Reviewerly/exHarmony huggingface dataset:
A Unified Formal Framework for Factorial and Probabilistic Topic Modelling

Topic modelling has become a highly popular technique for extracting knowledge from texts. It encompasses various method families, including Factorial methods, Probabilistic methods, and Natural Language Processing methods. This paper introduces a unified conceptual framework for Factorial and Probabilistic methods by identifying shared elements and representing them using a homogeneous notation. The paper presents 12 different methods within this framework, enabling easy comparative analysis to assess the flexibility and how realistic the assumptions of each approach are. This establishes the initial stage of a broader analysis aimed at relating all method families to this common framework, comprehensively understanding their strengths and weaknesses, and establishing general application guidelines. Also, an experimental setup reinforces the convenience of having harmonized notational schema. The paper concludes with a discussion on the presented methods and outlines future research directions.
Architectural Patterns for Blockchain Systems and Application Design

Blockchain technology has gained popularity in various applications, including finance transactions and beyond. However, developing blockchain application systems is challenging due to stringent quality requirements, such as performance, scalability, and security. Software architecture plays a critical role in realizing key quality requirements. Nonetheless, little work has been performed on software architectures for blockchain applications since blockchain application development is still a new field. This paper proposes twelve architectural patterns for blockchain application software architectures based on 400 cross-industry real-world applications available on the Internet. We determine the key components of each application guided by a blockchain application taxonomy we developed. We then identify typical architectural patterns from the interactions of these components guided by well-known software patterns, such as peer-to-peer, layered, pipe-filter, and access control. Based on the roles of these patterns, we organize them into four architectural views comprising four structural, two interactional, four transactional, and two security patterns. We describe each pattern in detail using a standard form and demonstrate the patterns through a real-world blockchain application. The use of patterns can be valuable in addressing blockchain’s unique challenges, but creativity remains essential in crafting innovative solutions. Mixing architectural patterns according to varying requirements can help developers communicate effectively.
Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems

Introduction [...]
Edge Consistency Feature Extraction Method for Multi-Source Image Registration

Multi-source image registration has often suffered from great radiation and geometric differences. Specifically, grayscale and texture from similar landforms in different source images often show significantly different visual features, and these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of the descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness.
A Hybrid Approach for Data Hiding using Twofish Algorithm and Compression Steganography Techniques

Data compression and encryption are essential components of information security, facilitating efficient data handling, reduced storage requirements, and secure data transmission. The system presents a novel hybrid data compression algorithm that combines lossy and lossless compression techniques, along with Twofish cryptography. The hybrid approach leverages the strengths of different compression techniques. First, the Huffman coding algorithm is employed to compress textual data efficiently. Subsequently, the cover image undergoes lossy compression using the Discrete Wavelet Transform (DWT) technique. To fortify data security, the Twofish algorithm offers robust and high-level encryption. The encrypted data is then embedded into the compacted cover image using the least significant bit (LSB) technique in a steganographic manner. In the evaluation phase, the system's performance is assessed using key metrics, bits per pixel, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Comparative analysis with existing methodologies demonstrates the superior performance and methodological efficiency of the system. The results indicate that the hybrid approach strikes a balance between compression efficiency and data security, enabling faster data transmission over limited bandwidth connections and effectively utilizing storage media.
Artificial Intelligence and Prohibition of Discrimination from the Perspective of Private Law

Artificial intelligence (AIAI) technologies promise to change our lives in positive way in many aspects while bringing along some risks. One of these risks is the possibility that decisions based on AI systems contain discrimination. Since the prohibition of discriminationProhibition of discrimination is predominantly seen as a matter of public law, it may seem to be questionable to talk about the prohibition of discrimination in private lawPrivate law where principles of private autonomyAutonomy and particularly freedom of contract prevail. Nevertheless, depriving individuals of the opportunity to enter into a fair and freely negotiated contract as a result of discrimination would be incompatible with the ideas underlying the freedom of contract. Moreover, since discrimination is insulting in most of the cases, it also violates the personalPersonal rights rights of the individual who is discriminated against. Thus, discrimination is an issue that also needs to be considered from the perspective of private law. As private law sanctionsPrivate law sanctions, nullityNullity, compensationCompensation or an obligation to contractObligation to contract can be applied against discrimination. The fact that discrimination is the product of a decision-making mechanism using AI systems brings along some legal problems specific to this situation. One of these problems is that the results produced by some AI technologies are unexplainable since the reasons on which the decision is based must first be known to conclude that a decision is based on discrimination.
An efficient and scalable SPARQL query processing framework for big data using MapReduce and hybrid optimum load balancing

