic analysis so it is a pertinent question how such aspirations fit in the wider context of monetary economic theory. In this work, we develop a framework that determines the optimal token supply policy of a cryptocurrency, as well as investigate how such policy may be algorithmically implemented. Our findings suggest that the optimal policy complies with the Friedman rule and it is dependent on the risk free rate, as well as the growth of the cryptocurrency platform. Furthermore, we demonstrate a wide set of conditions under which such policy can be implemented via contractions and expansions of token supply that can be realized algorithmically with block rewards, taxation of consumption and burning the proceeds, and blockchain oracles.
RL4CO: a Unified Reinforcement Learning for Combinatorial Optimization Library

Deep reinforcement learning offers notable benefits in addressing combinatorial problems over traditional solvers, reducing the reliance on domain-specific knowledge and expert solutions, and improving computational efficiency. Despite the recent surge in interest in neural combinatorial optimization, practitioners often do not have access to a standardized code base. Moreover, different algorithms are frequently based on fragmentized implementations that hinder reproducibility and fair comparison. To address these challenges, we introduce RL4CO, a unified Reinforcement Learning (RL) for Combinatorial Optimization (CO) library. We employ state-of-the-art software and best practices in implementation, such as modularity and configuration management, to be flexible, easily modifiable, and extensible by researchers. Thanks to our unified codebase, we benchmark baseline RL solvers with different evaluation schemes on zero-shot performance, generalization, and adaptability on diverse tasks. Notably, we find that some recent methods may fall behind their predecessors depending on the evaluation settings. We hope RL4CO will encourage the exploration of novel solutions to complex real-world tasks, allowing the community to compare with existing methods through a unified framework that decouples the science from software engineering. We open-source our library at https://github.com/ai4co/rl4co.
Saturating linear sets of minimal rank

Saturating sets are combinatorial objects in projective spaces over finite fields that have been intensively investigated in the last three decades. They are related to the so-called covering problem of codes in the Hamming metric. In this paper, we consider the recently introduced linear version of such sets, which is, in turn, related to the covering problem in the rank metric. The main questions in this context are how small the rank of a saturating linear set can be and how to construct saturating linear sets of small rank. Recently, Bonini, Borello, and Byrne provided a lower bound on the rank of saturating linear sets in a given projective space, which is shown to be tight in some cases. In this paper, we provide construction of saturating linear sets meeting the lower bound and we develop a link between the saturating property and the scatteredness of linear sets. The last part of the paper is devoted to show some parameters for which the bound is not tight.
Optimal mixers restricted to subspaces and the stabilizer formalism

We present a novel formalism to both understand and construct mixers that preserve a given subspace. The method connects and utilizes the stabilizer formalism that is used in error correcting codes. This can be useful in the setting when the quantum approximate optimization algorithm (QAOA), a popular meta-heuristic for solving combinatorial optimization problems, is applied in the setting where the constraints of the problem lead to a feasible subspace that is large but easy to specify. The proposed method gives a systematic way to construct mixers that are resource efficient in the number of controlled not gates and can be understood as a generalization of the well-known X and XY mixers and a relaxation of the Grover mixer: Given a basis of any subspace, a resource efficient mixer can be constructed that preserves the subspace. The numerical examples provided show a dramatic reduction of CX gates when compared to previous results. We call our approach logical X-Mixer or logical X QAOA ($\textbf{LX-QAOA}$), since it can be understood as dividing the subspace into code spaces of stabilizers S and consecutively applying logical rotational X gates associated with these code spaces. Overall, we hope that this new perspective can lead to further insight into the development of quantum algorithms.
Synthetic Demographic Data Generation for Card Fraud Detection Using GANs

Using machine learning models to generate synthetic data has become common in many fields. Technology to generate synthetic transactions that can be used to detect fraud is also growing fast. Generally, this synthetic data contains only information about the transaction, such as the time, place, and amount of money. It does not usually contain the individual user's characteristics (age and gender are occasionally included). Using relatively complex synthetic demographic data may improve the complexity of transaction data features, thus improving the fraud detection performance. Benefiting from developments of machine learning, some deep learning models have potential to perform better than other well-established synthetic data generation methods, such as microsimulation. In this study, we built a deep-learning Generative Adversarial Network (GAN), called DGGAN, which will be used for demographic data generation. Our model generates samples during model training, which we found important to overcame class imbalance issues. This study can help improve the cognition of synthetic data and further explore the application of synthetic data generation in card fraud detection.
Partial Syndrome Measurement for Hypergraph Product Codes

