Reviewerly/exHarmony huggingface dataset:
Copyright law, and the lifecycle of machine learning models

Machine learning, a subfield of artificial intelligence (AI), relies on large corpora of data as input into learning algorithms, resulting in trained models that can perform a variety of tasks. While data or information are not subject matter within copyright law, almost all materials used to construct corpora for machine learning are protected by copyright law: scientific papers, images, videos, and so on. There are global policy moves to address the copyright implications of machine learning, in particular in the context of so-called “foundation models” that underpin generative AI. This paper takes a step back, exploring empirically three technological settings through detailed case studies. We capture the established industry methodology of a lifecycle of AI (collecting data, organising data, model training, model operation) to arrive at descriptions suitable for legal analysis. This will allow an assessment of the challenges for a harmonisation of rights, exceptions and disclosure under EU copyright law.The three case studies are:(1) Machine learning for scientific purposes, in the context of studying regional short-term letting markets;(2) Natural Language Processing (NLP), in the context of large language models;(3) Computer vision, in the context of content moderation of images.We find that the nature and quality of data corpora at the input stage is central to the lifecycle of machine learning. Because of the uncertain legal status of data collection and processing, combined with the competitive advantage for firms not to disclose technological advances, the inputs of deployed models are often unknown. Moreover, the “lawful access” requirement of the EU exception for text and data mining may turn the exception into rightholders’ decision to allow machine learning contextually to their decision to allow access.We assess policy interventions at the EU level, seeking to clarify the legal status of input data via copyright exceptions, opt-outs or forced disclosure of copyright materials. We find that the likely result is a fully copyright licensed environment of machine learning that may have problematic effects for industry structure, innovation and scientific research.
EM-IFCM: Fuzzy c-means clustering algorithm based on edge modification for imbalanced data

The improved fuzzy c-means (IFCM) algorithm is an effective technique for handling the “uniform effect” in imbalanced data clustering; it adjusts the weight of each class based on the fuzzy size between clusters. However, the IFCM algorithm produces a “siphon effect” as the imbalance rate increases. It misclassifies the samples in small classes into large ones. Our analysis shows that this effect occurs because all samples have the same weight value of the same classes, the membership values are polarized, resulting in the model failing to converge to the correct interval. Thus, we propose an imbalanced fuzzy c-means clustering based on edge modification (EM-IFCM) algorithm to alleviate the “siphon effect” of the IFCM algorithm. It exhibits stronger inter-class separability by dynamically adjusting the weight of the samples to enhance the influence of edge samples on the model. In addition, we analyze the effectiveness and complexity of the algorithm and proved its convergence. Finally, we conduct extensive experiments on synthesis, machine-learning, and image-segmentation datasets and compare the results with those of six algorithms. The experimental results show that EM-IFCM has higher accuracy and exhibits an imbalance rate that is at least 1.94 times higher than that of the other algorithms.
Multispectral method for simulating pelophylax esculentus color protection function using Altshuller’s innovation algorithm

The object of the study is the procedure for modeling adaptive strategies for the functioning of the protective coloration of a frog (Pelophylax esculentus) using Altshuller’s innovation algorithm. Verbal modeling by means of the Altshuller’s innovation algorithm using natural language was duplicated by the use of artificial intelligence tools. The systemic aspects of the functioning of the protective coloration of animals are extremely complex. This complexity sometimes creates obstacles of a fundamental nature for their formalized description. We are talking about a formalized description by means of mathematics and computer science. This problem can be solved by using verbal modeling by means of natural language. In particular, the means that are used in the Altshuller’s innovation algorithm. With the use of these means were formulated: the ideal goal of the functioning of the leaking coloration of the animal, the contradiction that impedes the achievement of this goal, the way to resolve this contradiction. As a goal, an adaptive strategy for the functioning of the protective coloration of the animal, which ensures its camouflage and prevents its unmasking, was adopted. The achievement of this goal is hampered by the contradiction in the requirements for the diversity of the protective coloration of the animal. The approach presented in this paper to the study of adaptive strategies for the functioning of the protective coloration of animals is interesting for the development of remote (aerospace) methods for recording aquatic animals.
Using multiple optical cameras for correspondence identification between objects in the fields of view

