Reviewerly/exHarmony huggingface dataset:
The Advent of the Triple Entry Accounting

This chapter looks at the implications of blockchain technology in modern accounting and its possible implications for accounting and audit professionals. After a brief historical introduction about the evolution of financial accounting (the journey from single-entry to double-entry and now, triple-entry accounting), the chapter provides suggestions in terms of using blockchain as an anti-fraud control to help mitigate financial statement, cash larceny, inventory and payroll fraud schemes based on the Association of Fraud Examiners (ACFE) fraud tree model. This chapter also includes a blockchain-focused purchase cycle audit template for internal and external auditors.
Joint Debiased Representation Learning and Imbalanced Data Clustering

One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn representation using deep neural networks and perform clustering by defining a clustering loss on top of embedded features. However, these approaches are sensitive to imbalanced data and out-of-distribution samples. As a consequence, these methods optimize clustering by pushing data close to randomly initialized cluster centers. This is problematic when the number of instances varies largely in different classes or a cluster with few samples has less chance to be assigned a good centroid. To overcome these limitations, we introduce a new unsupervised framework for joint debiased representation learning and image clustering. We simultaneously train two deep learning models, a deep representation network that captures the data distribution, and a deep clustering network that learns embedded features and performs clustering. Specifically, the clustering network and learning representation network both take advantage of our proposed statistics pooling block that represents mean, variance, and cardinality to handle the out-of-distribution samples and class imbalance. Our experiments show that using these representations, one can considerably improve results on imbalanced image clustering across a variety of image datasets. Moreover, the learned representations generalize well when transferred to the out-of-distribution dataset.
Selective Text Augmentation with Word Roles for Low-Resource Text Classification

Data augmentation techniques are widely used in text classification tasks to improve the performance of classifiers, especially in low-resource scenarios. Most previous methods conduct text augmentation without considering the different functionalities of the words in the text, which may generate unsatisfactory samples. Different words may play different roles in text classification, which inspires us to strategically select the proper roles for text augmentation. In this work, we first identify the relationships between the words in a text and the text category from the perspectives of statistical correlation and semantic similarity and then utilize them to divide the words into four roles -- Gold, Venture, Bonus, and Trivial words, which have different functionalities for text classification. Based on these word roles, we present a new augmentation technique called STA (Selective Text Augmentation) where different text-editing operations are selectively applied to words with specific roles. STA can generate diverse and relatively clean samples, while preserving the original core semantics, and is also quite simple to implement. Extensive experiments on 5 benchmark low-resource text classification datasets illustrate that augmented samples produced by STA successfully boost the performance of classification models which significantly outperforms previous non-selective methods, including two large language model-based techniques. Cross-dataset experiments further indicate that STA can help the classifiers generalize better to other datasets than previous methods.
Wide-area monitoring system architectures

Several power utilities have recently designed and implemented advanced wide-area monitoring systems and data processing strategies to enhance grid stability and reliability. These strategies encompass the implementation of situational awareness systems, including disturbance alert, event location triangulation, oscillation detection, early warning systems, data archiving, and other advanced features [1-3].Wide-area monitoring systems will continue to evolve as software, sensing, and communications technology advance and signal processing tools improve. Various forms of wide-area monitoring systems have been developed to give early warning of power system disturbances [4, 5]. In order to integrate data from multiple sensors, specialized data fusion and communication techniques must be integrated into the existing wide area monitoring system (WAMS) architectures. The design methods must incorporate both fault-tolerant strategies and intelligent data fusion techniques to enhance reliability and safety and also to improve the performance of global monitoring systems.This chapter gives a broad overview of various wide-area monitoring architectures, including sensor development and conceptualization, data processing and data fusion, and anomaly detection algorithms. Inspired by recent work on data fusion techniques, advanced WAMS architectures are introduced that represent a combination of measuring, monitoring, and analysis architectures. Critical concepts in multisensor modeling, estimation, and fusion are reviewed.A framework for integrating data from multiple sensors to produce actionable intelligence is identified. Advantages, challenging aspects, and recent advances in the design and implementation of WAMS architectures are reviewed, and areas, where research is needed to advance the use of WAMS data, are highlighted and described.Challenges posed in developing distributed data fusion algorithms are also outlined.
A Brief Study on Object Detection and Tracking

