Reviewerly/exHarmony huggingface dataset:
The Development of Financial Statement Fraud Research: A Bibliometric Analysis

This research aims to present and explain the development of research related to financial statement fraud by reviewing bibliometric analysis taken from the Scopus database for the period 1979 – 2023. The method used is a bibliometric analysis approach using VOSviewer and Microsoft Excel to analyze data frequency. The results of this research show that the research topic of financial statement fraud will be most researched in 2023 with 60 articles, the journal that publishes the most topics related to financial statement fraud is the Journal of Financial Crime. The country that publishes the most on the topic of financial statement fraud is the United States. Variables that often appear related to the topic of financial statement fraud are corporate governance, earnings management, crime, and the fraud triangle. Brazel, Hermanson, and Jones are the authors who write most frequently or extensively about financial statement fraud.
ANALISIS KEPUASAN PENGGUNA WEBSITE ORLANSOFT MENGGUNAKAN METODE WEBQUAL 4.0

Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536&gt; F table 2.81 and the significance value is 0.0000011 &lt;0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.
A Literature Survey on BIO-PULSE AI based Medcare App

The MedCare Chatbot signifies a significant leap forward in healthcare technology, providing users with individualized medical guidance and prescription suggestions. Going beyond its primary functions, this inventive chatbot incorporates a pioneering feature centered on the identification of skin diseases through image processing techniques. Utilizing the capabilities of artificial intelligence and computer vision, the MedCare Chatbot can analyse images of skin conditions submitted by users, furnishing precise diagnoses and treatment recommendations. This paper details the design, implementation, and assessment of this advanced functionality seamlessly integrated into the MedCare Chatbot. Employing a dual strategy of symptom-based prescription advice and image-based skin disease detection, the MedCare Chatbot aims to enhance healthcare accessibility and empower users to actively manage their well-being
Secure Data Transfer using Video Steganography

Steganography, a wellestablished practice of covering messages inside standard ones, has found restored significance in the mechanized age, encompassing various mediums like pictures, message, sound, and dynamically, accounts. With the climb of mechanized video correspondence worked with by open taking care of programming, video steganography has emerged as a basic space, wanting to embed data cryptically while staying aware of video quality. This study presents a sharp video steganography plot that unequivocally embeds limited data inside moving things perceived through object recognizable proof, utilizing focus repeat sub-gatherings to safeguard visual decency. Through quantitative and emotional evaluations, the proposed plot shows predominant execution to the extent that intangibility and strength against disturbance attacks, outflanking existing methodologies. Likewise, a flexible steganography approach custom fitted for HEVC accounts is proposed to direct bitrate addition and mutilation storing up. This approach utilizes thought net and PU fragment modes, close by Issue Cross section Code (STC) steganography coding and convolutional cerebrum associations, to overhaul the visual quality and bitrate execution. Preliminary outcomes confirm the feasibility of the proposed computation, showing better perceptual quality through cutting-edgeg-edge strategies. In the end, careful testing against steganalysis techniques upholds the security of the proposed contrive, featuring its significance in working with covert correspondence and security affirmation in electronic video settings
Eigenphase distributions of unimodular circular ensembles

Abstract Motivated by the study of Polyakov lines in gauge theories, Hanada and Watanabe Hanada and Watanabe [1] recently presented a conjectured formula for the distribution of eigenphases of Haar-distributed random SU(N) matrices (β = 2), supported by explicit examples at small N and by numerical samplings at larger N. In this letter, I spell out a concise proof of their formula, and present its orthogonal and symplectic counterparts, i.e. the eigenphase distributions of Haar-random unimodular symmetric (β = 1) and selfdual (β = 4) unitary matrices parametrizing SU(N)/SO(N) and SU(2N)/Sp(2N), respectively.
Enhance Your Writing Potential With AI Tools

Nonprofit Communications ReportVolume 22, Issue 3 p. 8-8 Useful Writing Resources Enhance Your Writing Potential With AI Tools First published: 06 February 2024 https://doi.org/10.1002/npcr.32361Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume22, Issue3March 2024Pages 8-8 RelatedInformation
AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a  Single High-Resolution Image

The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, ie Jiangsu, Yosemite, and London; and outperforms the state of the art methods significantly.
IMPLEMENTASI METODE JIGSAW DALAM MENINGKATKAN KETRAMPILAN KOMUNIKASI LISAN SISWA SMALB MATA PELAJARAN PAI DI SLB NEGERI TULUNGAGUNG

Basically, every human being cannot be separated from interacting with other humans. As social creatures, humans always try to build relationships with others. However, some people are limited in communication (have special needs), as we know, students at Special Schools (B-Deaf) are limited in one of their senses, namely hearing, which causes communication to be less than fluent. Student deafness concerns individual hearing conditions, namely the student's inability to perceive verbal communication or other sounds that are adjusted to a certain frequency and intensity. By implementing the jigsaw method in PAI learning, it is hoped that it will be able to provide solutions in oral communication skills. The aim of this research is to find out how the implementation of the jigsaw method improves the oral communication skills of SMALB Tulungagung students. This research uses a qualitative method with a descriptive type. Descriptive research type is used as a procedure to propose problem solving by highlighting the state of the object being studied. Data collection took place from 01 August to 15 October 2023 at the Tulungagung State Special School. The results of the research showed that there was an increase in the oral communication skills of Tulungagung State High School students through the implementation of the jigsaw method in PAI subjects. The weakness of this research is that it does not cover all subjects so this research cannot yet be generalized and needs to be studied by further researchers
Training-Free Consistent Text-to-Image Generation

Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portray the same subject across diverse prompts remains challenging. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects or add image conditioning to the model. These methods require lengthy per-subject optimization or large-scale pre-training. Moreover, they struggle to align generated images with text prompts and face difficulties in portraying multiple subjects. Here, we present ConsiStory, a training-free approach that enables consistent subject generation by sharing the internal activations of the pretrained model. We introduce a subject-driven shared attention block and correspondence-based feature injection to promote subject consistency between images. Additionally, we develop strategies to encourage layout diversity while maintaining subject consistency. We compare ConsiStory to a range of baselines, and demonstrate state-of-the-art performance on subject consistency and text alignment, without requiring a single optimization step. Finally, ConsiStory can naturally extend to multi-subject scenarios, and even enable training-free personalization for common objects.
A new track finding algorithm based on a multi-dimensional extension of the Hough Transform

We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupancy situations. The algorithm needs to be trained using a sufficiently large set of simulated tracks. The same track finding algorithm can be used for very different detector geometries and only the set of simulated tracks used for training needs to be changed. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined with its intrinsic flexibility, should provide a most powerful tool for triggering at future colliders.
Video Super-Resolution for Optimized Bitrate and Green Online Streaming

Conventional per-title encoding schemes strive to optimize encoding resolutions to deliver the utmost perceptual quality for each bitrate ladder representation. Nevertheless, maintaining encoding time within an acceptable threshold is equally imperative in online streaming applications. Furthermore, modern client devices are equipped with the capability for fast deep-learning-based video super-resolution (VSR) techniques, enhancing the perceptual quality of the decoded bitstream. This suggests that opting for lower resolutions in representations during the encoding process can curtail the overall energy consumption without substantially compromising perceptual quality. In this context, this paper introduces a video super-resolution-based latency-aware optimized bitrate encoding scheme (ViSOR) designed for online adaptive streaming applications. ViSOR determines the encoding resolution for each target bitrate, ensuring the highest achievable perceptual quality after VSR within the bound of a maximum acceptable latency. Random forest-based prediction models are trained to predict the perceptual quality after VSR and the encoding time for each resolution using the spatiotemporal features extracted for each video segment. Experimental results show that ViSOR targeting fast super-resolution convolutional neural network (FSRCNN) achieves an overall average bitrate reduction of 24.65 % and 32.70 % to maintain the same PSNR and VMAF, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 4 s segments using the x265 encoder, when the maximum acceptable latency for each representation is set as two seconds. Considering a just noticeable difference (JND) of six VMAF points, the average cumulative storage consumption and encoding energy for each segment is reduced by 79.32 % and 68.21 %, respectively, contributing towards greener streaming.
Influencer Identification on Link Predicted Graphs

How could one identify a potential influencer, or how would admissions look like in a University program for influencers? In the realm of social network analysis, influence maximization and link prediction stand out as pivotal challenges. Influence maximization focuses on identifying a set of key nodes to maximize information dissemination, while link prediction aims to foresee potential connections within the network. Given the complexity of these tasks, especially in large-scale networks, we propose a novel algorithm, The Social Sphere Model, tailored for weighted networks. This algorithm uniquely combines link prediction techniques with influence maximization strategies to effectively identify future vital nodes. Our approach leverages two distinct contagion models, offering a promising solution with lower computational demands. This advancement not only enhances our understanding of network dynamics but also opens new avenues for efficient network management and influence strategy development.
Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites

Posts in software Q\&A sites often consist of three main parts: title, description and code, which are interconnected and jointly describe the question. Existing tag recommendation methods often treat different modalities as a whole or inadequately consider the interaction between different modalities. Additionally, they focus on extracting information directly from the post itself, neglecting the information from external knowledge sources. Therefore, we propose a Retrieval Augmented Cross-Modal (RACM) Tag Recommendation Model in Software Q\&A Sites. Specifically, we first use the input post as a query and enhance the representation of different modalities by retrieving information from external knowledge sources. For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code. In the fusion process, a gate mechanism is employed to achieve fine-grained feature selection, controlling the amount of information extracted from the submodalities. Finally, the fused information is used for tag recommendation. Experimental results on three real-world datasets demonstrate that our model outperforms the state-of-the-art counterparts.
Minds versus Machines: Rethinking Entailment Verification with Language  Models

