Reviewerly/exHarmony huggingface dataset:
Enhanced Credit Card Fraud Detection Model Using Machine Learning

The COVID-19 pandemic has limited people’s mobility to a certain extent, making it difficult to purchase goods and services offline, which has led the creation of a culture of increased dependence on online services. One of the crucial issues with using credit cards is fraud, which is a serious challenge in the realm of online transactions. Consequently, there is a huge need to develop the best approach possible to using machine learning in order to prevent almost all fraudulent credit card transactions. This paper studies a total of 66 machine learning models based on two stages of evaluation. A real-world credit card fraud detection dataset of European cardholders is used in each model along with stratified K-fold cross-validation. In the first stage, nine machine learning algorithms are tested to detect fraudulent transactions. The best three algorithms are nominated to be used again in the second stage, with 19 resampling techniques used with each one of the best three algorithms. Out of 330 evaluation metric values that took nearly one month to obtain, the All K-Nearest Neighbors (AllKNN) undersampling technique along with CatBoost (AllKNN-CatBoost) is considered to be the best proposed model. Accordingly, the AllKNN-CatBoost model is compared with related works. The results indicate that the proposed model outperforms previous models with an AUC value of 97.94%, a Recall value of 95.91%, and an F1-Score value of 87.40%.
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds

As camera and LiDAR sensors capture complementary information in autonomous driving, great efforts have been made to conduct semantic segmentation through multi-modality data fusion. However, fusion-based approaches require paired data, i.e., LiDAR point clouds and camera images with strict point-to-pixel mappings, as the inputs in both training and inference stages. It seriously hinders their application in practical scenarios. Thus, in this work, we propose the 2D Priors Assisted Semantic Segmentation (2DPASS) method, a general training scheme, to boost the representation learning on point clouds. The proposed 2DPASS method fully takes advantage of 2D images with rich appearance during training, and then conduct semantic segmentation without strict paired data constraints. In practice, by leveraging an auxiliary modal fusion and multi-scale fusion-to-single knowledge distillation (MSFSKD), 2DPASS acquires richer semantic and structural information from the multi-modal data, which are then distilled to the pure 3D network. As a result, our baseline model shows significant improvement with only point cloud inputs once equipped with the 2DPASS. Specifically, it achieves the state-of-the-arts on two large-scale recognized benchmarks (i.e., SemanticKITTI and NuScenes), i.e., ranking the top-1 in both single and multiple scan(s) competitions of SemanticKITTI.
Nonuniform State Space Reconstruction for Multivariate Chaotic Time Series

State space reconstruction is the foundation of chaotic system modeling. Selection of reconstructed variables is essential to the analysis and prediction of multivariate chaotic time series. As most existing state space reconstruction theorems deal with univariate time series, we have presented a novel nonuniform state space reconstruction method using information criterion for multivariate chaotic time series. We derived a new criterion based on low dimensional approximation of joint mutual information for time delay selection, which can be solved efficiently through the use of an intelligent optimization algorithm with low computation complexity. The embedding dimension is determined by conditional entropy, after which the reconstructed variables have relatively strong independence and low redundancy. The scheme, which integrates nonuniform embedding and feature selection, results in better reconstructions for multivariate chaotic systems. Moreover, the proposed nonuniform state space reconstruction method shows good performance in forecasting benchmark and actual multivariate chaotic time series.
Ripple vs. SWIFT: Transforming Cross Border Remittance Using Blockchain Technology

Blockchain technology is transforming traditional financial systems. Cross-border money remittance industry is in a crossroad being challenged. The traditional SWIFT system is facing new comers like Ripple system which is based on the blockchain distributed ledger technology with its own crypto tokens. This paper conducts a SWOT analysis on both technologies to find out whether the blockchain technology has the potential to transform a traditional industry and how this may be possible. We conclude that Ripple has all of the advantages over SWIFT despite some minor issues. In short-term, SWIFT will still take the lead in the remittance market due to the economy of scale. However, in long-term, emerging technology like Ripple will eventually revolutionize the remittance industry or even other financial systems.
Live functional programming with typed holes