The study aims to explore a method for identifying corresponding objects across multiple camera views, to improve the accuracy of object re-identification. We analyzed various techniques, including contour detection, region of interest extraction, and keypoint extraction. We also examined the challenges of finding object correspondences between multiple camera views. To evaluate the effectiveness of the proposed method, we utilized two human attribute datasets, Market-1501 and DukeMTMC-reID, and performed extensive testing on these datasets.
MSGL+: Fast and Reliable Model Selection-Inspired Graph Metric Learning

The problem of learning graph-based data structures from data has attracted considerable attention in the past decade. Different types of data can be used to infer the graph structure, such as graphical Lasso, which is learned from multiple graph signals or graph metric learning based on node features. However, most existing methods that use node features to learn the graph face difficulties when the label signals of the data are incomplete. In particular, the pair-wise distance metric learning problem becomes intractable as the dimensionality of the node features increases. To address this challenge, we propose a novel method called MSGL+. MSGL+ is inspired from model selection, leverages recent advancements in graph spectral signal processing (GSP), and offers several key innovations: (1) Polynomial Interpretation: We use a polynomial function of a certain order on the graph Laplacian to represent the inverse covariance matrix of the graph nodes to rigorously formulate an optimization problem. (2) Convex Formulation: We formulate a convex optimization objective with a cone constraint that optimizes the coefficients of the polynomial, which makes our approach efficient. (3) Linear Constraints: We convert the cone constraint of the objective to a set of linear ones to further ensure the efficiency of our method. (4) Optimization Objective: We explore the properties of these linear constraints within the optimization objective, avoiding sub-optimal results by the removal of the box constraints on the optimization variables, and successfully further reduce the number of variables compared to our preliminary work, MSGL. (5) Efficient Solution: We solve the objective using the efficient linear-program-based Frank–Wolfe algorithm. Application examples, including binary classification, multi-class classification, binary image denoising, and time-series analysis, demonstrate that MSGL+ achieves competitive accuracy performance with a significant speed advantage compared to existing graphical Lasso and feature-based graph learning methods.
Stative vs. Eventive Participles in an Arbëresh Variety under the Influence of the Italian Language

In this paper, I explore the properties and the uses of the past participles in the Arbëresh variety of S. Nicola dell’Alto, an Albanian dialect still spoken in Southern Italy, which has been in contact with Italo-Romance varieties for more than five centuries. The data are discussed in comparison to standard Albanian and the contact language, Italian. In Albanian grammar, there is only one type of participle: the past participle. It has both verbal and adjectival properties. As a verbal form, the participle is used in compound and in periphrastic tenses, in combination with both the auxiliaries KAM ‘have’ and JAM ‘be’. It can also be used in combination with other particles to create non-finite verbal forms such as gerund or infinitive or to build up temporal expressions. Finally, it can also be used after some modal impersonal verbs. Verbal participles never show agreement. Albanian participles can also be adjectival. All the adjectives derived by a participial verb take a linking article and always agree with the noun they modify in gender, number, Case and definiteness. The formal distinction of the verbal participles from adjectival participles seems to correlate with the aspectual properties of the construction: a verbal participle appears in eventive structures, whereas an adjectival participle occurs in stative structures. But, as we shall see, this is not always the case. Arbëresh participles have maintained the same morphological and syntactical properties of Albanian. They can be used both in stative and in eventive contexts, but in Arbëresh eventive passives, which are built up as in Italian rather than as in Albanian, the adjectival participles are always inflected. Agreement is obligatory in all the contexts where it is in Italian. This is a clear contact-induced change. The data presented in this paper show that Arbëresh, on the one hand, preserves features of Albanian grammar, whereas, on the other hand, it has undergone changes under the influence of the surrounding Italo-Romance varieties.
A Scoping Review on Analysis of the Barriers and Support Factors of Open Data

