Reviewerly/exHarmony huggingface dataset:
A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering

Maintenance is one of the costliest phases in the software development process. If architectural design models are accessible, software maintenance can be made more straightforward. When the software’s source code is the only available resource, comprehending the program profoundly impacts the costs associated with software maintenance. The primary objective of comprehending the source code is extracting information used during the software maintenance phase. Generating a structural model based on the program source code is an effective way of reducing overall software maintenance costs. Software module clustering is considered a tremendous reverse engineering technique for constructing structural design models from the program source code. The main objectives of clustering modules are to reduce the quantity of connections between clusters, increase connections within clusters, and improve the quality of clustering. Finding the perfect clustering model is considered an NP-complete problem, and many previous approaches had significant issues in addressing this problem, such as low success rates, instability, and poor modularization quality. This paper applied the horse herd optimization algorithm, a distinctive population-based and discrete metaheuristic technique, in clustering software modules. The proposed method’s effectiveness in addressing the module clustering problem was examined by ten real-world standard software test benchmarks. Based on the experimental data, the quality of the clustered models produced is approximately 3.219, with a standard deviation of 0.0718 across the ten benchmarks. The proposed method surpasses former methods in convergence, modularization quality, and result stability. Furthermore, the experimental results demonstrate the versatility of this approach in effectively addressing various real-world discrete optimization challenges.
General introduction to the project “Open semiotics”

This article is a Russian translation of the General introduction to the four-volume project “Open Semiotics”, published in 2023 under the editorship of Amir Biglari in the L'Harmattan publishing house. The original imprint – Biglari, A. (2023), General intoduction, A. Biglari (ed.), Open Semiotics: Epistemological and Conceptual Foundations, L'Harmattan, Paris, vol. 1, pp. 33–43.“Open Semiotics” comprises four volumes: (1) Epistemological and Conceptual Foundations, (2) Culture and Society, (3) Texts, Images, Arts, (4) Life and its Extensions. The project includes 141 chapters authored by 178 scholars from 58 countries spanning all continents, which represent a broad array of trends and approaches as well as numerous and diverse disciplinary crossings.Given that signs and meanings pervade the world in its different aspects, semiotics is naturally open to interactions with other fields, from the humanities and social sciences to the natural and pure sciences. Open Semiotics aims to explore and expand these interactions, and to facilitate new avenues for interdisciplinary and transdisciplinary research, providing insights into a redeployment of disciplinary fields.
Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk

The objective of this study was to determine whether multi-microRNA analysis using a combination of four microRNA biomarkers (miR-1246, 202, 21, and 219B) could improve the diagnostic performance of mammography in determining breast cancer risk by age group (under 50 vs. over 50) and distinguish breast cancer from benign breast diseases and other cancers (thyroid, colon, stomach, lung, liver, and cervix cancers). To verify breast cancer classification performance of the four miRNA biomarkers and whether the model providing breast cancer risk score could distinguish between benign breast disease and other cancers, the model was verified using nonlinear support vector machine (SVM) and generalized linear model (GLM) and age and four miRNA qRT-PCR analysis values (dCt) were input to these models. Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. The results of this study showed that this multi-microRNA analysis using the four miRNA biomarkers was effective in classifying breast cancer in patients under the age of 50, which is challenging to accurately diagnose. In addition, breast cancer and benign breast diseases can be classified, showing the possibility of helping with diagnosis by mammography. Verification of the performance of the four miRNA biomarkers confirmed that multi-microRNA analysis could be used as a new breast cancer screening aid to improve the accuracy of mammography. However, many factors must be considered for clinical use. Further validation with an appropriate screening population in large clinical trials is required. This trial is registered with (KNUCH 2022-04-036).
From the detection of monitoring anomalies to the probabilistic forecast of the evolution of volcanic unrest: an entropy-based approach