Abstract The detection and tracking of objects in computer vision systems is a critical and challenging subject. Object identification and tracking currently has applications in a wide range of industries, including surveillance, autonomous robot navigation and vehicle navigation because to the increased accessibility of computing power and vast public datasets. When analyzing an image, the object tracking algorithm goal is to divide the region of interest, monitor its motion and location and identify any blobs. Every tracking necessitates an object detection mechanism either in each frame or whenever an object appears for the first time in the video sequence. The availability of high performance processors and low cost-high quality video cameras has enhanced the need of automated object tracking video sequences. Multiple object Identification and tracking in a dynamic environment has recently become focus of computer vision research. In this paper we discuss about the various object detection and tracking algorithms.
Intellectual Property Rights, Non-Market Considerations and Foreign R and D Investments

This study focuses on how businesses can depend upon non-market elements, gain privileged handling from hosting governmental bodies, and safeguard the intellectual property in other states. This study interrogates different non-market elements; one is at the national level, while the other is at the corporate level, i.e., to influence where businesses focus their innovative efforts. It uses the qualitative method to analyse statutes, local and international laws, protocols, conventions, etc. The findings highlight the importance of intellectual property, which is the creation of the mind, innovation or innovative activities. Internationally these are fully protected by law under Intellectual property rights. Previous studies also consider other relevant factors like the political or governmental role in protecting overseas R and D investments by Intellectual property rights (IPR) regulations. Further, it concluded that the laws related to IPR and non-market factors safeguard company invention from piracy and make states more appealing for innovation-based activity.
Creation of Fake Identities on Social Media: An Analysis of Facebook

Facebook is one of the most popular social networking sites, but some call it a "fake book" (Mülle and Schulz, 2019). Hence to explore the phenomenon of fake identities, the study examined how Facebook users construct their identities and how much profile information is phony. The study used a survey methodology, and 647 university students (252 male and 351 female) participated. Facebook has 14 fields of profile data against which respondents have answered on the Likert scale whether they disclose their accurate information. The study found that both male (mean score 1.9)and female (mean score 2.3) Facebook users create fake identities, but women create more fake identities than men.
FedTrust: Towards Building Secure Robust and Trustworthy Moderators for Federated Learning

Most Federated Learning (FL) systems are built upon a strong assumption of trust-clients fully trust the cen-tralized moderator, which might not be feasible in practice. This work aims to mitigate the assumption by using appropriate cryptographic tools. Particularly, we examine various scenarios with different trust demands in FL, and then design the corresponding practical protocols with lightweight cryptographic tools. Three solutions for secure and trustworthy aggregation are proposed with increasing sophistication: (1) a single verifiable moderator, (2) a single secure and verifiable moderator, and (3) multiple secure and verifiable moderators, which can handle adversarial behaviors with different levels. We evaluate the performances of all our proposed protocols on the test accuracy and the training time, showing that our protocols maintain the accuracy with time overhead from 30% to 156% depending on the secure and trustworthy levels. The protocols can be deployed in many practical FL settings with appropriate optimizations.
The Impact of Talent Management Strategies and Entrepreneurial Orientation on Organizational Performance: The Mediating Role of Organizational Learning

The continual changes organizations face in today's inexorable marketplace necessitate constant scanning and analysis of the business environments to retain important resources that contribute to long-term competitive advantage and notable performance. This research examines the direct influence of talent management strategies and entrepreneurial orientation on the organizational performance of public universities in Jordan and their indirect role via organizational learning as a mediator. Data were collected from a random sample of 275 faculty members at the public universities in the south of Jordan using an online questionnaire. The study used the statistical package for social sciences software (SPSS.20) to conduct multiple regression and mediation testing to analyze the research data. The research results confirmed that from the viewpoint of the faculty members, talent management strategies and entrepreneurial orientation significantly impact organizational performance. Additionally, the results indicated that organizational learning mediates the influence of talent management strategies and entrepreneurial orientation on organizational performance. The novelty of this research stems from its focus on an under-researched context in the extant literature on organizational performance, namely in the higher education sector in a developing country in the Arab world. Additionally, this study investigates a potential mechanism through which talent management practices and entrepreneurial orientation could boost organizational performance.
Impacts of Asteroid Mining on Global Equity