Background: Using personal data as Open Data is a pervasive topic globally, spanning various sectors and disciplines. Recent technological advancements, particularly in artificial intelligence and algorithm-driven analysis, have significantly expanded the capacity for the automated analysis of vast datasets. There’s an expectation that Open Data analysis can drive innovation, enhance services, and streamline administrative processes. However, this necessitates a legally and ethically sound framework alongside intelligent technical tools to comprehensively analyze data for societal benefit. Methodology: A systematic review across seven databases (MEDLINE, CINAHL, BASE, LIVIVO, Web of Science, IEEExplore, and ACM) was conducted to assess the current research on barriers, support factors, and options for the anonymized processing of personal data as Open Data. Additionally, a supplementary search was performed in Google Scholar. A total of n=1192 studies were identified, and n=55 met the inclusion criteria through a multi-stage selection process for further analysis. Results: Fourteen potential supporting factors (n=14) and thirteen barriers (n=13) to the provision and anonymization of personal data were identified. These encompassed technical prerequisites as well as institutional, personnel, ethical, and legal considerations. These findings offer insights into existing obstacles and supportive structures within Open Data processes for effective implementation.
Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.
Blockchain Technology, Enterprise Risk and Enterprise Performance

In order to explore the impact of the application of blockchain technology on enterprise performance, as well as the mechanism of enterprise risk and the information disclosure quality on this impact process, and taking the data of A-share listed companies in China’s manufacturing industry from 2015 to 2022 as a research sample, this paper adopts methods such as multi-period difference-in-differences (DID) modeling to conduct an empirical investigation. Findings: The application of blockchain technology can improve enterprise performance. Enterprise risk plays a partial mediating effect, because blockchain technology can reduce enterprise risk and thereby improve enterprise performance. Information disclosure quality has an inhibitory influence on the process by which blockchain technology affects enterprise risk and a facilitating influence on the process by which enterprise risk affects enterprise performance. The results show that manufacturing enterprises with low information disclosure quality can reduce enterprise risk by combining with blockchain technology in production, management, and other aspects, thus improving enterprise performance and promoting sustainable development of enterprise economy.
The Governance of Cosmos Interchain Security

Interchain security (ICS) allows the Cosmos Hub to provide security to other blockchains (‘consumer chains’) and represents a significant revenue model for the Cosmos Hub. This paper investigates the economic and governance aspects of these ICS agreements with a focus on ensuring that the agreements are value adding and robust. The paper identifies potential risks such as vertical integration, challenges in adapting to incomplete contracts, and opportunism in asset-specific investments. It proposes recommendations to enhance the sustainability of ICS relationships, including the establishment of individual governance bodies for each ICS agreement, strategies to manage foreign exchange risks, and a decision tree for the Cosmos Hub to assess new consumer chains. A draft template for consumer chain onboarding is also presented, detailing essential elements like governance, payment terms, and exit clauses. This paper aims to offer actionable insights for improving the governance structures in ICS agreements, thereby fostering robust and enduring interchain security dynamics.
How We Identify Cause-Effect Relationships Given Evidence: An Exploratory Study

For well-informed decision-making and precise predictions, the ability to discern causality is imperative. An innate understanding of cause-and-effect relationships in daily life can be deceptive and lead to incorrect interpretations of correlations between variables. In literature estimates of the human ability to identify causal relationships from factual information remain rare. This paper builds upon Kendall and Charles study of the influence of persuasive false narratives on subjects’ inference within a linear cause-effect schema. Drawing on Oprea research, we extend Kendall and Charles2022 to non-linear patterns and the relationship between accuracy and complexity. In our framework participants face repeated tasks with data generation processes (DGP) of varying structure with 3 variables (Eberhardt2017) that they need to deduce by observing the data. Our findings reveal that even in the simplest case with three variables, the concept of conditional independence poses a significant challenge in correctly identifying cause-and-effect relationships, with accuracy levels only marginally exceeding random guessing (10-25 percent improvement). Moreover, different DGPs exhibit varied accuracy that does not align with standard t-complexity y (Oprea2020), where more connections imply greater complexity. Accuracy in our task is rather determined by the type of data source of the variable (observed or intervening) and how much the relationship or lack thereof matches the rest of the relationships in the DGP.
On symmetric \(2\)-\((70,24,8)\) designs with an automorphism of order \(6\)

In this paper we analyze possible actions of an automorphism of order six on a \(2\)-\((70, 24, 8)\) design, and give a complete classification for the action of the cyclic group of order six \(G= \langle \rho \rangle \cong Z_6 \cong Z_2 \times Z_3\), where \(\rho^3\) fixes exactly \(14\) points (blocks) and \(\rho^2\) fixes \(4\) points (blocks). Up to isomorphism there are \(3718\) such designs. This result significantly increases the number of previously known \(2\)-\((70,24,8)\) designs.
Answering Clean Tech Questions with Large Language Models

