Reviewerly/exHarmony huggingface dataset:
The Impact of Block Chain in Accounting and Auditing Domain - A Critical Approach for Enhanced Efficiency and Transparency

Digital archive management solutions have become increasingly popular in archive management as information technology and wireless technology have advanced. Unlike traditional paper archives, which have inherent distinctiveness and strong tamper-proof modification, electronic files and data are stored in centralized databases, which are more vulnerable to network attacks, data loss, or attempting to steal through malicious software, and are more likely to be crafted and tampered with by internal managers but rather external attackers. Accounting system digitization is still very much in infancy in comparison to other businesses, which have also been severely affected by technological breakthroughs. Some of the explanations may be discovered in the very stringent regulatory standards for authenticity and integrity. The whole accounting system is designed in such a way that counterfeiting is either impossible or prohibitively expensive. This is accomplished through the use of mutual control mechanisms, as well as checks and balances. This will certainly have an impact on day-to-day operations. There is systematic duplication of work, detailed documentation, and periodic controls, among other things. The majority of them that are manual, labor-intensive operations that are far from mechanized. To date, it seems to be the cost of telling the truth As a result, electronic information management is required for its capacity to properly store and retrieve huge amounts of data while ensuring computer system performance integrity.
Shadow Extraction Method Based on Multi-Information Fusion and Discrete Wavelet Transform

Shadow occlusion problem has been a hot research topic in fringe projection profilometry (FPP). Since the shadow area cannot be covered by fringe patterns and is easy to induce error, it should be identified and removed. The key to shadow extraction is to determine an accurate shadow threshold. Additionally, the threshold is expected to be automatically adjusted as the brightness changes. In this paper, we put forward a novel shadow extraction method based on 2D discrete wavelet transform (SE-DWT) to suppress outlier points caused by the shadow area. Meanwhile, the multi-information fusion strategy is presented to improve the anti-interference performance and the accuracy of shadow segmentation. More importantly, unlike the conventional methods that determine the shadow threshold based on the light intensity, we make full of the high frequency property of shadow on the phase map, and convert the problem of shadow segmentation into the problem of frequency analysis. Furthermore, the shadow threshold can be determined by analyzing the discrete shadow area. Finally, we can utilize the shadow threshold to binarize the background image, and obtain a continuous shadow mask. The experimental results demonstrate that the proposed method can dynamically adjust the shadow threshold under different brightness, contrast and positions, and can obtain an accurate shadow mask.
Do Autoencoders Need a Bottleneck for Anomaly Detection?

A common belief in designing deep autoencoders (AEs), a type of unsupervised neural network, is that a bottleneck is required to prevent learning the identity function. Learning the identity function renders the AEs useless for anomaly detection. In this work, we challenge this limiting belief and investigate the value of non-bottlenecked AEs. The bottleneck can be removed in two ways: (1) overparameterising the latent layer, and (2) introducing skip connections. However, limited works have reported on the use of one of the ways. For the first time, we carry out extensive experiments covering various combinations of bottleneck removal schemes, types of AEs and datasets. In addition, we propose the infinitely-wide AEs as an extreme example of non-bottlenecked AEs. Their improvement over the baseline implies learning the identity function is not trivial as previously assumed. Moreover, we find that non-bottlenecked architectures (highest AUROC=0.905) can outperform their bottlenecked counterparts (highest AUROC=0.714) on a recent benchmark of CIFAR (inliers) vs SVHN (anomalies), among other tasks, shedding light on the potential of developing non-bottlenecked AEs for improving anomaly detection.
Metaphors of stopping time in modern literature

