Reviewerly/exHarmony huggingface dataset:
A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs

Probabilistic Answer Set Programming under the credal semantics has emerged as one of the possible formalisms to encode uncertain domains described by an answer set program extended with probabilistic facts. Some problems require associating probability values to probabilistic facts such that the probability of a query is above a certain threshold. To solve this, we propose a new class of programs, called Probabilistic Optimizable Answer Set Programs, together with a practical algorithm based on constrained optimization to solve the task.
Statistical Relational Structure Learning with Scaled Weight Parameters

Markov Logic Networks (MLNs) combine relational specifications with probabilistic learning and reasoning. Although they can be specified without reference to a particular domain, their performance across domains of different sizes is generally unfavorable. Domain-size Aware Markov Logic Networks (DA-MLNs) address this issue by scaling down weight parameters based on the domain size. This allows for faster learning by training models on a random sample of the original domain. DA-MLNs also enable transferring models between naturally occurring domains of different sizes. This study proposes a combination of functional gradient boosting and weight scaling for single-target structure learning on large domains. It also evaluates performance and runtime on two benchmark domains of contrasting sizes. The results demonstrate that training a DA-MLN from a sample can significantly reduce learning time for large domains with minor performance trade-offs, which decrease with the size of the original domain. Additionally, the study explores how scaling reacts to varying domain sizes in a synthetic social network domain. It is observed that DA-MLNs outperform unscaled MLNs when the number of connections between individuals grows with domain size, but perform worse when the number of connections remains constant. This justifies the use of unscaled MLNs when sampling isolated subcommunities in areas such as social sciences research.
Regularization in Probabilistic Inductive Logic Programming

Abstract Probabilistic Logic Programming combines uncertainty and logic-based languages. Liftable Probabilistic Logic Programs have been recently proposed to perform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning via Expectation Maximization and LBFGS. In this paper, we present an updated version of LIFTCOVER, called LIFTCOVER+, in which regularization was added to improve the quality of the solutions and LBFGS was replaced by gradient descent. We tested LIFTCOVER+ on the same 12 datasets on which LIFTCOVER was tested and compared the performances in terms of AUC-ROC, AUC-PR, and execution times. Results show that in most cases Expectation Maximization with regularization improves the quality of the solutions.
Digital Transformation of Heat Supply and Unified Heat Supply Organizations Based on the Introduction of Digital Control Elements

Decrease in the efficiency of the heat supply in Russia due to the high level of deterioration and insufficient modernization of the system cause the necessity to find the ways for its development. As a result of the analysis of the practice of implementing heat supply digital and information solutions, performed within research, an increase in the efficiency and reliability of heat supply organizations and systems through their use was noted. The importance of using a systematic approach to the digital transformation of heat supply, which is implemented through unified heat supply organizations, is emphasized, since they are responsible for monitoring the main components of the heat supply system functioning, including the processes of its digitalization. Therefore, before planning and implementing the digitalization of the heat supply system, it is necessary to transform existing approaches to the organization and implementation of unified heat supply organizations business processes. For such processes digital transformation is considered as a form of managerial, organizational and technological changes which contribute to improving the efficiency of both the organizations themselves and heat supply systems in the territories assigned to them, as well as influencing the development of digitalization of the heat supply industry in general. This provision determined the purpose of the research, which is to develop a model for implementing the digital transformation of unified heat supply organizations. By summarizing individual elements and conditions for the digital transformation of organizations, the author proposes a model for implementing digital transformation of unified heat supply organizations. This model is detailed at the corresponding stages, including description of the tools used at these stages, as well as indicators and directions reflecting the effectiveness of the digital transformation of the organizations under consideration. Obtained results reflect a consistent approach to the implementation of unified heat supply organizations digital transformation, including an analysis of the current state and prospects of digital transformation, definition of its main goals and indicators, development of digital transformation measures, directions for their implementation, evaluation and control. Its’ successful implementation contributes both to increasing efficiency of unified heat supply organizations activities, as well as to improving reliability and reducing accidents in heat supply systems.
Learning Strategies of Inductive Logic Programming Using Reinforcement Learning

Learning settings are crucial for most Inductive Logic Programming (ILP) systems to learn efficiently. Hypothesis spaces can be huge, and ILP systems take a long time to output solutions or even cannot terminate within time limits. Therefore, users must set suitable learning settings for each ILP task to bring the best performance of the system. However, most users struggle to set appropriate settings for the task they see for the first time. In this paper, we propose a method to make an ILP system more adaptable to tasks with weak learning biases. In particular, we attempt to learn efficient strategies for an ILP system using reinforcement learning (RL). We use Popper, a state-of-the-art ILP system that implements the concept of learning from failures (LFF). We introduce RL-Popper, which divides the hypothesis space into subspaces more minutely than Popper. RL is used to learn the efficient search order of the divided spaces. We provide the details of RL-Popper and show some empirical results.
Towards ILP-Based $$\text {LTL}_\text {f}$$ Passive Learning