Abstract Owing to the current lack of plausible and exhaustive physical pre-eruptive models, often volcanologists rely on the observation of monitoring anomalies to track the evolution of volcanic unrest episodes. Taking advantage from the work made in the development of Bayesian Event Trees (BET), here we formalize an entropy-based model to translate the observation of anomalies into probability of a specific volcanic event of interest. The model is quite general and it could be used as a stand-alone eruption forecasting tool or to set up conditional probabilities for methodologies like the BET and of the Bayesian Belief Network (BBN). The proposed model has some important features worth noting: (i) it is rooted in a coherent logic, which gives a physical sense to the heuristic information of volcanologists in terms of entropy; (ii) it is fully transparent and can be established in advance of a crisis, making the results reproducible and revisable, providing a transparent audit trail that reduces the overall degree of subjectivity in communication with civil authorities; (iii) it can be embedded in a unified probabilistic framework, which provides an univocal taxonomy of different kinds of uncertainty affecting the forecast and handles these uncertainties in a formal way. Finally, for the sake of example, we apply the procedure to track the evolution of the 1982–1984 phase of unrest at Campi Flegrei.
Sphinx-in-the-Head: Group Signatures from Symmetric Primitives

Group signatures and their variants have been widely used in privacy-sensitive scenarios such as anonymous authentication and attestation. In this paper, we present a new post-quantum group signature scheme from symmetric primitives. Using only symmetric primitives makes the scheme less prone to unknown attacks than basing the design on newly proposed hard problems whose security is less well-understood. However, symmetric primitives do not have rich algebraic properties, and this makes it extremely challenging to design a group signature scheme on top of them. It is even more challenging if we want a group signature scheme suitable for real-world applications, one that can support large groups and require few trust assumptions. Our scheme is based on MPC-in-the-head non-interactive zero-knowledge proofs, and we specifically design a novel hash-based group credential scheme, which is rooted in the SPHINCS+ signature scheme but with various modifications to make it MPC (multi-party computation) friendly. The security of the scheme has been proved under the fully dynamic group signature model. We provide an implementation of the scheme and demonstrate the feasibility of handling a group size as large as 2 60 . This is the first group signature scheme from symmetric primitives that supports such a large group size and meets all the security requirements.
Agile Technical Debt Management usingthe LTD Framework

The Technical Debt (TD) metaphor refers to the unavoidable maintenance and evolution costs of the notquite- right decisions commonly taken by software developers. Due to its clear importance, developers usually document and manage TD by adopting ad-hoc and informal artifacts and activities. Thus, in this paper, we first propose a lightweight framework to support agile teams to manage and increase the awareness on TD. This framework, called LTD: Less Technical Debt Framework, has four key activities-TD Consensus, TD Discovery, TD Planning, and TD Payment-that can be easily plugged into current agile methodologies, such as Scrum. To assess the applicability of LTD in a real context, we also conduct a case study with two Scrum teams from a large public company. As a result, we achieved promising outcomes after adopting the framework. For example, the teams could reduce TD by creating a backlog of issues to pay during sprints.
Sanitized clustering against confounding bias

Abstract Real-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding factor that disturbs the cluster analysis. Existing methods eliminate the biases by projecting data onto the orthogonal complement of the subspace expanded by the confounding factor before clustering. Therein, the interested clustering factor and the confounding factor are coarsely considered in the raw feature space, where the correlation between the data and the confounding factor is ideally assumed to be linear for convenient solutions. These approaches are thus limited in scope as the data in real applications is usually complex and non-linearly correlated with the confounding factor. This paper presents a new clustering framework named Sanitized Clustering Against confounding Bias, which removes the confounding factor in the semantic latent space of complex data through a non-linear dependence measure. To be specific, we eliminate the bias information in the latent space by minimizing the mutual information between the confounding factor and the latent representation delivered by variational auto-encoder. Meanwhile, a clustering module is introduced to cluster over the purified latent representations. Extensive experiments on complex datasets demonstrate that our SCAB achieves a significant gain in clustering performance by removing the confounding bias.
TRIK MAGASING: MENINGKATKAN PRESTASI BELAJAR MATEMATIKA SD

