Reviewerly/exHarmony huggingface dataset:
Improving estimate at completion (EAC) cost of construction projects using adaptive neuro-fuzzy inference system (ANFIS)

Earned value management (EVM) is a well-known technique for measuring project performance and progress. Owing to the EVM's attitude to simultaneously combine cost and time performance, project performance can be forecasted accurately, and this plays a vital role in the future of the projects. In the current study, the authors employed an adaptive neuro-fuzzy inference system (ANFIS) as a powerful prediction tool to forecast the completion cost of the projects considering the percentage of risk for qualitative variables and comparing it with other types of neural networks. Because the network structure is usually tuned based on the obtained results, a network optimization procedure is applied using a conventional method for estimating the cost-caused project breakdown. The results showed that ANFIS had a suitable performance (MSE = 0.0003), and based on the sensitivity analysis, the earned value is recognized as the most sensitive factor in the project. This study improves the general estimate of the completion formula by considering the uncertain conditions.
ReCiter: An open source, identity-driven, authorship prediction algorithm optimized for academic institutions

Academic institutions need to maintain publication lists for thousands of faculty and other scholars. Automated tools are essential to minimize the need for direct feedback from the scholars themselves who are practically unable to commit necessary effort to keep the data accurate. In relying exclusively on clustering techniques, author disambiguation applications fail to satisfy key use cases of academic institutions. Algorithms can perfectly group together a set of publications authored by a common individual, but, for them to be useful to an academic institution, they need to programmatically and recurrently map articles to thousands of scholars of interest en masse . Consistent with a savvy librarian’s approach for generating a scholar’s list of publications, identity-driven authorship prediction is the process of using information about a scholar to quantify the likelihood that person wrote certain articles. ReCiter is an application that attempts to do exactly that. ReCiter uses institutionally-maintained identity data such as name of department and year of terminal degree to predict which articles a given scholar has authored. To compute the overall score for a given candidate article from PubMed (and, optionally, Scopus), ReCiter uses: up to 12 types of commonly available, identity data; whether other members of a cluster have been accepted or rejected by a user; and the average score of a cluster. In addition, ReCiter provides scoring and qualitative evidence supporting why particular articles are suggested. This context and confidence scoring allows curators to more accurately provide feedback on behalf of scholars. To help users to more efficiently curate publication lists, we used a support vector machine analysis to optimize the scoring of the ReCiter algorithm. In our analysis of a diverse test group of 500 scholars at an academic private medical center, ReCiter correctly predicted 98% of their publications in PubMed.
Mining Feature Relationships in Data

When faced with a new dataset, most practitioners begin by performing exploratory data analysis to discover interesting patterns and characteristics within data. Techniques such as association rule mining are commonly applied to uncover relationships between features (attributes) of the data. However, association rules are primarily designed for use on binary or categorical data, due to their use of rule-based machine learning. A large proportion of real-world data is continuous in nature, and discretisation of such data leads to inaccurate and less informative association rules. In this paper, we propose an alternative approach called feature relationship mining (FRM), which uses a genetic programming approach to automatically discover symbolic relationships between continuous or categorical features in data. To the best of our knowledge, our proposed approach is the first such symbolic approach with the goal of explicitly discovering relationships between features. Empirical testing on a variety of real-world datasets shows the proposed method is able to find high-quality, simple feature relationships which can be easily interpreted and which provide clear and non-trivial insight into data.
XGBoost Based Supply Chain Fraud Detection Model

There are plenty of product data in today's world, and increasing companies pay more attention to using the data effectively. And the application of machine learning models to detect product Fraud has received widespread attention from researchers and enterprises, which could help companies adjust their plans to adapt to the market and increase revenue. In this paper, we hybridize the XGBoost algorithm and Random Forest, and then get an optimized model whose output is the average of the output of the XGBoost model and the Random Forest model. It shows that the Confusion matrixes for XGBoost and Random Forest, the quantity of TP and TF is large, which means the hybrid model performs well. Moreover, I evaluate the hybrid model on the DataCo smart supply chain datasets provided by the Kaggle competition. Experimental results show my method achieves superior performance over the other machine learning approaches. My model's F1 score is 0.49, 0.49, and 27.9 higher than the Logistic regression algorithm, the SVM, and the Gaussian Naive Bayes algorithm respectively.
SRWGANTV: Image Super-Resolution Through Wasserstein Generative Adversarial Networks with Total Variational Regularization