The success of the "net zero transition" relies on the acceleration of the clean technology development to increase renewable energy capacity and low-emission solutions, but also to improve energy efficiency and enable carbon capture. Tracking such technologies and their mineral requirements is becoming increasingly important, but has traditionally required expert knowledge. In this paper, we propose a framework using Large Language Models and question-answering tasks to monitor the novelty within the clean tech industry, but also the minerals on which these technologies rely. It demonstrates the benefits of using artificial intelligence, and more specifically NLP techniques, to reconstruct expert knowledge and track rapidly changing markets.
An Empirical Test of Pretrial Signaling: Text Analysis of GitHub Copyright Notices

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correlating propensity between code smells and vulnerabilities in java applications

The ever-advancing world in terms of technology from web 1.0 to web 3.0, the need for designing and developing software applications has increased many folds. The digitalization of everything at a quick pace from including banking applications, mobile gaming etc. has led to the negligence of the part of the software developers which has led to increment in maintainability as well as security issue of the application, namely, code smells and vulnerability respectively. Code smells are the niggardly practices followed while developing a software by the developers or the software engineers, thwacking the rudimentary delineation principles and cynically thwacking delineation idiosyncrasy. Vulnerability is the snag, glitch or blemishes existing in software or operating system allowing the attackers to derelict the security measures. The paper focusses on finding the relationship between the code smells and vulnerability detected using an Eclipse plugin, PMD and correlating them using software metrics and rule-based machine learning approach.
Analyzing the Evolution of Windows Operating Systems: A Critical Review for Advancing Existing Technological Model

Sophisticated operating systems have been made possible by 21st-century technological breakthroughs in our technologically driven world. These operating systems are made to make it easier to manufacture industrial equipment, but as time goes on, their obsolescence will give way to new ones. This journal serves as evidence that the Windows operating system can adapt to the rapid advancements in technology. It adopts and follows the desires of the technology. In summary, the Windows operating system has evolved over time to accommodate new technologies. It greatly aids us and facilitates the work we do.
TokenLink: a secure loyalty point exchange system powered by smart contracts

Loyalty initiatives refer to the rewards offered by a business to customers who make recurring purchases. Traditional loyalty programmes, on the other hand, have numerous disadvantages, including low redemption rates, expired points, high user acquisition costs, and the difficulty of administering multiple loyalty programmes. Several of these issues can be addressed by utilizing blockchain technology. This paper examines the shortcomings of current loyalty programs and the potential applications of blockchain technology to resolve them. In an effort to improve customer benefits and retention rates while making it simpler for businesses to operate their own loyalty programmes, author proposes a blockchain-powered global loyalty network. This platform intends to combine multiple loyalty programs into a single system to facilitate the transmission of loyalty points between users and to promote co-branding among numerous businesses with global locations.
Unlocking Transparency: Harnessing Blockchain for Anti-Money Laundering in Alternative Assets. The Case of Fine Art and Real Estate

Environmental, Social, and Governance (ESG) criteria have emerged as critical indicators for evaluating long-term companies’ resilience and investments’ ethical implications. Anti-money laundering laws and regulations are crucial to good practices in governance, and they are becoming even more essential now that the use of blockchain and cryptocurrencies raises concerns about possible misconduct facilitated by these new technologies. Amongst alternative assets, this paper focuses specifically on real estate and fine art, which are notably vulnerable to money laundering. Firstly, the paper provides an overview of the key characteristics of these industries, which make them attractive to money launderers. Secondly, it investigates why the misuse of blockchain and cryptocurrencies can facilitate criminal activities, analysing the case of Tornado Cash, an Ethereum smart contract mixer. Finally, a different perspective on blockchain is introduced. The technology has a broader scope and applications beyond cryptocurrencies, and it can represent a valuable resource to increase transparency and promote sustainable finance objectives. Key findings are that blockchain technology can be a double-edged sword for governance and sustainable finance but has great potential to benefit society. What is crucial is an interdisciplinary approach and a comprehensive understanding of blockchain and related technologies to help policymakers and regulators shape effective policies and regulations.