With rapid development in technology, asteroid mining may become a reality in the near future. This paper focuses on the possible impacts of asteroid mining on global equity. In order to evaluate the global equity situation, we developed a global equity assessment model with 5 superior indicators and 18 inferior indicators, in which analytic hierarchy process (AHP) and entropy weight method (EWM) are used to obtain the combined weight, and TOPSIS was used to calculate the National Strength Index (NSI). Then Global Equity Index (GEI) is obtained by measuring the dispersion degree of NSI. Hierarchical grouping. Finally, hierarchical cluster analysis is used to determine which countries are capable of asteroids mining, and the changes of NSI and then GEI are obtained through the mutual iteration of the value obtained from minerals and NSI. Our analysis of data from 30 European countries from 1995 to 2020 shows that unregulated asteroid mining increases global inequity, depending on factors such as the total value of minerals and the difficulty of mining. Our study provides theoretical support and policy recommendations for reducing potential inequities caused by asteroid mining.
Hubungan Kualitas Pembelajaran dan Minat Belajar Daring dengan Hasil Belajar PJOK selama Pandemi Covid-19

Several factors that can affect learning outcomes are the quality of learning and interest in learning. During the Covid-19 pandemic, the teaching and learning system underwent changes, namely there was no face-to-face learning or so-called learning from home and online learning was implemented. It is known from interviews with classroom teachers that interest in learning and learning outcomes are low. The objectives of this study are: (1) to determine the relationship between quality and learning outcomes during the Covid-19 pandemic, (2) to determine interest in online learning and learning outcomes during the pandemic, (3) to determine the relationship between quality and interest in online learning and learning outcomes. learn students during the pandemic.This research method is correlation research. The population of this study amounted to 285 students. The research sample amounted to a sample of 156 students by means of cluster random sampling. Data collection methods in this study used interviews, documentation and questionnaires. The research analysis technique uses person correlation and multiple correlation. The results of this study show: (1) there is no relationship between the quality of learning and learning outcomes during the Covid-19 pandemic with an rcount of -0.148 &lt; rtable 0.1572 a significant value of 0.06 &gt; 0.05, (2) there is no relationship between interest in learning online learning outcomes during the Covid-19 pandemic with an rcount value of 0.107 &lt; rtable 0.1572 a significant value of 0.184 &gt; 0.05, (3) there is no relationship between quality and interest in online learning with student learning outcomes during the Covid-19 pandemic with an rcount value -0.145 &lt;rtable0.1572 significant value of 0.92 &gt; 0.05. The conclusion of this study shows that there is no relationship between learning quality and online learning interest with student learning outcomes during the Covid-19 pandemic.
Lessons from <i>The Politics of Ballot Design</i>: A Review Article

WHEN THE 2000 ELECTION REVEALED WEAKNESSES in U.S. election administration, scholars gathered what they knew about the topic. While scholars had not done extensive research on election administration issues, there was some: training election officials,1 punchcard voting machines,2 roll-off,3 straight-party voting,4 the consequences of the Australian ballot,5 and, of course, ballot design.6 Research conducted in the wake of the 2000 election made it starkly clear that because of the hyper-decentralization of elections in the United States, data were not centralized, nor were concepts consistently defined. Even data basic to an election, such as “voter turnout,” was not consistently defined across states or local jurisdictions. Scholars went to work. Elections and election data collection have improved since 2000.7 Scholars and practitioners continue to advance the theoretical and scientific study of election administration, and they are helping improve the day-to-day operations of election administration in the United States. In fact, an interdisciplinary group that includes both scholars and election officials—Election Sciences, Reform, and Administration, or ESRA—has met each summer since 2017. This group's annual conference “brings together election experts from academia, state and local government, non-profits, and research institutes to develop empirical approaches to the study of how law and administrative procedures affect the quality of elections in the United States.”8 Often with guidance from election officials, scholars have examined such important and timely topics as how to operate elections during a pandemic,9 voting rights and disenfranchisement,10 how the public regards elections and their legitimacy,11 and straight-party ballots, as well as other aspects of ballot design.12 Paul Herrnson and colleagues have analyzed the straight-party box on office-block ballots using experimental research (of course, experiments increase confidence in causal inference). Their work is based on usability theory and research: “systems with simple, straightforward, end-to-end design, involving fewer steps, requiring little user memory, giving confirmation of one's actions, and providing system-based help are more effective than systems that have added complexity, require individuals to remember previous actions, do not provide assistance with cognitive tasks, and are inefficient.”13 Election science scholars are still collecting and centralizing data from local jurisdictions (counties and townships) around the nation. One of the latest data points gathered is how much local jurisdictions spend to administer elections.14 While ESRA is a nascent organization, scholars have been publishing important election science research—many times in major journals—for quite some time.
The Effect of Peripheral Contrast Sensitivity Functions on the Performance of the Foveated Wavelet Image Quality Index