Mathematics learning outcomes are still said to be low, because many students have not yet reached the KKM. Many factors can influence student learning outcomes in mathematics such as student interest in learning, learning motivation, and student learning creativity. The aim of this research is to determine the effect of applying the magazine trick in mathematics to improve student learning achievement. The population of this study consisted of 8 students who were class VI students from elementary schools in Gunungkidul. With a quantitative approach, Pre-Experimental Design type of experiment. Research results show that the magazine trick can influence students' learning interest, learning motivation and learning creativity, which supports an increase in achievement or learning outcomes. There was an increase in the average value of 28.75, from 28.75 to 86.25.
On Mining User-Item Interactions via Knowledge Graph for Recommendation

Introducing Knowledge Graph (KG) to facilitate recommender system has become a tendency in recent years. Many existing methods leverage KG to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KG also may contribute to better user representation learning. To solve the above issue, we propose a novel algorithm, KIGR ( K nowledge-aware I nteraction G raph for R ecommendation), to mine user-item interactions via Knowledge Graph for assisting user representation learning. Specifically, a user-item interaction is encoded by attentively summing up the relation embedding about the item in KG. Then an unsupervised learning method is used to group the user-item interactions into different latent types. Further, a user-item interaction graph is divided into several subgraphs, which is referred to as Knowledge-aware Interaction Graph, making each subgraph only contains one latent type of interactions. Finally, user representation is the fusion of user interest embedding, which is learned on knowledge-aware interaction graph; While item representation is learned on KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the SOTA algorithms.
Artificial Intelligence: Where the Music of the Future Is Heading

In this chapter we venture into the future of music technologies, paying particular attention to the role that artificial intelligence (AI) may play in the creative phase and circulation of music in the coming years. In the music world is emerging a feeling that artificial intelligence may soon replace artists and musicians, transforming creativity into something cold and functional, perhaps at the service of the profit of industries and digital platforms. Our exploration about the future of music includes different levels of investigation. We will depart from a reflection related to the long-standing fascination about the mathematical foundations of music and the possibility of automating music creativity. Then, we will move to the analysis of some concrete applications of artificial intelligence in the world of music production, departing from the activities and strategies of the Creator Technology Research Lab, started in 2018 by Spotify; then we will analyze some of the most relevant platforms and technologies for automated music creation and distribution, including the service Amper for music creation, LANDR for audio mastering, and a series of blockchain-based tools for music distribution. Finally, our reflection will shift to what the implications might be of an ever more massive diffusion of different AI tools, primarily blockchain technology and its possible applications in the music sector.
ON THE MAXIMUM CIRCULAR INVERSES OF MAXIMUM CIRCLES

In this study, the inverses of maximum circles under the maximum circular inversion are examined. The maximum circular inversion is observed to transform maximum circles to curves consisting of parabolic arcs, line segments, and sometimes rays. It is seen that the image curve occurs in different shapes depending on the relative positions of the inversion circle and the maximum circle. While the images of maximum circles not passing through the inversion center are closed curves, the images of those passing through the inversion center are not closed. Inverse curves have been studied by considering the radii of the maximum circles and the positions of the lines joining their centers and the center of maximum inversion. Classifications of the inverses of maximum circles are presented.
Exploring the Use of Interlibrary Loan Service and User Preferences in a Countywide Public Library System

Interlibrary Loan (ILL) service allows users to borrow materials from other libraries, whether physical or electronic. We sought to explore the usage and preferences of ILL service among users of a county-wide public library system in Tennessee. The survey consisted of questions pertaining to the frequency of ILL usage, the types of materials requested, and overall satisfaction with the service. Our findings show which aspects of ILL users find beneficial, as well as any shortcomings that could be addressed. Overall, those who utilized ILL service found it very valuable, would recommend it to others and expressed satisfaction with their experience.
Lambek Calculus with Optional Divisions