The article examines the metaphors of the stopped time, defined as artistic ways of representing the Soviet century in post-Soviet discourse. The metaphor reveals the reflection of the “dying century”, fractally connecting the beginning of the twentieth century and the modern period in the integral semiotic field of artistic historiography (post)soviet era. The purpose of the article is to show the multidimensional nature of the metaphorical interchangeability of the semantic concepts “time is history” and “history is time”. The novelty lies in the study of artistic experimental world projects, in which metaphor fixes the models “time as an illusion” and “time as a body”. The authors consider various images of the “mental chronotope” in which the metaphor of stopped time captures special altered states of consciousness (ideological, cultural and personal) that form the artistic “myth of shaken consciousness” of the twentieth century: for example, Sukhbat Aflatuni — lethargy, Sasha Filipenko — alzheimer and coma, L. Petrushevskaya — a split personality, E. Vodolazkin — a tabula rasa state, etc. The article systematizes such metaphorical forms as time-consciousness, body-time, time-labyrinth, time-fractal, etc. A. Bitov emphasizes a special collective-incorrect time, designated as an exit from the time-labyrinth of the “cult of personality” into the general space of thawing truth. The mythosemantics of the metaphorical image of time-being and time-substance combines imperial time and thanatological parasemantics and becomes the subject of analysis on the material of the novels by Kior Yanev “Southern Mangazeya” and Henri Volokhonsky “Roman the Deceased”.
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality

The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment.However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers.Specific problems arise when dealing with honorifics, particularly translating from English into languages with formality markers.For example, the sentence 'Are you sure?' can be translated in German as 'Sind Sie sich sicher?' (formal register) or 'Bist du dir sicher?' (informal).Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics.This work addresses the problem of learning to control target language attributes, in this case formality, from a small amount of labeled contrastive data.We introduce an annotated dataset (CoCoA-MT) and an associated evaluation metric for training and evaluating formality-controlled MT models for six diverse target languages.We show that we can train formality-controlled models by fine-tuning on labeled contrastive data, achieving high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality.
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition

Distantly-supervised named entity recognition (NER) locates and classifies entities using only knowledge bases and unlabeled corpus to mitigate the reliance on human-annotated labels.The distantly annotated data suffer from the noise in labels, and previous works on DSNER have proved the importance of pre-refining distant labels with hand-crafted rules and extra existing semantic information.In this work, we explore the way to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing these annotations in contrastive learning frameworks.the proposed distant label refinement model can give modified suggestions on distant data without additional supervised labels, and thus reduces the requirement on the quality of the knowledge bases.We perform extensive experiments and observe that recent and state-of-the-art DSNER methods gain evident benefits with our method.
A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation

It is difficult for non-autoregressive translation (NAT) models to capture the multi-modal distribution of target translations due to their conditional independence assumption, which is known as the "multi-modality problem", including the lexical multi-modality and the syntactic multi-modality.While the first one has been well studied, the syntactic multi-modality brings severe challenge to the standard cross entropy (XE) loss in NAT and is under studied.In this paper, we conduct a systematic study on the syntactic multi-modality problem.Specifically, we decompose it into short-and longrange syntactic multi-modalities and evaluate several recent NAT algorithms with advanced loss functions on both carefully designed synthesized datasets and real datasets.We find that the Connectionist Temporal Classification (CTC) loss and the Order-Agnostic Cross Entropy (OAXE) loss can better handle short-and long-range syntactic multi-modalities respectively.Furthermore, we take the best of both and design a new loss function to better handle the complicated syntactic multi-modality in real-world datasets.To facilitate practical usage, we provide a guide to use different loss functions for different kinds of syntactic multimodality.
Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

Responsive teaching is a highly effective strategy that promotes student learning.In math classrooms, teachers might funnel students towards a normative answer or focus students to reflect on their own thinking, deepening their understanding of math concepts.When teachers focus, they treat students' contributions as resources for collective sensemaking, and thereby significantly improve students' achievement and confidence in mathematics.We propose the task of computationally detecting funneling and focusing questions in classroom discourse.We do so by creating and releasing an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither.We introduce supervised and unsupervised approaches to differentiating these questions.Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of .76 with human expert labels and with positive educational outcomes, including math instruction quality and student achievement, showing the model's potential for use in automated teacher feedback tools.Our unsupervised measures show significant but weaker correlations with human labels and outcomes, and they highlight interesting linguistic patterns of funneling and focusing questions.The high performance of the supervised measure indicates its promise for supporting teachers in their instruction. 1
Application of intelligent grammar error correction system following deep learning algorithm in English teaching