Live programming environments aim to provide programmers (and sometimes audiences) with continuous feedback about a program's dynamic behavior as it is being edited. The problem is that programming languages typically assign dynamic meaning only to programs that are complete, i.e. syntactically well-formed and free of type errors. Consequently, live feedback presented to the programmer exhibits temporal or perceptive gaps. This paper confronts this "gap problem" from type-theoretic first principles by developing a dynamic semantics for incomplete functional programs, starting from the static semantics for incomplete functional programs developed in recent work on Hazelnut. We model incomplete functional programs as expressions with holes, with empty holes standing for missing expressions or types, and non-empty holes operating as membranes around static and dynamic type inconsistencies. Rather than aborting when evaluation encounters any of these holes as in some existing systems, evaluation proceeds around holes, tracking the closure around each hole instance as it flows through the remainder of the program. Editor services can use the information in these hole closures to help the programmer develop and confirm their mental model of the behavior of the complete portions of the program as they decide how to fill the remaining holes. Hole closures also enable a fill-and-resume operation that avoids the need to restart evaluation after edits that amount to hole filling. Formally, the semantics borrows machinery from both gradual type theory (which supplies the basis for handling unfilled type holes) and contextual modal type theory (which supplies a logical basis for hole closures), combining these and developing additional machinery necessary to continue evaluation past holes while maintaining type safety. We have mechanized the metatheory of the core calculus, called Hazelnut Live, using the Agda proof assistant. We have also implemented these ideas into the Hazel programming environment. The implementation inserts holes automatically, following the Hazelnut edit action calculus, to guarantee that every editor state has some (possibly incomplete) type. Taken together with this paper's type safety property, the result is a proof-of-concept live programming environment where rich dynamic feedback is truly available without gaps, i.e. for every reachable editor state.
Adversarial Domain Adaptation for Machine Reading Comprehension

In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain.To this end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where (i) pseudo questions are first generated for unlabeled passages in the target domain, and then (ii) a domain classifier is incorporated into an MRC model to predict which domain a given passage-question pair comes from.The classifier and the passagequestion encoder are jointly trained using adversarial learning to enforce domain-invariant representation learning.Comprehensive evaluations demonstrate that our approach (i) is generalizable to different MRC models and datasets, (ii) can be combined with pre-trained large-scale language models (such as ELMo and BERT), and (iii) can be extended to semisupervised learning.
Analyzing the Robustness of Open-World Machine Learning

When deploying machine learning models in real-world applications, an open-world learning framework is needed to deal with both normal in-distribution inputs and undesired out-of-distribution (OOD) inputs. Open-world learning frameworks include OOD detectors that aim to discard input examples which are not from the same distribution as the training data of machine learning classifiers. However, our understanding of current OOD detectors is limited to the setting of benign OOD data, and an open question is whether they are robust in the presence of adversaries. In this paper, we present the first analysis of the robustness of open-world learning frameworks in the presence of adversaries by introducing and designing øodAdvExamples. Our experimental results show that current OOD detectors can be easily evaded by slightly perturbing benign OOD inputs, revealing a severe limitation of current open-world learning frameworks. Furthermore, we find that øodAdvExamples also pose a strong threat to adversarial training based defense methods in spite of their effectiveness against in-distribution adversarial attacks. To counteract these threats and ensure the trustworthy detection of OOD inputs, we outline a preliminary design for a robust open-world machine learning framework.
Beyond the Single Neuron Convex Barrier for Neural Network Certification

We propose a new parametric framework, called k-ReLU, for computing precise and scalable convex relaxations used to certify neural networks. The key idea is to approximate the output of multiple ReLUs in a layer jointly instead of separately. This joint relaxation captures dependencies between the inputs to different ReLUs in a layer and thus overcomes the convex barrier imposed by the single neuron triangle relaxation and its approximations. The framework is parametric in the number of k ReLUs it considers jointly and can be combined with existing verifiers in order to improve their precision. Our experimental results show that k-ReLU en- ables significantly more precise certification than existing state-of-the-art verifiers while maintaining scalability.
How much Position Information Do Convolutional Neural Networks Encode

In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. Information concerning absolute position is inherently useful, and it is reasonable to assume that deep CNNs may implicitly learn to encode this information if there is a means to do so. In this paper, we test this hypothesis revealing the surprising degree of absolute position information that is encoded in commonly used neural networks. A comprehensive set of experiments show the validity of this hypothesis and shed light on how and where this information is represented while offering clues to where positional information is derived from in deep CNNs.
Countering Extremists on Social Media: Challenges for Strategic Communication and Content Moderation