Inferring a $$\text {LTL}_\text {f}$$ formula from a set of example traces, also known as passive learning, is a challenging task for model-based techniques. Despite the combinatorial nature of the problem, current state-of-the-art solutions are based on exhaustive search. They use an example at the time to discard a single candidate formula at the time, instead of exploiting the full set of examples to prune the search space. This hinders their applicability when examples involve many atomic propositions or when the target formula is not small. This short paper proposes the first ILP-based approach for learning $$\text {LTL}_\text {f}$$ formula from a set of example traces, using a learning from answer sets system called ILASP. It compares it to both pure SAT-based techniques and the exhaustive search method. Preliminary experimental results show that our approach improves on previous SAT-based techniques and that has the potential to overcome the limitation of an exhaustive search by optimizing over the full set of examples. Further research directions for the ILP-based $$\text {LTL}_\text {f}$$ passive learning problem are also discussed.
Select First, Transfer Later: Choosing Proper Datasets for Statistical Relational Transfer Learning

Statistical Relational Learning (SRL) relies on statistical and probabilistic modeling to represent, learn, and reason about domains with complex relational and rich probability structures. Although SRL techniques have succeeded in many real-world applications, they follow the same assumption as most ML techniques by assuming training and testing data have the same distribution and are sampled from the same feature space. Changes between these distributions might require training a new model using new data. Transfer Learning adapts knowledge already learned to other tasks and domains to help create new models, particularly in a low-data regime setting. Many recent works have succeeded in applying Transfer Learning to relational domains. However, most focus on what and how to transfer. When to transfer is still an open research problem as a pre-trained model is not guaranteed to help or improve performance for learning a new model. Besides, testing every possible pair of source and target domains to perform transference is costly. In this work, we focus on when by proposing a method that relies on probabilistic representations of relational databases and distributions learned by models to indicate the most suitable source domain for transferring. To evaluate our approach, we analyze the performances of two transfer learning-based algorithms given the most similar target domain to a source domain according to our proposal. In the experimental results, our method has succeeded as both algorithms reach their best performance when transferring between the most similar pair of source and target domains.
Meta-interpretive Learning from Fractal Images

Fractals are geometric patterns with identical characteristics in each of their component parts. They are used to depict features which have recurring patterns at ever-smaller scales. This study offers a technique for learning from fractal images using Meta-Interpretative Learning (MIL). MIL has previously been employed for few-shot learning from geometrical shapes (e.g. regular polygons) and has exhibited significantly higher accuracy when compared to Convolutional Neural Networks (CNN). Our objective is to illustrate the application of MIL in learning from fractal images. We first generate a dataset of images of simple fractal and non-fractal geometries and then we implement a technique to learn recursive rules which describe fractal geometries. Our approach uses graphs extracted from images as background knowledge. Finally, we evaluate our approach against CNN-based approaches, such as Siamese Net, VGG19, ResNet50 and DenseNet169.
VEVCC program for concatenation of volcanic events based on cross-correlation analysis

Volcanic eruptions pose a significant risk to communities located near active volcanoes. Disaster mitigation and risk reduction efforts rely on detecting and monitoring volcanic activity as early as possible. This article introduces VEVCC, a MATLAB-based application designed to precisely identify and extract volcanic seismic events from continuous data streams. VEVCC's primary objective is to facilitate the creation of an Excel file containing the arrival times of detected events, which can then be used for various purposes, such as early warning disaster mitigation and automated event identification via machine learning techniques. VEVCC utilizes cross-correlation algorithms to identify volcanic seismic events. It separates these events from background noise and other sources of seismicity, allowing for the construction of a clean and informative dataset. The extracted data is a valuable resource for estimating the frequency of volcanic events and evaluating patterns of volcanic activity. VEVCC's time-stamped event data is indispensable for improving early warning systems, real-time surveillance, and automated event identification. We tested the program on the Merapi volcano datasets during a 1998 campaign for a broadband experiment with the capability to extract the events automatically. Further machine-learning models and algorithms enhance the automatic recognition of volcanic events.
Counting rational points of quartic diagonal hypersurfaces over finite fields

