Open source software ecosystems are composed of a variety of stakeholders including but not limited to non-profit organizations, volunteer contributors, users, and corporations. The needs and motivations of these stakeholders are often diverse, unknown, and sometimes even conflicting given the engagement and investment of both volunteers and corporate actors. Given this, it is not clear how open source communities identify and engage with their stakeholders, understand their needs, and hold themselves accountable to those needs. We convened 24 expert scholars and practitioners studying and working with open source software communities for an exploratory workshop discussion on these ideas. The workshop titled "Accountability and Open Source Software Ecosystems" was organized on Oct 14-15 on campus in Carnegie Mellon University, Pittsburgh, PA. The purpose of this in-person workshop was to initiate conversations that explore important and urgent questions related to the role of accountability in open source software ecosystems, and to inspire an exciting research agenda and meaningful stakeholder engagement ideas for practitioners.
Beatrice Biasioli, Chiara Marcolla, Nadir Murru
et al.
The Brakerski-Gentry-Vaikuntanathan (BGV) scheme is one of the most significant fully homomorphic encryption (FHE) schemes. It belongs to a class of FHE schemes whose security is based on the presumed intractability of the Learning with Errors (LWE) problem and its ring variant (RLWE). Such schemes deal with a quantity, called noise, which increases each time a homomorphic operation is performed. Specifically, in order for the scheme to work properly, it is essential that the noise remains below a certain threshold throughout the process. For BGV, this threshold strictly depends on the ciphertext modulus, which is one of the initial parameters whose selection heavily affects both the efficiency and security of the scheme. For an optimal parameter choice, it is crucial to accurately estimate the noise growth, particularly that arising from multiplication, which is the most complex operation. In this work, we propose a novel average-case approach that precisely models noise evolution and guides the selection of initial parameters, improving efficiency while ensuring security. The key innovation of our method lies in accounting for the dependencies among ciphertext errors generated with the same key, and in providing general guidelines for accurate parameter selection that are library-independent.
This study aims to analyze the effect of transformational leadership (TL) on innovation capability (IC), with knowledge management capability (KMC) serving as a mediating variable among micro, small, and medium enterprises (MSMEs) in Surabaya. A quantitative approach with an explanatory research design was employed by distributing questionnaires to 290 MSME actors across various sectors. Data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with SmartPLS software. The findings reveal that TL has a positive and significant effect on IC, confirming that transformational leadership fosters the development of an innovative work culture. Moreover, TL also exerts a positive and significant influence on KMC, indicating that transformational leaders play a crucial role in strengthening knowledge management systems through motivation, inspiration, and collaboration. Furthermore, KMC was found to positively and significantly affect IC, suggesting that knowledge management acts as a key catalyst in enhancing innovation within MSMEs. Another important finding demonstrates that KMC significantly mediates the relationship between TL and IC, implying that the impact of transformational leadership on innovation becomes more effective when facilitated by optimal knowledge management practices. Theoretically, this study contributes to the expansion of the literature on the relationship between TL, KMC, and IC in the MSME context, while practically, it provides valuable implications for MSME owners and managers to integrate transformational leadership with reinforced knowledge management in order to achieve sustainable competitive advantage.
Micro, Small, and Medium Enterprises (MSMEs) play a vital role in supporting economic growth, absorbing labor, and reducing poverty, yet many entrepreneurs still face challenges in financial management, particularly in bookkeeping practices. This community service program was carried out to respond to the limited capacity of ceramic trading entrepreneurs in Pekanbaru, who often manage their businesses informally without proper financial recording, which hampers decision-making, cost calculation, and access to financial support. The purpose of this program was to improve the knowledge and skills of MSME entrepreneurs in preparing simple bookkeeping records adapted to their business characteristics. The method applied was the Participatory Action Research (PAR) approach, involving socialization, lectures, discussions, and mentoring that enabled participants to directly practice recording daily transactions and compiling basic financial reports such as balance sheets and income statements. The results show that participants were able to separate personal and business finances, record transactions more regularly, and recognize the strategic importance of bookkeeping for business sustainability. The drawn conclusion is that bookkeeping training contributes significantly to increasing financial literacy and competitiveness of MSMEs, although long-term mentoring is still required to strengthen consistency and adoption of digital tools. This program contributes practically by empowering local entrepreneurs and theoretically by affirming the relevance of financial literacy in MSME development.