The increasing RDF (Resource Description Framework) data volume requires a Hadoop platform for processing queries over large datasets. In this work, SPARQL (Simple Protocol and Rdf Query Language) queries are evaluated with Hadoop based on the objective of minimizing the number of joins through data partitioning for performing map/reduce jobs. The query evaluation time and the number of cross node joins are minimized with the proposed partitioning techniques. Extended vertical partitioning is proposed for distributed data stores based on objects’ explicit information for splitting predicates. For accessing the RDF data, hybrid monarch butterfly with beetle swarm load balancing optimization with Map-reduce (Hybrid Optimum Load Balancing) is applied. The proposed SPARQL query processing is evaluated over large RDF datasets. The proposed approach’s evaluation results are analyzed with the existing approaches, indicating the proposed framework’s efficiency. By using the proposed approach, an accuracy of 97 % is obtained.
Understanding the Dynamics of Gross and Informal Employment Clusters

Employment is a key indication of economic growth, and measuring it is important for determining economic and social policy. The total number of persons participating in profitable economic activity is known as their gross employment. Since the majority of employees in Ecuador are engaged in the informal sector, gross and informal employment are serious problems. An all-encompassing strategy involving the government and the private sector will be necessary to address the issue of informal employment in Ecuador. With two and four clusters, running the K-means algorithm, interpreting the clusters, and using the insights acquired to influence policy decisions are all steps in the approach for using K-means clustering to evaluate ground and informal employment.
The Future of Learning in Romanian Schools: Examining the Transformative Impact of Gaming Technologies in Education—A Pilot Study in Bucharest

This study explores the transformative impact of gaming technologies on education in Romanian schools through a pilot study conducted in Bucharest. The hypothesis is that gaming technologies have the potential to enhance learning outcomes and foster a more interactive and engaging learning environment. The main objectives are to identify the benefits and challenges of using gaming technology in the classroom and to explore the attitudes of teachers toward this approach to learning. To achieve these objectives, an interview was conducted with 22 teachers from both private and public schools in Bucharest, the capital of Romania. The data was analyzed using both qualitative and quantitative methods. The pilot study was conducted in March 2023. The results indicate a generally positive view of gaming technology, with many teachers recognizing its potential to engage students and enhance learning outcomes. The article also discusses the potential challenges and limitations of integrating gaming technology in the classroom, as well as the need for further research in this area. The findings of this study have important implications for the future of learning in Romanian schools.
Student Academic Behavior Attributes: First Phase

This work consists of identifying attributes in the data record that help to understand student behavior, and thereby improve teacher feedback. For this, the J48 algorithms were applied, which were able to identify the base attribute on which students can be grouped and know their possible behavior in the classroom. The results show a ROC curve with an AUC = 0.97 and the grouping of KNN greater than 80%, which indicates possible attributes for the identification of academic student behavior.
Active Methodologies: An Approach to Virtual Teaching in Natural Sciences