In this paper, we introduce an extension of the Lambek calculus by optional divisions ( $$\textrm{L}_{opt}$$ ). Namely, the right optional division $$A \angle B$$ is defined as $$A \wedge (A/B)$$ , and the left one is defined as $$A \wedge (B \backslash A)$$ . A possible linguistic motivation to consider the new operations is describing verbs with optional arguments, e.g. reads in Tim reads the book. $$\textrm{L}_{opt}$$ is a fragment of the multiplicative-additive Lambek calculus, so it would be interesting to compare the two calculi. The main part of the paper is devoted to the following grammar result: finite intersections of context-free languages can be generated by grammars over the calculus $$\textrm{L}_{opt}$$ . The proof involves introducing a useful normal form for Lambek grammars, namely, the notion of an interpretable grammar.
Formalizing Henkin-Style Completeness of an Axiomatic System for Propositional Logic

I formalize a Henkin-style completeness proof for an axiomatic system for propositional logic in the proof assistant Isabelle/HOL. The formalization precisely details the structure of this proof method.
Hope for Epistemic Reasoning with Faulty Agents!

In this paper, we study a new epistemic modal operator, termed hope, which has been introduced recently in the context of a novel epistemic reasoning framework for distributed multi-agent systems with arbitrarily (“byzantine”) faulty agents. It has been proved that both preconditions of actions used in agent protocols, as well as assertions about the epistemic states of agents obtained for analysis purposes, need to be restricted in a particular way for such systems. Hope has been proposed as the most promising candidate for analysis purposes, and defined in terms of the standard knowledge operator. To support the challenging next step of defining the semantics of common hope and eventual common hope, which are crucial for the analysis of fault-tolerant distributed agreement algorithms, we provide a suitable axiomatization of individual hope that avoids knowledge altogether, and prove its (strong) soundness and (strong) completeness.
Dual-Graph-Regularization Constrained Nonnegative Matrix Factorization with Label Discrimination for Data Clustering

NONNEGATIVE matrix factorization (NMF) is an effective technique for dimensionality reduction of high-dimensional data for tasks such as machine learning and data visualization. However, for practical clustering tasks, traditional NMF ignores the manifold information of both the data space and feature space, as well as the discriminative information of the data. In this paper, we propose a semisupervised NMF called dual-graph-regularization-constrained nonnegative matrix factorization with label discrimination (DCNMFLD). DCNMFLD combines dual graph regularization and prior label information as additional constraints, making full use of the intrinsic geometric and discriminative structures of the data, and can efficiently enhance the discriminative and exclusionary nature of clustering and improve the clustering performance. The evaluation of the clustering experimental results on four benchmark datasets demonstrates the effectiveness of our new algorithm.
Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems.
Trends from 30-Year Observations of Downward Solar Irradiance in Thessaloniki, Greece

The shortwave downward solar irradiance (SDR) is an important factor that drives climate processes and energy production and can affect all living organisms. Observations of SDR at different locations around the world with different environmental characteristics have been used to investigate its long-term variability and trends at different time scales. Periods of positive trends are referred to as brightening periods and of negative trends as dimming periods. In this study we have used 30 years of pyranometer data in Thessaloniki, Greece, to investigate the variability of SDR under three types of sky conditions (clear-, cloudy- and all-sky). The clear-sky data were identified by applying a cloud screening algorithm. We have found a positive trend of 0.38%/year for all-sky, ∼0.1%/year for clear-sky, and 0.41%/year for cloudy conditions. The consistency of these trends, their seasonal variability, and the effect of the solar zenith angle have also been investigated. Under all three sky categories, the SDR trend is stronger in winter, with 0.7, 0.4, and 0.76%/year, respectively, for all-, clear-, and cloudy-sky conditions. The next larger seasonal trends are in autumn—0.42 and 0.19%/year, for all and cloudy skies, respectively. The rest of the seasonal trends are significant smaller, close to zero, with a negative values in summer, for clear and cloudy skies. The SDR trend is increasing with increasing solar zenith angle, except under cloudy skies, where the trend is highly variable and close to zero. Finally, we discuss shorter-term variations in SDR anomalies by examining the patterns of the cumulative sums of monthly anomalies from the climatological mean, both before and after removing the long-term trend.