The openness and transparency of Ethereum transaction data make it easy to be exploited by any entities, executing malicious attacks. The sandwich attack manipulates the Automated Market Maker (AMM) mechanism, profiting from manipulating the market price through front or after-running transactions. To identify and prevent sandwich attacks, we propose a cascade classification framework GasTrace. GasTrace analyzes various transaction features to detect malicious accounts, notably through the analysis and modeling of Gas features. In the initial classification, we utilize the Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel to generate the predicted probabilities of accounts, further constructing a detailed transaction network. Subsequently, the behavior features are captured by the Graph Attention Network (GAT) technique in the second classification. Through cascade classification, GasTrace can analyze and classify the sandwich attacks. Our experimental results demonstrate that GasTrace achieves a remarkable detection and generation capability, performing an accuracy of 96.73% and an F1 score of 95.71% for identifying sandwich attack accounts.
Category fluency is a widely studied cognitive phenomenon, yet two conflicting accounts have been proposed as the underlying retrieval mechanism -- an optimal foraging process deliberately searching through memory (Hills et al., 2012) and a random walk sampling from a semantic network (Abbott et al., 2015). Evidence for both accounts has centered around predicting human patch switches, where both existing models of category fluency produce paradoxically identical results. We begin by peeling back the assumptions made by existing models, namely that each named example only depends on the previous example, by (i) adding an additional bias to model the category transition probability directly and (ii) relying on a large language model to predict based on the entire existing sequence. Then, we present evidence towards resolving the disagreement between each account of foraging by reformulating models as sequence generators. To evaluate, we compare generated category fluency runs to a bank of human-written sequences by proposing a metric based on n-gram overlap. We find category switch predictors do not necessarily produce human-like sequences, in fact the additional biases used by the Hills et al. (2012) model are required to improve generation quality, which are later improved by our category modification. Even generating exclusively with an LLM requires an additional global cue to trigger the patch switching behavior during production. Further tests on only the search process on top of the semantic network highlight the importance of deterministic search to replicate human behavior.
Community Service activities are carried out to provide assistance and training on the importance of accounting and financial reports to determine company performance in terms of finance by utilizing current technological advances. The service activity was carried out at the Big Alind catering home industry in Sendangadi Village, Mlati District, Sleman Regency. The reason for the service was due to the lack of competence of the business owner in bookkeeping reports in the business being run. The method used in this activity is the method of mentoring and direct training in preparing financial reports using the BukuWarung application for business owner. Considered successful when business owner are able to understand the differences before and after being given assistance and training. From the results of the service activity, business owner understand the importance of making structured financial reports, by utilizing the BukuWarung financial application and also the understanding of business owner regarding financial reports that will be prepared in the following year.
Cryptocurrencies are rapidly expanding and becoming vital in digital financial markets. However, the rise in cryptocurrency-related illicit activities has led to significant losses for users. To protect the security of these platforms, it is critical to identify illicit accounts effectively. Current detection methods mainly depend on feature engineering or are inadequate to leverage the complex information within cryptocurrency transaction networks, resulting in suboptimal performance. In this paper, we present DIAM, an effective method for detecting illicit accounts in cryptocurrency transaction networks modeled by directed multi-graphs with attributed edges. DIAM first features an Edge2Seq module that captures intrinsic transaction patterns from parallel edges by considering edge attributes and their directed sequences, to generate effective node representations. Then in DIAM, we design a multigraph Discrepancy (MGD) module with a tailored message passing mechanism to capture the discrepant features between normal and illicit nodes over the multigraph topology, assisted by an attention mechanism. DIAM integrates these techniques for end-to-end training to detect illicit accounts from legitimate ones. Extensive experiments, comparing against 15 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently outperforms others in accurately identifying illicit accounts. For example, on a Bitcoin dataset with 20 million nodes and 203 million edges, DIAM attains an F1 score of 96.55%, markedly surpassing the runner-up's score of 83.92%. The code is available at https://github.com/TommyDzh/DIAM.
Purpose: This study aims to determine the influence of elements from the fraud hexagon theory and characteristics of audit committees on detecting financial statement fraud.
Methodology/approach: The study data uses secondary data sourced from annual reports of manufacturing companies in the basic and chemical industry sebsectors listed on the IDX for 2019-2022 period.
Findings: This study resulted in findings that pressure has a positive effect and opportunity has a negative effect on financial statement fraud. Rationalization, capability, arrogance, collusion and two characteristics of audit committee, namely financial expertise and frequency of audit committee meetings has no effect on financial statement fraud.
Practical and Theoretical contribution/Originality: These findings contribute to researchers and business managers in increasing understanding of the factors that lead to fraud through the hexagon fraud model an characteristics of audit committees, so as to reduce frequency and amount of losses due to fraud. Novelty this study is to use the independent variable characteristics of audit committee, namely financial expertise and frequency of meetings on detecting financial statement fraud.