The purpose is to explore the application of deep learning technology in the English grammar error correction algorithm. An English grammar error correction model based on seq2seq (Sequence to Sequence) is established, and then its ability is improved applied to English teaching. In addition, the advanced edge calculation in recent years is used to improve the training efficiency and error-correction ability of the model. The TensorFlow framework is used to implement the model, which verifies the effectiveness of the error-correction algorithm model and greatly improves the accuracy of the error-correction system. The results show that the application of artificial intelligence in grammar error correction has gradually attracted the attention of relevant researchers. The edge computing model performs better than the seq2seq model in a variety of data types, and the accuracy in spelling error correction is improved by nearly 44.21%.
Real Time Human Activity Recognition from Accelerometer Data using Convolutional Neural Networks

The study of human regular tasks have become more prevalent and accessible as a result of the widespread use of different sensors integrated into mobile devices. This issue now exists in different vast real-world area applications some examples are “healthcare monitoring, fitness tracking, and user-adaptive systems”. Where a generic model capable of recognizing an arbitrary user’s activity in real-time is necessary. This paper presents a user-independent deep learning technique for digital human activity categorization. The proposed research study advocates the utilization of CNN in combination with basic statistical characteristics to maintain the information regarding the global shape of different time series for performing a local feature extraction. This research study also focuses on how the duration of a time series affects recognition accuracy ans restricting it to a single second to allow time series analysis for performing activity classification.
Random forest rock type classification with integration of geochemical and photographic data

Systematic manual and algorithmic classification workflows to characterize rock types are increasingly applied in the mineral exploration and mining industry, leveraging large systematically collected datasets. The aim of these are robust and repeatable classifications to aid more traditional visual logging practices. This study uses random forest algorithms to examine the impacts of integrating distinct datasets with complementary characteristics; chemistry to enable compositional distinctions, and photography to enable textural distinctions. We use a random forest classifier to examine the accuracy metrics of models producing rock type classifications using these two data types independently and integrated together. Prediction accuracy, measured using 10-fold cross validation, was 87% for geochemical-only inputs, 85% for photographic-only inputs, and 90% for mixed inputs from both datasets. A mining and exploration project in the Late Miocene to early Pliocene porphyry belt in Chile is the site of this case study, where datasets were systematically acquired using in-field methods on historical drill-cores. Results indicate that classification of lithology is improved by integration of photography-based and composition-based feature inputs. We infer that the benefits of integration would increase in proportion with increasing compositional similarity between rock types. This approach might also be applied to similar geological problems, such as alteration or metallurgical classifications; and with somewhat distinct datatypes, such as geochemical interval data and photographic metric extraction from coincident intervals in core photos.
GAFL: Global adaptive filtering layer for computer vision

We devise a universal global adaptive filtering layer, GAFL, capable of “learning” optimal frequency filter for each image in a dataset together with the weights of the base neural network that performs some computer vision task. The proposed approach takes the source image in the spatial domain, selects the best frequencies in the Fourier domain for the benefit of the global task, and prepends the inverse-transform image to the main neural network for a joint training. Remarkably, such a simple add-on layer, capable of optimizing the frequency content of an input for a specific task, dramatically improves the performance of the main network regardless of its design. We observe that the light networks gain a noticeable boost in the performance metrics; whereas, the training of the heavy ones converges faster when GAFL is prepended to the main architecture. We showcase the performance of the layer in four classical computer vision tasks: classification, segmentation, denoising, and erasing, considering popular natural and medical data benchmarks.
An Extremely Effective Spatial Pyramid and Pixel Shuffle Upsampling Decoder for Multiscale Monocular Depth Estimation

To estimate the accurate depth from a single image, we proposed a novel and effective depth estimation architecture to solve the problem of missing and blurred contours of small objects in the depth map. The architecture consists of Extremely Effective Spatial Pyramid modules (EESP) and Pixel Shuffle upsampling Decoders (PSD). The results of this study show that multilevel information and the upsampling method in the decoders are essential for recovering the accurate depth map. Through the model we proposed, competitive performance compared with state-of-the-art methods in terms of reconstruction of object boundaries and the detection rate of small objects has been demonstrated. Our approach has wide applications in higher-level visual tasks, including 3D reconstruction and autonomous driving.
Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