The study of generative adversarial networks (GAN) has enormously promoted the research work on single image super-resolution (SISR) problem. SRGAN firstly apply GAN to SISR reconstruction, which has achieved good results. However, SRGAN sacrifices the fidelity. At the same time, it is well known that the GANs are difficult to train and the improper training fails the SISR results easily. Recently, Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) has been proposed to alleviate these issues at the expense of performance of the model with a relatively simple training process. However, we find that applying WGAN-GP to SISR still suffers from training instability, leading to failure to obtain a good SR result. To address this problem, we present an image super resolution framework base on enhanced WGAN (SRWGAN-TV). We introduce the total variational (TV) regularization term into the loss function of WGAN. The total variational (TV) regularization term can stabilize the network training and improve the quality of generated images. Experimental results on public datasets show that the proposed method achieves superior performance in both quantitative and qualitative measurements.
Interactive Transfer Learning-Assisted Fuzzy Neural Network

Transfer learning algorithm can provide a framework to utilize the previous knowledge to train fuzzy neural network (FNN). However, the performance of TL-based FNN will be destroyed by the knowledge over-fitting problem in the learning process. To solve this problem, an interactive transfer learning (ITL) algorithm, which can alleviate the negative transfer among different domains to improve the learning performance of FNN, is designed and analyzed in this article. This ITL-assisted FNN (ITL-FNN) contains the following advantages. First, a knowledge filter algorithm is developed to reconstruct the knowledge in source scene by balancing the matching accuracy and diversity. Then, the knowledge from source scene can fit the instance of target scene with suitable accuracy. Second, a self-balancing mechanism is designed to balance the driven information between the source and target scenes. Then, the knowledge can be refitted to reduce the useless information. Third, a structural competition algorithm is proposed to adjust the knowledge of FNN. Then, the proposed ITL-FNN can achieve compact structure to improve the generalization performance. Finally, some benchmark problems and industrial applications are provided to demonstrate the merits of ITL-FNN.
Effects of Humor and Bystander Gender on Responses to Antigay Harassment

Individuals perceived to be LGBTQ+ are at risk for harassment. Although bystanders who confront harassers and support targets of harassment may promote inclusivity, the use of humor to express antigay harassment may inhibit prosocial bystander intervention. Non-LGBTQ+ undergraduates (N = 326) were randomly assigned to respond to a scenario in which a male peer disparaged another peer with antigay comments involving either the presence or absence of humor. Results showed that bystanders reported less intent to intervene in the presence of humor than in its absence. Bystander gender moderated perceptions of humorous harassing speech but not intent to intervene. In the presence of humor, men but not women perceived antigay harassment as more amusing; they also perceived the harassing peer more favorably. To promote ally behavior, bystander education may explicitly address critical thinking about the functions and effects of disparaging humor.
Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges

Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. The aim of this paper is to present a qualitative assessment of the morphing attack issue, and the challenges it entails, highlighting both the technological and human aspects of the problem. Here, after the face morphing attack scenario is presented, the paper provides an overview of the relevant bibliography and recent advances towards two central directions. First, the morphing of face images is outlined with a particular focus on the three main steps that are involved in the process, namely, landmark detection, face alignment and blending. Second, the detection of morphing attacks is presented under the prism of the so-called on-line and off-line detection scenarios and whether the proposed techniques employ handcrafted features, using classical methods, or automatically generated features, using deep-learning-based methods. The paper, then, presents the evaluation metrics that are employed in the corresponding bibliography and concludes with a discussion on open challenges that need to be address for further advancing automatic detection of morphing attacks. Despite the progress being made, the general consensus of the research community is that significant effort and resources are needed in the near future for the mitigation of the issue, especially, towards the creation of datasets capturing the full extent of the problem at hand and the availability of reference evaluation procedures for comparing novel automatic attack detection algorithms.
Multiple extended target tracking by truncated JPDA in a clutter environment

