Reviewerly/exHarmony huggingface dataset:
Relevance of big data to forensic accounting practice and education

Purpose This paper aims to examine the relevance of Big Data to forensic accounting practice and education by gathering opinions from a sample of academics and practitioners in China. Design/methodology/approach The authors conduct a survey of academics and practitioners regarding the desired demand, importance and content of Big Data educational skills and topics for forensic accounting education to effectively respond to challenges and opportunities in the age of Big Data. Findings Results indicate that the demand for and interest in Big Data/data analytics and forensic accounting will continue to increase; Big Data/data analytics and forensic accounting should be integrated into the business curriculum; many of the suggested Big Data topics should be integrated into forensic accounting education; and some attributes and techniques of Big Data are important in improving forensic accounting education and practice. Research limitations/implications Readers should interpret the results with caution because of the sample size (95 academics and 103 practitioners) and responses obtained from academics and practitioners in one country (China) that may not be representative of the global population. Practical implications The results are useful in integrating Big Data topics into the forensic accounting curriculum and in redesigning the forensic accounting courses/programs. Social implications The results have implications for forensic accountants in effectively fulfilling their responsibilities to their profession and society by combating fraud. Originality/value This study provides educational, research and practical implications as Big Data and forensic accounting are advancing.
Segmentation of Heavily Clustered Nuclei from Histopathological Images

Abstract Automated cell nucleus segmentation is the key to gain further insight into cell features and functionality which support computer-aided pathology in early diagnosis of diseases such as breast cancer and brain tumour. Despite considerable advances in automated segmentation, it still remains a challenging task to split heavily clustered nuclei due to intensity variations caused by noise and uneven absorption of stains. To address this problem, we propose a novel method applicable to variety of histopathological images stained for different proteins, with high speed, accuracy and level of automation. Our algorithm is initiated by applying a new locally adaptive thresholding method on watershed regions. Followed by a new splitting technique based on multilevel thresholding and the watershed algorithm to separate clustered nuclei. Finalized by a model-based merging step to eliminate oversegmentation and a model-based correction step to improve segmentation results and eliminate small objects. We have applied our method to three image datasets: breast cancer stained for hematoxylin and eosin (H&amp;E), Drosophila Kc167 cells stained for DNA to label nuclei, and mature neurons stained for NeuN. Evaluated results show our method outperforms the state-of-the-art methods in terms of accuracy, precision, F1-measure, and computational time.
An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification

Covariates are factors that have a debilitating influence on face verification performance. In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance. To study the first problem, we implement five state-of-the-art deep convolutional networks and evaluate them on three challenging covariates datasets. In total, seven covariates are considered: pose (yaw and roll), age, facial hair, gender, indoor/outdoor, occlusion (nose and mouth visibility, and forehead visibility), and skin tone. We first report the performance of each individual network on the overall protocol and use the score-level fusion method to analyze each covariate. Some of the results confirm and extend the findings of previous studies, and others are new findings that were rarely mentioned previously or did not show consistent trends. For the second problem, we demonstrate that with the assistance of gender information, the quality of a precurated noisy large-scale face dataset for face recognition can be further improved. After retraining the face recognition model using the curated data, performance improvement is observed at low false acceptance rates.
The importance of accounting for real-world labelling when predicting software vulnerabilities

Previous work on vulnerability prediction assume that predictive models are trained with respect to perfect labelling information (includes labels from future, as yet undiscovered vulnerabilities). In this paper we present results from a comprehensive empirical study of 1,898 real-world vulnerabilities reported in 74 releases of three security-critical open source systems (Linux Kernel, OpenSSL and Wiresark). Our study investigates the effectiveness of three previously proposed vulnerability prediction approaches, in two settings: with and without the unrealistic labelling assumption. The results reveal that the unrealistic labelling assumption can profoundly mis- lead the scientific conclusions drawn; suggesting highly effective and deployable prediction results vanish when we fully account for realistically available labelling in the experimental methodology. More precisely, MCC mean values of predictive effectiveness drop from 0.77, 0.65 and 0.43 to 0.08, 0.22, 0.10 for Linux Kernel, OpenSSL and Wiresark, respectively. Similar results are also obtained for precision, recall and other assessments of predictive efficacy. The community therefore needs to upgrade experimental and empirical methodology for vulnerability prediction evaluation and development to ensure robust and actionable scientific findings.
Breast Cancer Diagnosis with Transfer Learning and Global Pooling

Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients. In this study, we aim to develop a fully automatic, deep learning-based, method using descriptor features extracted by Deep Convolutional Neural Network (DCNN) models and pooling operation for the classification of hematoxylin and eosin stain (H&E) histological breast cancer images provided as a part of the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology (BACH) Images. Different data augmentation methods are applied to optimize the DCNN performance. We also investigated the efficacy of different stain normalization methods as a pre-processing step. The proposed network architecture using a pre-trained Xception model yields 92.50% average classification accuracy.
Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network

Accurate temporal action proposals play an important role in detecting actions from untrimmed videos. The existing approaches have difficulties in capturing global contextual information and simultaneously localizing actions with different durations. To this end, we propose a Relation-aware pyramid Network (RapNet) to generate highly accurate temporal action proposals. In RapNet, a novel relation-aware module is introduced to exploit bi-directional long-range relations between local features for context distilling. This embedded module enhances the RapNet in terms of its multi-granularity temporal proposal generation ability, given predefined anchor boxes. We further introduce a two-stage adjustment scheme to refine the proposal boundaries and measure their confidence in containing an action with snippet-level actionness. Extensive experiments on the challenging ActivityNet and THUMOS14 benchmarks demonstrate our RapNet generates superior accurate proposals over the existing state-of-the-art methods.
Blind Modulation Classification for Asynchronous OFDM Systems Over Unknown Signal Parameters and Channel Statistics

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. However, till date, only a few works have been reported in the literature for blind MC of orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading environment. In this paper, a blind MC algorithm has been proposed and implemented over National Instruments (NI) testbed setup for linearly modulated signals of OFDM system by using discrete Fourier transform (DFT) and normalized fourth-order cumulant. The proposed MC algorithm works in the presence of synchronization errors, i.e., frequency, timing, and phase offsets and without the prior information about the signal parameters and channel statistics. To nullify the effect of timing offset in the feature extraction process, a statistical average has been taken over OFDM symbols after introducing uniformly distributed random timing offsets in each of the OFDM symbols. In this work, we have classified a more extensive pool of modulation formats for OFDM signal, i.e., binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK (OQPSK), minimum shift keying (MSK), and 16 quadrature amplitude modulation (16-QAM). Classification is performed in two stages. At the first stage, a normalized fourth-order cumulant is used on the DFT of the received OFDM signal to classify OQPSK, MSK, and 16-QAM modulation formats. At the second stage, first we compute the DFT of the square of the received OFDM signal and then a normalized fourth-order cumulant is used to classify BPSK and QPSK modulation formats. The success rate of the proposed MC algorithm is evaluated through analytical and Monte Carlo simulations and compared with existing methods. Finally, the work is validated by providing an experimental setup on NI hardware over an indoor propagation environment.
Uncertainty-Aware Curriculum Learning for Neural Machine Translation

Neural machine translation (NMT) has proven to be facilitated by curriculum learning which presents examples in an easy-to-hard order at different training stages. The keys lie in the assessment of data difficulty and model competence. We propose uncertainty-aware curriculum learning, which is motivated by the intuition that: 1) the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and 2) the end of the decline in model uncertainty indicates the completeness of current training stage. Specifically, we serve cross-entropy of an example as its data difficulty and exploit the variance of distributions over the weights of the network to present the model uncertainty. Extensive experiments on various translation tasks reveal that our approach outperforms the strong baseline and related methods on both translation quality and convergence speed. Quantitative analyses reveal that the proposed strategy offers NMT the ability to automatically govern its learning schedule.
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds

Detecting objects in 3D LiDAR data is a core technology for autonomous driving and other robotics applications. Although LiDAR data is acquired over time, most of the 3D object detection algorithms propose object bounding boxes independently for each frame and neglect the useful information available in the temporal domain. To address this problem, in this paper we propose a sparse LSTM-based multi-frame 3d object detection algorithm. We use a U-Net style 3D sparse convolution network to extract features for each frame’s LiDAR point-cloud. These features are fed to the LSTM module together with the hidden and memory features from last frame to predict the 3d objects in the current frame as well as hidden and memory features that are passed to the next frame. Experiments on the Waymo Open Dataset show that our algorithm outperforms the traditional frame by frame approach by 7.5% mAP@0.7 and other multi-frame approaches by 1.2% while using less memory and computation per frame. To the best of our knowledge, this is the first work to use an LSTM for 3D object detection in sparse point clouds.
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy

Training deep learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of raw data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to two issues. First, the range difference of weights in different deep learning model layers has not been explicitly considered when applying local differential privacy mechanism. Second, the privacy budget explodes due to the high dimensionality of weights in deep learning models and many query iterations of federated learning. In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. It makes the local weights update differentially private by adapting to the varying ranges at different layers of a deep neural network, which introduces a smaller variance of the estimated model weights, especially for deeper models. Moreover, the proposed mechanism bypasses the curse of dimensionality by parameter shuffling aggregation. A series of empirical evaluations on three commonly used datasets in prior differential privacy works, MNIST, Fashion-MNIST and CIFAR-10, demonstrate that our solution can not only achieve superior deep learning performance but also provide a strong privacy guarantee at the same time.
“President Vows to Cut &lt;Taxes&gt; Hair”: Dataset and Analysis of Creative Text Editing for Humorous Headlines

Nabil Hossain, John Krumm, Michael Gamon. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling, 2013; Rezende et al., 2014). These approaches maximize a variational lower bound on the intractable log likelihood of the observed data. Burda et al. (2015) introduced a multi-sample variational bound, IWAE, that is at least as tight as the standard variational lower bound and becomes increasingly tight as the number of samples increases. Counterintuitively, the typical inference network gradient estimator for the IWAE bound performs poorly as the number of samples increases (Rainforth et al., 2018; Le et al., 2018). Roeder et al. (2017) propose an improved gradient estimator, however, are unable to show it is unbiased. We show that it is in fact biased and that the bias can be estimated efficiently with a second application of the reparameterization trick. The doubly reparameterized gradient (DReG) estimator does not suffer as the number of samples increases, resolving the previously raised issues. The same idea can be used to improve many recently introduced training techniques for latent variable models. In particular, we show that this estimator reduces the variance of the IWAE gradient, the reweighted wake-sleep update (RWS) (Bornschein & Bengio, 2014), and the jackknife variational inference (JVI) gradient (Nowozin, 2018). Finally, we show that this computationally efficient, unbiased drop-in gradient estimator translates to improved performance for all three objectives on several modeling tasks.
Research on Overfitting of Deep Learning

Deep learning has been widely used in search engines, data mining, machine learning, natural language processing, multimedia learning, voice recognition, recommendation system, and other related fields. In this paper, a deep neural network based on multilayer perceptron and its optimization algorithm are studied. MNIST handwritten digital datasets were used to verify the reliability of the model and the optimization algorithm. The important correlation between the overfitting and the activation functions, network structures, training epochs, learning rates is verified. The study of overfitting is of great significance to reduce generalization error. This paper proposes an innovative activation function called: modified-sigmoid which is based on the well-known sigmoid function. This activation function can effectively improve the accuracy of the model and inhibit the overfitting problem.
A Knowledge-Based Two-Population Optimization Algorithm for Distributed Energy-Efficient Parallel Machines Scheduling