During the training process on Natural Science subject in elementary school, it is important for the student to know, understand, respect, conserve, care for and value nature, the benefits it receives, interact with the environment, recognize the characteristics of each element of nature, its cycles, risks and care. The main objective of this work was to implement active methodologies in the science teaching-learning process. The methodology is based on quantitative methods, non-experimental designs, cross-sectional studies, theoretical, empirical and statistical methods. The technique used was a survey as an instrument and a questionnaire for data collection. A survey was applied to 40 people (students, teachers) of eighth grade, to obtain information on the interventions implemented by teachers. As a result of the survey analysis, the need to renew the essential methodological strategies to improve the teaching-learning process was identified, for which three active methodological strategies were developed for Natural Sciences’ teaching associated with the environment (physical, chemical and environmental). The strategies were designed with creativity, the use of environmental and recycling materials together with technology, each with the following elements: title, materials to be used, procedure, graphic representations and application examples. These active methodological strategies allow students to learn through the development of processes and skills that can be used in a variety of emerging situations and also allow teachers to provide a comprehensive and quality education. Based on the experience of the application of strategies in the area of Natural Sciences as future work in the educational context, it is intended to identify a set of abilities and skills in students in various subjects in order to strengthen teaching methodologies in teachers with the application of new technologies and digital resources that generate a new vision in the field of research.
Virtual Laboratories for Learning Chemistry and Physics Through Chemlab and Modellus

In the present-day context, educational institutions have difficulties maintaining technological equipment available to the academic community. Therefore, the technology tools’ implementation for teaching Physics and Chemistry, through virtual laboratories in applications such as Chemlab and Modellus, is pertinent. The objective is to analyze Chemlab and Modellus simulators as didactic strategies that allow integral learning between theory and practice, applied to 200 high-school students of a public educational institution. The methodology is based on a mixed approach to gather information followed by an analysis and interpretation of the results, which are relevant to the use of qualitative and quantitative data based on the measurement of the variables. The subsequent results show satisfaction in the processes according to a survey of professional experts in the area and students who demonstrate to be satisfied acquiring and understanding contents and topics related to the environment reducing the difficulty degree, awakening interest, achieving a balance between theoretical concepts with experimentation and solving exercises, consolidating acquired competencies such as communication and technological skills, critical thinking, autonomy, collaborative work, leadership, decision-making, and resolution capacity.
Didactic-Disruptive: Technological Tool for Children Who Present Slow Learning of Basic Arithmetic Operations

Didactic materials have been, for a long time, the main support to achieve the teaching–learning process of boys and girls. With the use of Information and Communication Technologies, a new proposal for disruptive teaching materials arises, which introduce a new way of teaching or learning, breaking with traditional methods and promoting creativity and innovation; having the intention of stimulating the imagination and curiosity of the students, making them actively participate in their learning process. Mathematics is one of the oldest sciences, which is in our daily lives fundamental since they help us to better understand our environment. Their study is not very pleasant for some children and adults, as they have difficulties learning specific mathematical skills, such as mental calculation or problem-solving. In this mixed-type research, the survey, interview, observation, and analysis of documents were used as a data collection tool, obtaining as a result a technological application, which aims to serve as didactic support to improve academic performance, of children who attend the first grade of primary school and who present slow learning, in handling basic arithmetic operations.
Text augmentation for semantic frame induction and parsing

Abstract Semantic frames are formal structures describing situations, actions or events, e.g., Commerce buy , Kidnapping , or Exchange . Each frame provides a set of frame elements or semantic roles corresponding to participants of the situation and lexical units (LUs)—words and phrases that can evoke this particular frame in texts. For example, for the frame Kidnapping , two key roles are Perpetrator and the Victim , and this frame can be evoked with lexical units abduct, kidnap , or snatcher . While formally sound, the scarce availability of semantic frame resources and their limited lexical coverage hinders the wider adoption of frame semantics across languages and domains. To tackle this problem, firstly, we propose a method that takes as input a few frame-annotated sentences and generates alternative lexical realizations of lexical units and semantic roles matching the original frame definition. Secondly, we show that the obtained synthetically generated semantic frame annotated examples help to improve the quality of frame-semantic parsing. To evaluate our proposed approach, we decompose our work into two parts. In the first part of text augmentation for LUs and roles, we experiment with various types of models such as distributional thesauri, non-contextualized word embeddings (word2vec, fastText, GloVe), and Transformer-based contextualized models, such as BERT or XLNet. We perform the intrinsic evaluation of these induced lexical substitutes using FrameNet gold annotations. Models based on Transformers show overall superior performance, however, they do not always outperform simpler models (based on static embeddings) unless information about the target word is suitably injected. However, we observe that non-contextualized models also show comparable performance on the task of LU expansion. We also show that combining substitutes of individual models can significantly improve the quality of final substitutes. Because intrinsic evaluation scores are highly dependent on the gold dataset and the frame preservation, and cannot be ensured by an automatic evaluation mechanism because of the incompleteness of gold datasets, we also carried out experiments with manual evaluation on sample datasets to further analyze the usefulness of our approach. The results show that the manual evaluation framework significantly outperforms automatic evaluation for lexical substitution. For extrinsic evaluation, the second part of this work assesses the utility of these lexical substitutes for the improvement of frame-semantic parsing. We took a small set of frame-annotated sentences and augmented them by replacing corresponding target words with their closest substitutes, obtained from best-performing models. Our extensive experiments on the original and augmented set of annotations with two semantic parsers show that our method is effective for improving the downstream parsing task by training set augmentation, as well as for quickly building FrameNet-like resources for new languages or subject domains.
Bilingual Legal Resources for Arabic: State of Affairs and Future Perspectives