Data association is a crucial part of target tracking systems with clutter measurements. In general, its complexity increases sharply with a number of targets and measurements. Recently, high-resolution sensors have given rise to extended target tracking problems and more than one measurements can emerge from each target, making the association problems more complex. In this study, a tractable algorithm based on the Gaussian process measurement model and truncated joint probabilistic data association technique is proposed for multiple extended target tracking in the presence of measurement origin uncertainty. Based on the marginal association probabilities, the calculation amount is effectively reduced by truncating the association events with low probabilities in the shortest path problem. The effectiveness of the proposed algorithm is verified by the test of multiple extended targets tracking and compared with the linear-time joint probabilistic data association as well as the algorithm on random finite sets. Simulation results show that the proposed algorithm can track multiple extended targets accurately, which is significant in high-resolution radar tracking systems.
stabm: Stability Measures for Feature Selection

The R (R Core Team, 2020) package stabm provides functionality for quantifying the similarity of two or more sets.For example, consider the two sets {A, B, C, D} and {A, B, C, E}.Intuitively, these sets are quite similar because their overlap is large compared to the cardinality of the two sets.The R package stabm implements functions to express the similarity of sets by a real valued score.Quantifying the similarity of sets is useful for comparing sets of selected features.But also for many other tasks like similarity analyses of gene sets or text corpora, the R package stabm can be employed.
Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.
A Novel Hierarchical Model in Ensemble Environment for Road Detection Application

As a popular research direction in the field of intelligent transportation, road detection has been extensively concerned by many researchers. However, there are still some key issues in specific applications that need to be further improved, such as the feature processing of road images, the optimal choice of information extraction and detection methods, and the inevitable limitations of detection schemes. In the existing research work, most of the image segmentation algorithms applied to road detection are sensitive to noise data and are prone to generate redundant information or over-segmentation, which makes the computation of segmentation process more complicated. In addition, the algorithm needs to overcome objective factors such as different road conditions and natural environments to ensure certain execution efficiency and segmentation accuracy. In order to improve these issues, we integrate the idea of shallow machine-learning model that clusters first and then classifies in this paper, and a hierarchical multifeature road image segmentation integration framework is proposed. The proposed model has been tested and evaluated on two sets of road datasets based on real scenes and compared with common detection methods, and its effectiveness and accuracy have been verified. Moreover, it demonstrates that the method opens up a new way to enhance the learning and detection capabilities of the model. Most importantly, it has certain potential for application in various practical fields such as intelligent transportation or assisted driving.
Enhancement of Deep Learning in Image Classification Performance Using VGG16 with Swish Activation Function for Breast Cancer Detection

Breast cancer is one of the main reasons for death among women throughout the whole world. The mortality rate of breast cancer is controlled by early detection and diagnosis. In this paper, we proposed a CAD model based on a deep convolutional neural network that helps the radiographer with medical imaging analysis. We have applied a novel modification to the VGG16 model with a Swish activation function for mammogram classification. Firstly, we have cropped and normalized the ROI images from two publicly available mammogram databases called MIAS and DDSM. Secondly, we applied the augmentation method for avoiding the over-fitting problem. After augmentation, the resultant images are combined and train with the proposed CAD model. We have considered the five-fold cross-validation method and the best fine-tuning strategy to represent transfer learning’s importance during the training. Our proposed model experimented with a publicly available dataset called DDSM and achieves better accuracy of 97.15% compared to other existing methods.
Efficient Reinforcement Learning for <i>StarCraft</i> by Abstract Forward Models and Transfer Learning

Injecting human knowledge is an effective way to accelerate reinforcement learning (RL). However, these methods are underexplored. This article presents our discovery that an abstract forward model [thought-game (TG)] combined with transfer learning is an effective way. We take <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StarCraft II</i> as our study environment. With the help of a designed TG, the agent can learn a 99% win-rate on a 64×64 map against the Level-7 built-in AI, using only 1.08 h in a single commercial machine. We also show that the TG method is not as restrictive as it was thought to be. It can work with roughly designed TGs, and can also be useful when the environment changes. Comparing with previous model-based RL, we show TG is more effective. We also present a TG hypothesis that gives the influence of different fidelity levels of TG. For real games that have unequal state and action spaces, we proposed a novel XfrNet of which usefulness is validated while achieving a 90% win-rate against the cheating Level-10 AI. We argue that the TG method might shed light on further studies of efficient RL with human knowledge.
USEVis: Visual analytics of attention-based neural embedding in information retrieval