Research Limitation: The independent variables in this study are only 31.6% which affect the detection of financial statement fraud. While the remaining 68.4% is influenced by other variables outside this research model. In addition, this study also cannot be generalized because only one sub-sector of the company is examined.
Accounting. Bookkeeping, Business mathematics. Commercial arithmetic. Including tables, etc.
The Chinese economy has been steadily showing growth and development for decades, which attracts the attention of the largest scientists around the world to the economic model of this country, business practices, accounting and statistics. The author conducted this study using the historical observation methods, comparison, sampling, systematization and generalization. Also, the author studied sources mainly in Chinese and analyzed publicly available publications in Russian and English. The research divided the features of the national economy growth during the Qing Dynasty into two historical periods: before the opium wars and in the post-opium period until the fall of the dynasty itself. The paper has given the main regulations governing the accounting and statistics of economic management, including the Laws of the Qing Dynasty and Regulations on the registration of households. The research has considered the basics of organizing statistics and accounting at the national level during the early and late Qing Dynasty, including the activities of the Ministry of Households, the Bureau of Statistics, the Bureau of Investigation, the Customs Administration and the China Post. The practical significance of the work for Russian specialists lies in the detailed coverage of national methods of accounting and statistics, such as the household registration Qianlong baojia, the system of tax books and acres segmentation, three-legged accounting, four-legged methods and Dragon Doors. The paper presents the main ideas and research results of such prominent Chinese thinkers in accounting and statistics as Gu Yanwu, Hong Liangji, Wei Yuan Wang Shiduo and Peng Zuzhi.
The bayesTFR package for R provides a set of functions to produce probabilistic projections of the total fertility rates (TFR) for all countries, and is widely used, including as part of the basis for the UN's official population projections for all countries. Liu and Raftery (2020) extended the theoretical model by adding a layer that accounts for the past TFR estimation uncertainty. A major update of bayesTFR implements the new extension. Moreover, a new feature of producing annual TFR estimation and projections extends the existing functionality of estimating and projecting for five-year time periods. An additional autoregressive component has been developed in order to account for the larger autocorrelation in the annual version of the model. This article summarizes the updated model, describes the basic steps to generate probabilistic estimation and projections under different settings, compares performance, and provides instructions on how to summarize, visualize and diagnose the model results.
Audit task complexity, as one of the important and effective factors on the auditors' judgment and decision-making, is one of the controversial concepts in the audit field, which, due to its multidimensional nature, has led to many researches in the audit field. Despite the provision of individual indicators to measure audit task complexity in these studies, explaining a multidimensional model to measure of this fundamental concept in auditing is a problem that requires to research. It is expected that the explanation of such an index can lead to a better understanding of this concept and its dimensions and help auditors in planning audit task as well as ways to increase the quality of judgment and decision making. Accordingly, the current research purpose is to explain a model to measure for audit task complexity concept. For this purpose, information related to 128 companies in Tehran Stock Exchange during 2010-2019 was collected and tested through Partial Least Squares Structural Equation Modeling. Findings based on Constructive-Constructive measurement model and second-order Confirmatory Factor analysis showed that twenty-one factors effect on audit task complexity. Also, the results showed that audit task complexity is influenced by three dimensions of input, processing and output complexity. These findings, in addition to explaining the concept of audit task complexity, have helped to understand the effective factors and dimensions of this concept and can be useful in auditor's tasks planning and policy making and provide a more powerful tool to increase audit judgment quality.
This study aimed to investigate the internal and external factors that motivate accounting students to have a career as forensic accountants using Behavioral career decisions theory. Otherwise, this study analyses the motivation differences between students of public universities and private universities students. This study used a survey method by providing a questionnaire. The population in this study were active undergraduate accounting students at Jenderal Sudirman University, Muhammadiyah University of Purwokerto, and Wijayakusuma University. The respondents were 95, and data were analyzed using SPSS.
The results show that internal and external motivation positively and significantly affect forensic accountants' career selection. There is no difference in motivation for choosing a forensic accountant career between accounting students in public universities and private universities in Banyumas Residency. The study results show achievements, recognition of appreciation, salary rewards and individual environmental conditions as motivational factors. With its unique characteristics as a particular field in accounting studies, forensics is one of the essential sciences in accounting. Therefore, accounting educators and academics can consider preparing a suitable forensic course syllabus or curriculum for the undergraduate programme.