The support vector machine theory has been developed into a very mature system at present. The original support vector machine to solve the optimization problem is transformed into a direct calculation formula of line in this paper and the model is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>o</mi> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mi>n</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </mfenced> </math> time complexity. In the model of this article, weited theory, multiclassification problem and online learning have all become the direct inference, and we have applied the new model to the UCI data set. We hope that in the future, this model will be useful in real-world problems such as stock forecasting, which require nonlinear hi-speed algorithms.
Stag hunt game-based approach for cooperative UAVs

Stag hunt game-based approach for cooperative UAVs Lanh Van Nguyen, Ignacio Torres Herrera, Trung Hoang Le, Duong Manh Phung, Ricardo Patricio Aguilera and Quang Phuc Ha Pages 367-374 (2022 Proceedings of the 39th ISARC, Bogotá, Colombia, ISBN 978-952-69524-2-0, ISSN 2413-5844) Abstract: Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology has found applications in nu-merous construction project phases, ranging from site map-ping, progress monitoring, building inspection, damage as-sessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and eÿciency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant equilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algo-rithm in generating feasible and eÿcient flight paths for UAV formation during the inspection task. Keywords: Unmanned aerial vehicle; Cooperative path planning; Stag hunt game; Payoff-dominant equilibrium; Particle swarm optimization DOI: https://doi.org/10.22260/ISARC2022/0051 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Hybrid Sampling-Based Particle Filtering With Temporal Constraints

This article presents the state estimation problem of nonlinear dynamic stochastic systems with temporal constraints, depicting the nonlinear interval relationship between states at two successive time instants for the first time. To this end, a hybrid sampling-based particle filter (HSPF) with temporal constraints is proposed by integrating the acceptance–rejection sampling, the repeat sampling, and the sample-to-sample sampling via online optimization, where a decision criterion of improving sampling efficiency is designed to determine whether or not the repeat sampling is activated and a simple sequential quadratic programming (SSQP) is derived to mitigate the computational burden of particle optimizations. Next, compared with filters without introducing temporal constraints, we find that the number of effective particles increases, and the differential entropy of the probability density function as a measure of uncertainty is small, implying that fusing more extra information will help to improve the accuracy of estimates. Finally, two simulation scenarios verify the performance of the proposed filter with temporal constraints.
Automatic Measurement of Digital Cameras’ Exposure Time Using Equivalent Sampling

This article presents a novel method to measure the exposure time of digital cameras. The measurement relies on a sequence of images taken by the camera in video mode, using the effect of equivalent sampling. The hardware requirements of the proposed method are low: a signal generator drives an LED source and the blinking LED is recorded by the camera under test. The exposure time is determined from the recorded images and the frequency of the generator. The detailed error analysis of the proposed method is provided, and its performance is validated by real measurements. The measurements indicate that the proposed solution is able to provide estimates with uncertainties in the low microsecond range.
Example Guided Synthesis of Linear Approximations for Neural Network Verification

Abstract Linear approximations of nonlinear functions have a wide range of applications such as rigorous global optimization and, recently, verification problems involving neural networks. In the latter case, a linear approximation must be hand-crafted for the neural network’s activation functions. This hand-crafting is tedious, potentially error-prone, and requires an expert to prove the soundness of the linear approximation. Such a limitation is at odds with the rapidly advancing deep learning field – current verification tools either lack the necessary linear approximation, or perform poorly on neural networks with state-of-the-art activation functions. In this work, we consider the problem of automatically synthesizing sound linear approximations for a given neural network activation function. Our approach is example-guided : we develop a procedure to generate examples, and then we leverage machine learning techniques to learn a (static) function that outputs linear approximations. However, since the machine learning techniques we employ do not come with formal guarantees, the resulting synthesized function may produce linear approximations with violations. To remedy this, we bound the maximum violation using rigorous global optimization techniques, and then adjust the synthesized linear approximation accordingly to ensure soundness. We evaluate our approach on several neural network verification tasks. Our evaluation shows that the automatically synthesized linear approximations greatly improve the accuracy (i.e., in terms of the number of verification problems solved) compared to hand-crafted linear approximations in state-of-the-art neural network verification tools. An artifact with our code and experimental scripts is available at: https://zenodo.org/record/6525186#.Yp51L9LMIzM . "Image missing"<!-- image only, no MathML or LaTex -->"Image missing"<!-- image only, no MathML or LaTex -->