Hypergraph product codes are a promising avenue to achieving fault-tolerant quantum computation with constant overhead. When embedding these and other constant-rate qLDPC codes into 2D, a significant number of nonlocal connections are required, posing difficulties for some quantum computing architectures. In this work, we introduce a fault-tolerance scheme that aims to alleviate the effects of implementing this nonlocality by measuring generators acting on spatially distant qubits less frequently than those which do not. We investigate the performance of a simplified version of this scheme, where the measured generators are randomly selected. When applied to hypergraph product codes and a modified small-set-flip decoding algorithm, we prove that for a sufficiently high percentage of generators being measured, a threshold still exists. We also find numerical evidence that the logical error rate is exponentially suppressed even when a large constant fraction of generators are not measured.
PERMAINAN DALAM KESENIAN LAESAN ANAK DI DESA BAJOMULYO KABUPATEN PATI

The study seeks to describe and expalin the imprortance of becoming familiar with the game in a show of laesan the elements in the show. The method useh in this study is a descriptive analysis method that emphasizes on field observations by digging for information and visuals according to facts and data colletion of observation interviews for library studies and document studies. Studies have analyzed the game in a show that uses the theory of Johan Huizinga and Desmita to dissect each elementt of the game including, the song liric, and the property used.
Self-Efficacy pada Hasil Belajar Peserta Didik Materi Bentuk Aljabar Madrasah Tsanawiyah Tanjungpinang

Self-efficacy is one of the factors that affect student learning outcomes. This study was conducted in order to the relationship between self-efficacy with learning outcomes to describe how much the relationship between self-efficacy and learning outcomes, for algebraic form involving twenty-seven 7 grader students of Madrasah Tsanawiyah in Tanjungpinang. To achieve this goal, this study used a correlation technique with a quantitative approach. The analysis was conducted in the form of self-efficacy questionnaire analysis and learning formative tests. Data analysis was performed using Pearson Product Moment correlation techniques with a significance level of 0.05. After doing the analysis phase, this study obtained a correlation coefficient (r = 0.906), the figure can be said to be very strong based on correlation coefficient interpretation as the researcher's reference. In other words, there was a positive and significant relationship between self-efficacy and learning outcomes, for algebraic form involving twenty-five 7 grader students of Madrasah Tsanawiyah in Tanjungpinang.
MIMIC: Masked Image Modeling with Image Correspondences

Dense pixel-specific representation learning at scale has been bottlenecked due to the unavailability of large-scale multi-view datasets. Current methods for building effective pretraining datasets heavily rely on annotated 3D meshes, point clouds, and camera parameters from simulated environments, preventing them from building datasets from real-world data sources where such metadata is lacking. We propose a pretraining dataset-curation approach that does not require any additional annotations. Our method allows us to generate multi-view datasets from both real-world videos and simulated environments at scale. Specifically, we experiment with two scales: MIMIC-1M with 1.3M and MIMIC-3M with 3.1M multi-view image pairs. We train multiple models with different masked image modeling objectives to showcase the following findings: Representations trained on our automatically generated MIMIC-3M outperform those learned from expensive crowdsourced datasets (ImageNet-1K) and those learned from synthetic environments (MULTIVIEW-HABITAT) on two dense geometric tasks: depth estimation on NYUv2 (1.7%), and surface normals estimation on Taskonomy (2.05%). For dense tasks which also require object understanding, we outperform MULTIVIEW-HABITAT, on semantic segmentation on ADE20K (3.89%), pose estimation on MSCOCO (9.4%), and reduce the gap with models pre-trained on the object-centric expensive ImageNet-1K. We outperform even when the representations are frozen, and when downstream training data is limited to few-shot. Larger dataset (MIMIC-3M) significantly improves performance, which is promising since our curation method can arbitrarily scale to produce even larger datasets. MIMIC code, dataset, and pretrained models are open-sourced at https://github.com/RAIVNLab/MIMIC.
Sistem Informasi Manajemen Berkas Perkara Berbasis Web Pada Kejaksaan Negeri Merangin

The Merangin District Prosecutor's Office is a law enforcement agency that is required to play a greater role in enforcing the law. In the current file data management system, it is still done manually, that is, every incoming case file is recorded in the register book and archived into the file cabinet. This makes it difficult for the process of searching for file data and compiling case files which will be processed by the prosecutor's office and making reports. Based on the existing problems, a case file management system is needed in order to assist the Merangin District Attorney in overcoming the existing problems. The purpose of this study is to design a prototype case file management system using system modeling tools, namely UML and the diagrams used include use case diagrams, activity diagrams and class diagrams. The results of this study are a prototype case file management system design that can be applied by the Merangin District Attorney to help expedite case management and provide maximum service to the public.
Cloze Test and Multiple Choice Test for Reading Comprehension.