Humans make numerous inferences in text comprehension to understand discourse. This paper aims to understand the commonalities and disparities in the inference judgments between humans and state-of-the-art Large Language Models (LLMs). Leveraging a comprehensively curated entailment verification benchmark, we evaluate both human and LLM performance across various reasoning categories. Our benchmark includes datasets from three categories (NLI, contextual QA, and rationales) that include multi-sentence premises and different knowledge types, thereby evaluating the inference capabilities in complex reasoning instances. Notably, our findings reveal LLMs' superiority in multi-hop reasoning across extended contexts, while humans excel in tasks necessitating simple deductive reasoning. Leveraging these insights, we introduce a fine-tuned Flan-T5 model that outperforms GPT-3.5 and rivals with GPT-4, offering a robust open-source solution for entailment verification. As a practical application, we showcase the efficacy of our finetuned model in enhancing self-consistency in model-generated explanations, resulting in a 6% performance boost on average across three multiple-choice question-answering datasets.
Automated Description Generation for Software Patches

Software patches are pivotal in refining and evolving codebases, addressing bugs, vulnerabilities, and optimizations. Patch descriptions provide detailed accounts of changes, aiding comprehension and collaboration among developers. However, manual description creation poses challenges in terms of time consumption and variations in quality and detail. In this paper, we propose PATCHEXPLAINER, an approach that addresses these challenges by framing patch description generation as a machine translation task. In PATCHEXPLAINER, we leverage explicit representations of critical elements, historical context, and syntactic conventions. Moreover, the translation model in PATCHEXPLAINER is designed with an awareness of description similarity. Particularly, the model is explicitly trained to recognize and incorporate similarities present in patch descriptions clustered into groups, improving its ability to generate accurate and consistent descriptions across similar patches. The dual objectives maximize similarity and accurately predict affiliating groups. Our experimental results on a large dataset of real-world software patches show that PATCHEXPLAINER consistently outperforms existing methods, with improvements up to 189% in BLEU, 5.7X in Exact Match rate, and 154% in Semantic Similarity, affirming its effectiveness in generating software patch descriptions.
Improving difficult inference tasks and likelihood evaluations with neural networks

A number of simulation-based methods, such as Gillespie's algorithm or stochastic differential equations, provide a way to simulate data generated by a system following a model. By contrast to forward simulation-based strategies, to deduce dynamical laws or parameters from data requires inverse strategies such as Bayesian methods. Likelihoods are a central object of Bayesian modeling whose computation demand significant time and memory resources. To facilitate the task of evaluating likelihoods, these can be approximated using neural networks trained to emulate forward simulations. Here we use such methods for problems of relevance in biophysics from the protein to the cellular scale.
Optimization of a machine-learning-based approach to quantify and characterize microtubule dynamic instability

Microtubules (MTs) are biological polymers that constitute an essential part of the cytoskeleton of eukaryotic cells and are found in all characterized eukaryotic organisms. MTs display “dynamic instability” (DI), a behavior traditionally characterized by stochastic switching between phases of growth and shrinkage separated by transitions called catastrophe and rescue. This behavior requires energy, is intrinsic to the MT subunits (it does not require additional proteins) and is fundamental to MT functions. To understand how MT dynamics are regulated, experimentalists must characterize and quantify DI behavior. Classical approaches assumed that DI has only two phases, with instantaneous transitions between them, but high-resolution experimental data shows that DI is more complicated. To address this problem, we developed the statistical tool for automated dynamic instability Analysis (STADIA), which uses unsupervised machine-learning approaches to characterize and quantify DI behaviors (including the “stutters” that frequently occur before catastrophe) using microtubule length-history data as input (Mahserejian et al., 2022). Our first release of the STADIA software serves as an improvement over traditional DI analysis methods and provides a foundation for further refinement. The purpose of this project is to improve STADIA by refining its methods for segmenting the inputted length-history data and classifying the line segments. We propose to use a change-point detection-based method to improve vertex selection in the segmentation stage and an advanced clustering algorithm to optimize the methodology used for classification. These changes will further refine the classification and quantification of DI, providing a more comprehensive and data-driven approach to analyzing MT dynamics compared to previously developed techniques.
Expanding tagging toolbox for visualizing translation live

Proteins are essential molecules in living cells. Disrupted intracellular protein homeostasis could lead to many diseases, such as cancers and Alzheimer’s disease. Translation, the birth of proteins, is a critical step for maintaining protein homeostasis. Understanding the regulation mechanisms of translation helps us to explore novel therapeutics. Recently, the use of repeat short epitope tags combined with complementary fluorescent antibody fragments, such as the spaghetti monster/Fabs and the SunTag/scFv imaging systems, has made it possible to visualize translation dynamics live at single mRNA level. By applying these tagging systems, many novel mechanisms of translation have been uncovered. However, the lack of comparable and orthogonal genetically encodable tagging systems has made applications that require multicolor imaging challenging. To address this challenge, we developed a panel of orthogonal genetically encodable tagging systems with comparable lengths of epitope tags: the HA tag (9 aa), the FLAG tag (8 aa), and a novel UTag (11 aa). For this, we grafted complementary determining regions or loops of the antibodies to a stable single-chain variable fragment (scFv) scaffold that folds correctly in the reduced intracellular environment. After the loop grafting, the binding of the wild-type scFvs was rescued in living cells. Our FRAP data suggest the binding of these three tagging systems is comparably tight. We believe these favorable properties will make these tagging systems useful tools in multicolor imaging applications.