The Contrast Sensitivity Function (CSF) is an integral part of objective foveated image/video quality assessment metrics. In this paper, we investigate the effect of a new eccentricity-dependent CSF model on the performance of the foveated wavelet image quality index (FWQI). Our results do not show a considerable change in FWQI performance when it is evaluated against the LIVE-FBT-FCVR 2D dataset. We argue that the resolution of the head-mounted display used in the subjective experiment limits our ability to reveal the anticipated effect of the new CSF on FWQI performance.
Breaking the Limits of Display and Fabrication using Perception-aware Optimizations

Novel display devices and fabrication techniques enable highly tangible ways of creating, experiencing, and interacting with digital content. The capabilities offered by these new output devices, such as virtual and augmented reality head-mounted displays and new multi-material 3D printers, make them real game-changers in many &#xFB01;elds. At the same time, the new possibilities offered by these devices impose many challenges for content creation techniques regarding quality and computational ef&#xFB01;ciency. This paper discusses the concept of perception-aware optimizations, which incorporate insights from human perception into computational methods to optimize content according to the capabilities of different output devices, e.g., displays, 3D printers, and requirements of the human sensory system. As demonstrated in this paper, the key advantage of such strategies is that tailoring computation to perceptually-relevant aspects of the content often reduces the computational cost related to the content creation or overcomes certain limitations of output devices. Besides discussing the general concept, the paper presents several speci&#xFB01;c applications where perception-aware optimization has been proven bene&#xFB01;cial. The examples include methods for optimizing visual content for novel display devices that focus on perceived quality and new computational fabrication techniques for manufacturing objects that look and feel like real ones.
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles

Reinforcement learning (RL) methods have been shown to be capable of learning intelligent behavior in rich domains. However, this has largely been done in simulated domains without adequate focus on the process of building the simulator. In this paper, we consider a setting where we have access to an ensemble of pre-trained and possibly inaccurate simulators (models). We approximate the real environment using a state-dependent linear combination of the ensemble, where the coefficients are determined by the given state features and some unknown parameters. Our proposed algorithm provably learns a near-optimal policy with a sample complexity polynomial in the number of unknown parameters, and incurs no dependence on the size of the state (or action) space. As an extension, we also consider the more challenging problem of model selection, where the state features are unknown and can be chosen from a large candidate set. We provide exponential lower bounds that illustrate the fundamental hardness of this problem, and develop a provably efficient algorithm under additional natural assumptions.
Natural Vocabulary Emerges from Free-Form Annotations

We propose an approach for annotating object classes using free-form text written by undirected and untrained annotators. Free-form labeling is natural for annotators, they intuitively provide very specific and exhaustive labels, and no training stage is necessary. We first collect 729 labels on 15k images using 124 different annotators. Then we automatically enrich the structure of these free-form annotations by discovering a natural vocabulary of 4020 classes within them. This vocabulary represents the natural distribution of objects well and is learned directly from data, instead of being an educated guess done before collecting any labels. Hence, the natural vocabulary emerges from a large mass of free-form annotations. To do so, we (i) map the raw input strings to entities in an ontology of physical objects (which gives them an unambiguous meaning); and (ii) leverage inter-annotator co-occurrences, as well as biases and knowledge specific to individual annotators. Finally, we also automatically extract natural vocabularies of reduced size that have high object coverage while remaining specific. These reduced vocabularies represent the natural distribution of objects much better than commonly used predefined vocabularies. Moreover, they feature more uniform sample distribution over classes.
Exploiting discourse relations to produce Arabic extracts

Text summarisation is one of the interesting tools for a quick and optimal exploitation of the huge amount of online textual documents. Several approaches have been proposed to date to produce extractive summaries in Arabic. However, in most cases, the linguistic qualities of the generated summary are not satisfactory. In this paper, we attempt to overcome this limitation by proposing a new approach for single-document summarisation that combines a discourse analysis following the rhetorical structure theory (RST) framework and a score-based method. Unlike traditional RST-based approaches, the proposed approach relies on exploiting intra-sentence discourse relations instead of text discourse structure to produce a primary summary. Then, each sentence within the primary summary is evaluated based on a combination of statistical and linguistic features to produce the final summary considering user compression rate. The proposed approach was evaluated under Essex Arabic Summaries Corpus (EASC) using ROUGE-1 and ROUGE-2 measures, and compared against other existing methods. A human evaluation was also conducted in order to assess the linguistic qualities of generated summaries. Experimental results are very encouraging and prove that, exploiting discourse relations is very useful to produce Arabic extractive summaries with good linguistic qualities.