In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-efficient parallel machines scheduling problem (DEPMSP) has not been studied yet. This article aims to solve DEPMSP by integrating factory assignment and machine assignment into an extended machine assignment to handle the coupled relations of subproblems. A knowledge-based two-population optimization (KTPO) algorithm is proposed to minimize total energy consumption and total tardiness simultaneously. Five properties are derived by analyzing the characteristics of DEPMSP. The population is initialized by using two heuristics based on problem-specific knowledge and a random heuristic. The nondominated sorting genetic algorithm-II and differential evolution perform cooperatively on the population in parallel. Moreover, two knowledge-based local search operators are proposed to enhance the exploitation. Extensive simulation experiments are conducted by comparing KTPO with four algorithms from the literature. The comparative results and statistical analysis demonstrate the effectiveness and advantages of KTPO in solving DEPMSP.
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot—i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community due to the fact that it can generate high-quality responses to human input and self-correct previous mistakes based on subsequent conversations. However, it is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot. In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories. With extensive empirical studies, we demonstrate both the effectiveness and limitations of the current version of ChatGPT. We find that ChatGPT performs well on many tasks favoring reasoning capabilities (e.g., arithmetic reasoning) while it still faces challenges when solving specific tasks such as sequence tagging. We additionally provide in-depth analysis through qualitative case studies.
Recurrent Attention Model for Pedestrian Attribute Recognition

Pedestrian attribute recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset. It is observed that many semantic pedestrian attributes to be recognised tend to show spatial locality and semantic correlations by which they can be grouped while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations and Attention Model’s capability of highlighting the region of interest on feature map, this paper proposes end-to-end Recurrent Convolutional (RC) and Recurrent Attention (RA) models, which are complementary to each other. RC model mines the correlations among different attribute groups with convolutional LSTM unit, while RA model takes advantage of the intra-group spatial locality and inter-group attention correlation to improve the performance of pedestrian attribute recognition. Our RA method combines the Recurrent Learning and Attention Model to highlight the spatial position on feature map and mine the attention correlations among different attribute groups to obtain more precise attention. Extensive empirical evidence shows that our recurrent model frameworks achieve state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.
Are Disentangled Representations Helpful for Abstract Visual Reasoning

A disentangled representation encodes information about the salient factors of variation in the data independently. Although it is often argued that this representational format is useful in learning to solve many real-world down-stream tasks, there is little empirical evidence that supports this claim. In this paper, we conduct a large-scale study that investigates whether disentangled representations are more suitable for abstract reasoning tasks. Using two new tasks similar to Raven's Progressive Matrices, we evaluate the usefulness of the representations learned by 360 state-of-the-art unsupervised disentanglement models. Based on these representations, we train 3600 abstract reasoning models and observe that disentangled representations do in fact lead to better down-stream performance. In particular, they enable quicker learning using fewer samples.
Weakly Supervised Sequence Tagging from Noisy Rules

We propose a framework for training sequence tagging models with weak supervision consisting of multiple heuristic rules of unknown accuracy. In addition to supporting rules that vote on tags in the output sequence, we introduce a new type of weak supervision, called linking rules, that vote on how sequence elements should be grouped into spans with the same tag. These rules are an alternative to candidate span generators that require significantly more human effort. To estimate the accuracies of the rules and combine their conflicting outputs into training data, we introduce a new type of generative model, linked hidden Markov models (linked HMMs), and prove they are generically identifiable (up to a tag permutation) without any observed training labels. We find that linked HMMs provide an average 7 F1 point boost on benchmark named entity recognition tasks versus generative models that assume the tags are i.i.d. Further, neural sequence taggers trained with these structure-aware generative models outperform comparable state-of-the-art approaches to weak supervision by an average of 2.6 F1 points.
An Overview of Image Caption Generation Methods

In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.