Abstract The context-based use of terminology and phraseology is one of the essential building blocks of legal translation. The contextual nature of both components has implications when it comes to designing resources that are adapted to the needs of translators. For Arabic legal translation, there are a multitude of different print and online resources available, however, they do not integrate the context-related parameter for term choice acceptability. In this article, we will describe the main features of certain bilingual legal dictionaries with the English-Arabic and French-Arabic language pairs. We will then make a descriptive assessment of the tools available online, highlighting their limitations. Taking into consideration all the contextual parameters involved in making a translation choice, we will put forward the value of developing bilingual ontologies with Arabic. With the rapid expansion of information technologies, a move towards formalizing legal knowledge will help fill existing gaps in the representation of Arabic legal content and the retrieval of information, providing legal translators with a tool that provides specific details that will enable translators to make informed and relevant decisions, in addition to opening new research perspectives for Arabic legal translation.
Subtracting Reasons in Normative Domains

Abstract Practical reasons can be aggregated to decide what one ought to do. This paper explores an operation that undoes aggregation: subtraction. I consider several distinctions concerning subtraction: subtracting content and subtracting strength; and subtracting one reason from one other reason or from a set of reasons. I put forward a precise understanding of subtracting the content of one reason from another, based on an operation of difference on a state-like, structured notion of content. Finally, I apply my approach to subtracting reasons to normative domains, and in particular to belief revision and norm change.
Keep It Simple: Evaluating Local Search-Based Latent Space Editing

Abstract Semantic image editing allows users to selectively change entire image attributes in a controlled manner with just a few clicks. Most approaches use a generative adversarial network (GAN) for this task to learn an appropriate latent space representation and attribute-specific transformations. Attribute entanglement has been a limiting factor for previous approaches to attribute manipulation. However, more recent approaches have made significant improvements in this regard using separate networks for attribute extraction. Iterative optimization algorithms based on backpropagation can be used to find attribute vectors with minimal entanglement, but this requires large amounts of GPU memory, can lead to training instability, and requires differentiable models. To circumvent these issues, we present a local search-based approach to latent space editing that achieves comparable performance to existing algorithms while avoiding the aforementioned drawbacks. We also introduce a new evaluation metric that is easier to interpret than previous metrics.
Entanglement classification via witness operators generated by support vector machine

Abstract Although entanglement is a basic resource for reaching quantum advantage in many computation and information protocols, we lack a universal recipe for detecting it, with analytical results obtained for low-dimensional systems and few special cases of higher-dimensional systems. In this work, we use a machine learning algorithm, the support vector machine with polynomial kernel, to classify separable and entangled states. We apply it to two-qubit and three-qubit systems, and we show that, after training, the support vector machine is able to recognize if a random state is entangled with an accuracy up to $$92\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>92</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for the two-qubit system and up to $$98\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>98</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for the three-qubit system. We also describe why and in what regime the support vector machine algorithm is able to implement the evaluation of an entanglement witness operator applied to many copies of the state, and we describe how we can translate this procedure into a quantum circuit.