Neural attention-based encoders, which effectively attend sentence tokens to their associated context without being restricted by long-term distance or dependency, have demonstrated outstanding performance in embedding sentences into meaningful representations (embeddings). The Universal Sentence Encoder (USE) is one of the most well-recognized deep neural network (DNN) based solutions, which is facilitated with an attention-driven transformer architecture and has been pre-trained on a large number of sentences from the Internet. Besides the fact that USE has been widely used in many downstream applications, including information retrieval (IR), interpreting its complicated internal working mechanism remains challenging. In this work, we present a visual analytics solution towards addressing this challenge. Specifically, focused on semantics and syntactics (concepts and relations) that are critical to domain clinical IR, we designed and developed a visual analytics system, i.e., USEVis. The system investigates the power of USE in effectively extracting sentences' semantics and syntactics through exploring and interpreting how linguistic properties are captured by attentions. Furthermore, by thoroughly examining and comparing the inherent patterns of these attentions, we are able to exploit attentions to retrieve sentences/documents that have similar semantics or are closely related to a given clinical problem in IR. By collaborating with domain experts, we demonstrate use cases with inspiring findings to validate the contribution of our work and the effectiveness of our system.
Real-time detectors for digital and physical adversarial inputs to perception systems

Deep neural network (DNN) models have proven to be vulnerable to adversarial digital and physical attacks. In this paper, we propose a novel attack- and dataset-agnostic and real-time detector for both types of adversarial inputs to DNN-based perception systems. In particular, the proposed detector relies on the observation that adversarial images are sensitive to certain label-invariant transformations. Specifically, to determine if an image has been adversarially manipulated, the proposed detector checks if the output of the target classifier on a given input image changes significantly after feeding it a transformed version of the image under investigation. Moreover, we show that the proposed detector is computationally-light both at runtime and design-time which makes it suitable for real-time applications that may also involve large-scale image domains. To highlight this, we demonstrate the efficiency of the proposed detector on ImageNet, a task that is computationally challenging for the majority of relevant defenses, and on physically attacked traffic signs that may be encountered in real-time autonomy applications. Finally, we propose the first adversarial dataset, called AdvNet that includes both clean and physical traffic sign images. Our extensive comparative experiments on the MNIST, CIFAR10, ImageNet, and AdvNet datasets show that VisionGuard outperforms existing defenses in terms of scalability and detection performance. We have also evaluated the proposed detector on field test data obtained on a moving vehicle equipped with a perception-based DNN being under attack.
Beyond Matching: Modeling Two-Sided Multi-Behavioral Sequences for Dynamic Person-Job Fit

Online recruitment aims to match right talents with right jobs (Person-Job Fit, PJF) online by satisfying the preferences of both persons (job seekers) and jobs (recruiters). Recently, some research tried to solve this problem by deep semantic matching of curriculum vitaes and job postings. But those static profiles don't (fully) reflect users' personalized preferences. In addition, most existing preference learning methods are based on users' matching behaviors. However, matching behaviors are sparse due to the nature of PJF and not fine-grained enough to reflect users' dynamic preferences. With going deep into the process of online PJF, we observed abundant auxiliary behaviors generated by both sides before achieving a matching, such as click, invite/apply and chat. To solve the above problems, we propose to collect and utilize these behaviors along the timeline to capture users' dynamic preferences. We design a Dynamic Multi-Key Value Memory Network to capture users' dynamic preferences from their multi-behavioral sequences. Furthermore, a Bilateral Cascade Multi-Task Learning framework is designed to transfer two-sided preferences learned from auxiliary behaviors to the matching task with consideration of their cascade relations. Offline experimental results on two real-world datasets show our method outperforms the state-of-the-art methods.