&lt;abstract&gt;&lt;p&gt;Let $ \mathbb{F}_q $ be the finite field of order $ q $ where $ q = p^{k} $, $ k $ is a positive integer and $ p $ is an odd prime. Let $ \mathbb{F}_q^* $ represent the nonzero elements of $ \mathbb{F}_{q} $. For $ f(x_1, \cdots, x_n)\in\mathbb{F}_q[x_1, \cdots, x_n] $, we used $ N\big(f(x_1, \cdots, x_n) = 0\big) $ to denote the number of $ \mathbb{F}_q $-rational points of the affine hypersurface $ f(x_1, \cdots, x_n) = 0 $. In 2020, Zhao et al. obtained the explicit formulae for $ N(x_1^4+x_2^4 = c) $, $ N(x_1^4+x_2^4+x_3^4 = c) $ and $ N(x_1^4+x_2^4+x_3^4+x_4^4 = c) $ over $ \mathbb{F}_q $, with $ c\in\mathbb{F}_q^* $. In this paper, by using Jacobi sums and an analog of the Hasse-Davenport theorem, we arrived at explicit formulae for $ N(a_1x_1^4+a_2x_2^4 = c) $ and $ N(b_1x_1^4+b_2x_2^4+b_3x_3^4 = c) $ with $ a_i, b_j\in \mathbb{F}_q^* (1\leq i \leq 2, 1 \leq j \leq 3) $ and $ c\in \mathbb{F}_q $. Furthermore, by using the reduction formula for Jacobi sums, the number of rational points of the quartic diagonal hypersurface $ a_1x_1^4+a_2x_2^4+\cdots+a_nx_n^4 = c $ of $ n\geq 4 $ variables with $ a_i\in\mathbb{F}_q^* $ $ (1\leq i\leq n) $, $ c\in\mathbb{F}_q $ and $ p\equiv1({\rm{mod}} \ 4) $, can also be deduced. These extended and improved earlier results.&lt;/p&gt;&lt;/abstract&gt;
Test Automation Framework with Help of Synonym-Based Identification Algorithm

The aim of this research paper is to propose a test automation framework that uses a synonym-based locator identification algorithm to identify locators available on web page through scraping the data and representing it using Python flask framework (PFF). The proposed technique also aims to simplify and streamline the process of test automation for web applications, making it more efficient and effective by using suitable data structure. This research also depicts benefits such as reliability, finding similar components on the web page.
Augmented Reality-Based Education Ecosystem for Specially Abled Children

Children who are mentally unstable or who have small or major physical organ impairments are referred to as “specially abled” children to help them integrate into life's normal flow and give them a nurturing environment in which they can thrive. Carrying hefty books and unappealing texts affects their level of concentration to a greater extent. An application must be devised to satisfy the ongoing needs of students who need special care in education. Any student requiring special care in education can learn by hearing and seeing things. Students can listen to sound through which they will be able to listen to audio of instructors coaching the subject. In this application, which is built using Android Studio, when one scans a photo in a book, a 3D animation is projected through the mobile phone in 3D. An improvised learning environment can be provided through the application. The concept of AR is so new and powerful for learning. The AR application’s main motive is “ANYONE CAN LEARN ANYWHERE ANYTIME”. The app is built offline, to be accessed by anyone at any time. For future developments, the online mode can be added to have more access to learning content.
The Contributions of Technologies and ICT Tools for Teaching and Learning Languages

ICT tools have brought a significant change in the way we teach and learn languages. These tools have made language learning more accessible, enjoyable, and interactive. By selecting the right tool and creating a regular learning schedule, students can achieve their language learning goals with ease. The present study seeks to introduce and classify the different types of ICT tools that can be used in learning languages. The advantages, features and descriptions of several ICT tools are also introduced and how they can be utilized for learning different languages.
Removing Backdoors in Pre-trained Models by Regularized Continual Pre-training

Abstract Recent research has revealed that pre-trained models (PTMs) are vulnerable to backdoor attacks before the fine-tuning stage. The attackers can implant transferable task-agnostic backdoors in PTMs, and control model outputs on any downstream task, which poses severe security threats to all downstream applications. Existing backdoor-removal defenses focus on task-specific classification models and they are not suitable for defending PTMs against task-agnostic backdoor attacks. To this end, we propose the first task-agnostic backdoor removal method for PTMs. Based on the selective activation phenomenon in backdoored PTMs, we design a simple and effective backdoor eraser, which continually pre-trains the backdoored PTMs with a regularization term in an end-to-end approach. The regularization term removes backdoor functionalities from PTMs while the continual pre-training maintains the normal functionalities of PTMs. We conduct extensive experiments on pre-trained models across different modalities and architectures. The experimental results show that our method can effectively remove backdoors inside PTMs and preserve benign functionalities of PTMs with a few downstream-task-irrelevant auxiliary data, e.g., unlabeled plain texts. The average attack success rate on three downstream datasets is reduced from 99.88% to 8.10% after our defense on the backdoored BERT. The codes are publicly available at https://github.com/thunlp/RECIPE.
ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation

Abstract Compositional generalization benchmarks for semantic parsing seek to assess whether models can accurately compute meanings for novel sentences, but operationalize this in terms of logical form (LF) prediction. This raises the concern that semantically irrelevant details of the chosen LFs could shape model performance. We argue that this concern is realized for the COGS benchmark (Kim and Linzen, 2020). COGS poses generalization splits that appear impossible for present-day models, which could be taken as an indictment of those models. However, we show that the negative results trace to incidental features of COGS LFs. Converting these LFs to semantically equivalent ones and factoring out capabilities unrelated to semantic interpretation, we find that even baseline models get traction. A recent variable-free translation of COGS LFs suggests similar conclusions, but we observe this format is not semantically equivalent; it is incapable of accurately representing some COGS meanings. These findings inform our proposal for ReCOGS, a modified version of COGS that comes closer to assessing the target semantic capabilities while remaining very challenging. Overall, our results reaffirm the importance of compositional generalization and careful benchmark task design.
De-Noising Diffusion of Image Using Probabilistic Models

Generative diffusion processes are an emerging and effective tool for image and speech generation. In the existing methods, the underlying noise distribution of the diffusion process is Gaussian noise. However, fitting distributions with more degrees of freedom could improve the performance of such generative models.Image de-noising is used to remove the additive noise while retaining as much as possible the important signal features. Generally, data sets collected by image sensors are contaminated by noise. Imperfect instruments, problems with data acquisition process, and interfering natural phenomena can all corrupt the data of interest. Using model training procedures which helped to improve and achieve “image fidelity” rivaling GANs and established the validity of these new generative algorithms. "generative" mean in the name "Generative Adversarial Network"? "Generative" describes a class of statistical models that contrasts with discriminative models.Mode recently becomes super popular for image generation and achieves competitive Result to usual state of the art gangs, especially in generative art field diffusion model have enabled rally amazing result for text to image i.e., text is generating image from text “A bird is flying on the sky, evening nature scene”.
<scp>QAmeleon</scp>: Multilingual QA with Only 5 Examples

Abstract The availability of large, high-quality datasets has been a major driver of recent progress in question answering (QA). Such annotated datasets, however, are difficult and costly to collect, and rarely exist in languages other than English, rendering QA technology inaccessible to underrepresented languages. An alternative to building large monolingual training datasets is to leverage pre-trained language models (PLMs) under a few-shot learning setting. Our approach, QAmeleon, uses a PLM to automatically generate multilingual data upon which QA models are fine-tuned, thus avoiding costly annotation. Prompt tuning the PLM with only five examples per language delivers accuracy superior to translation-based baselines; it bridges nearly 60% of the gap between an English-only baseline and a fully-supervised upper bound fine-tuned on almost 50,000 hand-labeled examples; and consistently leads to improvements compared to directly fine-tuning a QA model on labeled examples in low resource settings. Experiments on the TyDiqa-GoldP and MLQA benchmarks show that few-shot prompt tuning for data synthesis scales across languages and is a viable alternative to large-scale annotation.1
A Survey on Image Steganography using LSB Algorithm

Today's technology is developing very quickly, and as a result, the world is becoming more transparent and nothing is kept secret. This provides hackers the opportunity to access the information and utilise it for their own purposes. It is incredibly dangerous for everyone when our personal information is used for evil intent. Some of the data in this open world ought to be extremely private and impenetrable to hackers. Here, in our work, we have created a model that would conceal the sensitive information from hackers in a way that, even if the hacker were to successfully obtain the data or file, they would not be able to decipher the concealed message or the main purpose of the file. This project's major goal is to give people protection and hide their private information from the public.
The Influence of Arduino-Based Student Experimentation on the Development of Students’ Skills and Competences

The process of educational development requires the use of new ideas in addition to traditional techniques. In the field of physics education, it is necessary to extend our methodological toolbar in order to teach effectively even under the changed conditions—technological development, changing curricula—and provide students with the same quality of education. Arduino-based student experimentation is an implicit way of learning, which gives the opportunity to students to work in small groups and conduct experiments with the application of modern digital devices. It involves students completely in the work processes and develops those skills and competences that are essential for a successful member of the society. The method we developed encourages students to participate actively in their learning processes and acquire knowledge by observing, describing and understanding different phenomena. The overall aim of our study is to present an Arduino-based method, where a digital device helps to improve physics teaching. Meanwhile, we map the competencies of the physics teachers that have to be improved for such a teaching project. The project pointed out that there is a demand for the development of TPACK—Technological Pedagogical Content Knowledge—courses in physics teacher education.