Tujuan penelitian ini adalah untuk mengetahui dan memahami praktik pengelolaan Pajak Daerah oleh Dinas Pendapatan daerah Kota Batu. Penelitian ini menggunakan metode penelitian deskriptif dengan pendekatan kualitatif. Teknik pengumpulan data dalam penelitian ini yaitu observasi, dokumentasi dan wawancara. Hasil menunjukkan bahwa dalam praktiknya, Dispenda kerap menemui kenakalan-kenakalan yang dilakukan oleh wajib pajak (WP). Merujuk fakta demikian, Dispenda pun melakukan beberapa upaya, diantaranya: monitoring, sosialisasi, bekerja sama dengan Dishub dan Kejaksaan, memasang tapping box dan melakukan pendataan ulang.
Background: Measurement errors in terms of quantification or classification frequently occur in epidemiologic data and can strongly impact inference. Measurement errors may occur when ascertaining, recording or extracting data. Although the effects of measurement errors can be severe and are well described, simple straight forward general analytic solutions are not readily available for statistical analysis and measurement error is frequently not acknowledged or accounted for. Generally, to account for measurement error requires some data where we can observe the variables once with and once without error, to establish the relationship between the two. Methods: Here we describe a general method accounting for measurement error in outcome and/or predictor variables for the parametric regression setting when there is a validation subsample where variables are measured once with and once without error. The method does not describe and thus does not depend on the particular relation between the variables measured with and without error, and is generally robust to the type of measurement error, for example nondifferential, differential or Berkson errors. Results: Simulation studies show how the method reduces bias compared to models based upon variables measured with error alone and reduces variances compared to models based upon the variables measured without error in the validation subsample alone. Conclusion: The proposed estimator has favorable properties in terms of bias and variance, is easily derived empirically, and is robust to different types of measurement error. This method should be a valuable tool in the analysis of data with measurement error.
Rosina O. Weber, Prateek Goel, Shideh Amiri
et al.
Previous research in interpretable machine learning (IML) and explainable artificial intelligence (XAI) can be broadly categorized as either focusing on seeking interpretability in the agent's model (i.e., IML) or focusing on the context of the user in addition to the model (i.e., XAI). The former can be categorized as feature or instance attribution. Example- or sample-based methods such as those using or inspired by case-based reasoning (CBR) rely on various approaches to select instances that are not necessarily attributing instances responsible for an agent's decision. Furthermore, existing approaches have focused on interpretability and explainability but fall short when it comes to accountability. Inspired in case-based reasoning principles, this paper introduces a pseudo-metric we call Longitudinal distance and its use to attribute instances to a neural network agent's decision that can be potentially used to build accountable CBR agents.
It is frequently stated that the electromagnetic vector potential acquires a fundamental role in quantum physics, whereas classically it only represents a convenient, but by no means necessary, way of representing the electromagnetic field. Here we argue that this is a historical accident due to the fact that the electromagnetic field was discovered before photons, while the electron itself was discovered first as a particle, before it became clear that it must also be treated as a wave and therefore as an excitation of the underlying electron field. We illustrate the fact that the vector potential ought to play a fundamental role classically using the Aharonov-Bohm effect. This effect is considered as the strongest argument for the role the vector potential plays in quantum physics, however, here we offer a fully classical account of it. This is a consequence of the fact that any account, be it classical or quantum, must involve the vector potential in order to preserve the local nature of the Aharonov-Bohm (as well as all the other) phases.
Aleem Khan, Elizabeth Fleming, Noah Schofield
et al.
We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and metadata of their corresponding document streams. We focus on learning an embedding that maps variable-sized samples of user activity -- ranging from single posts to entire months of activity -- to a vector space, where samples by the same author map to nearby points. The approach does not require human-annotated data for training purposes, which allows us to leverage large amounts of social media content. The proposed model outperforms several competitive baselines under a novel evaluation framework modeled after established recognition benchmarks in other domains. Our method achieves high linking accuracy, even with small samples from accounts not seen at training time, a prerequisite for practical applications of the proposed linking framework.
In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models. Previously, neural network designs, such as Multitask Learning, have accounted for variations in emotional expressions due to demographic and contextual factors. However, existing models face a few constraints: 1) they rely on a clear definition of domains (e.g. gender, noise condition, etc.) and the availability of domain labels; 2) they often attempt to learn domain-invariant features while emotion expressions can be domain-specific. In the present study, we propose the Nonparametric Hierarchical Neural Network (NHNN), a lightweight hierarchical neural network model based on Bayesian nonparametric clustering. In comparison to Multitask Learning approaches, the proposed model does not require domain/task labels. In our experiments, the NHNN models generally outperform the models with similar levels of complexity and state-of-the-art models in within-corpus and cross-corpus tests. Through clustering analysis, we show that the NHNN models are able to learn group-specific features and bridge the performance gap between groups.