The objectives of this study are to analyze about the different and similar of multiple choice and cloze test in reading comprehension, and about the perception toward the multiple choice and cloze test among students that influence reading comprehension. This study used descriptive qualitative method. In collecting the data, this study used, Observation, Interview, Questionnaire, and Documentaton. The data were taken by test cloze and multiple choice. This study was conducted to there were 21 informants which were 7 boys and 13 girls of Students and a Teacher of SMA 15 Muara Enim. As a result of this study showed that test was able to make the students tested motivated to learn a lot about English. But there were some who do not understand. In essence, the cloze test and multiple choice test were very suitable to be used as learning test references at SMA 15 Muara Enim.
FEATURES OF THE INTERNET BLOGS LANGUAGE

This article is devoted to the study of the peculiarities of the Internet blog language.The relevance of the research is due to the growing influence of the Internet on all spheres of human life, including communication, and, accordingly, the growing interest among linguists in Internet communication.The relevance of the problem determined the purpose of the article -to identify and analyze the features of the blog language at the phonetic, lexical and grammatical levels.While studying this issue it was found that at the level of phonetics we can talk about phonetic and graphic features, such as sound imitation, the use of interjections and exclamatory intonation, as well as graphic emphasis through punctuation, font and upper case and emoticons.These tools help bloggers to fully convey the intent of their written and oral message, and the readers to feel like an equal participant in communication.At the lexical level, we found that the use of words and turns of phrase in blogs is directly related to the kind of activity, interests, emotional mood of the author and the purpose of creating a post, as well as in some cases it depends on the platform used to create the post.Grammatical categories that are abstract in nature play an important role in creating expressive and emotional effects for the reader's perception of the blog: we can talk about such features as the frequent use of 1st and 2nd person personal pronouns, the formation of new adjectives by adding words and parts of word combinations, the predominance of simple narrative sentences and the use of the imperative.Online language represents a specific communicative sphere and influences the language of others spheres, spreading beyond the Internet.Thus, scientists must study these phenomena to keep up with the times.
The Required reading for Entering Systemic Functional Linguistics: A review of Systemic Functional Grammar: A First Step into the Theory

This article introduces the book Systemic Functional Grammar: A First Step into the Theory by Christian M.I.M. Matthiessen and M.A.K. Halliday, overviewing its contents and features. This book has involved the research fields, research models, key terms, and the origin and developmental stages of Systemic Functional Linguistics, which is the required reading to enter this filed of linguistics study and is significant for beginners.
Signature Identification using Digital Image Processing and Machine Learning Methods

Signature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained from 25 people over the age of 50. The signers provided 20 signatures and were free to choose the stationery used to write the signature on white paper. The total data obtained in this study was 500 signature data. The obtained signature was scanned to create a signature image, which was then pre-processed to prepare it for feature extraction, which can characterize the signature images. The HOG method was used to extract features, resulting in a dataset with 4,536 feature vectors for each signature image. To identify the signature image, the classification methods SVM, Decision Tree, Nave Bayes, and K-NN were compared. SVM achieved the highest accuracy, which is 100%. When K=5, the K-NN method achieved a fairly good accuracy of 97.3%. Meanwhile, Naive Bayes and Decision Tree achieved accuracy significantly lower than K-NN (61%). Because the HOG method produced a large feature vector for each signature, it is recommended that important features that represent signatures be optimized or extracted to produce smaller features to speed up computation without sacrificing accuracy, and that the HOG method be compared to other extraction feature methods to obtain a better model in future research.
Perceived Usefulness and Perceived Ease of Use of Online Learning for Islamic Religious Education Teacher

The primary objective of this study is to examine how the perceived ease of use and usefulness of online learning, specifically using the SPACE (Sistem Pembelajaran Agama Cara Elektronik/Electronic Way of Religion Learning System) platform, impact the effectiveness of teaching professional education students. The study employs a mixed-methods approach, combining both qualitative and quantitative methods, and utilizes descriptive techniques. The research instruments utilized include interview forms and the TAM (technology acceptance model) questionnaire, which assess the participants' perceptions of the ease of use and usefulness of online learning. The research sample was selected using purposive sampling. Data analysis involved the utilization of multiple linear regression analysis. The findings of the study indicate a notable and significant influence of the perceived ease of use and usefulness of online learning. This is supported by the observation that the graduation rate of professional education students who acquire teacher professional certificates increased by 14.77% as a result.