Extremist exploitation of social media platforms is an important regulatory question for civil society, government, and the private sector. Extremists exploit social media for a range of reasons—from spreading hateful narratives and propaganda to financing, recruitment, and sharing operational information. Policy responses to this question fit under two headings, strategic communication and content moderation. At the center of both of these policy responses is a calculation about how best to limit audience exposure to extremist narratives and maintain the marginality of extremist views, while being conscious of rights to free expression and the appropriateness of restrictions on speech. This special issue on “Countering Extremists on Social Media: Challenges for Strategic Communication and Content Moderation” focuses on one form of strategic communication, countering violent extremism. In this editorial we discuss the background and effectiveness of this approach, and introduce five articles which develop multiple strands of research into responses and solutions to extremist exploitation of social media. We conclude by suggesting an agenda for future research on how multistakeholder initiatives to challenge extremist exploitation of social media are conceived, designed, and implemented, and the challenges these initiatives need to surmount.
Towards Recognizing Unseen Categories in Unseen Domains

Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core challenge of Zero-Shot Learning (ZSL) is to cope with the semantic-shift whereas the main challenge of Domain Adaptation and Domain Generalization (DG) is the domain-shift. While historically ZSL and DG tasks are tackled in isolation, this work develops with the ambitious goal of solving them jointly, i.e. by recognizing unseen visual concepts in unseen domains. We present CuMix (Curriculum Mixup for recognizing unseen categories in unseen domains), a holistic algorithm to tackle ZSL, DG and ZSL+DG. The key idea of CuMix is to simulate the test-time domain and semantic shift using images and features from unseen domains and categories generated by mixing up the multiple source domains and categories available during training. Moreover, a curriculum-based mixing policy is devised to generate increasingly complex training samples. Results on standard ZSL and DG datasets and on ZSL+DG using the DomainNet benchmark demonstrate the effectiveness of our approach.
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines

Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a large variance of the task performance. Previous literature (Devlin et al., 2019; Lee et al., 2020; Dodge et al., 2020) identified two potential reasons for the observed instability: catastrophic forgetting and small size of the fine-tuning datasets. In this paper, we show that both hypotheses fail to explain the fine-tuning instability. We analyze BERT, RoBERTa, and ALBERT, fine-tuned on commonly used datasets from the GLUE benchmark, and show that the observed instability is caused by optimization difficulties that lead to vanishing gradients. Additionally, we show that the remaining variance of the downstream task performance can be attributed to differences in generalization where fine-tuned models with the same training loss exhibit noticeably different test performance. Based on our analysis, we present a simple but strong baseline that makes fine-tuning BERT-based models significantly more stable than the previously proposed approaches.
Automatic Fact-Checking Using Context and Discourse Information

We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information. We address two related tasks: ( i ) detecting check-worthy claims and ( ii ) fact-checking claims. We develop supervised systems based on neural networks, kernel-based support vector machines, and combinations thereof, which make use of rich input representations in terms of discourse cues and contextual features. For the check-worthiness estimation task, we focus on political debates, and we model the target claim in the context of the full intervention of a participant and the previous and following turns in the debate, taking into account contextual meta information. For the fact-checking task, we focus on answer verification in a community forum, and we model the veracity of the answer with respect to the entire question–answer thread in which it occurs as well as with respect to other related posts from the entire forum. We develop annotated datasets for both tasks and we run extensive experimental evaluation, confirming that both types of information—but especially contextual features—play an important role.
Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo and sketch modalities are preserved. However, a fundamental challenge in SBIR has been largely ignored so far, that is, sketches are drawn by humans and considerable style variations exist amongst different users. An effective SBIR model needs to explicitly account for this style diversity, crucially, to generalise to unseen user styles. To this end, a novel style-agnostic SBIR model is proposed. Different from existing models, a cross-modal variational autoencoder (VAE) is employed to explicitly disentangle each sketch into a semantic content part shared with the corresponding photo, and a style part unique to the sketcher. Importantly, to make our model dynamically adaptable to any unseen user styles, we propose to meta-train our cross-modal VAE by adding two style-adaptive components: a set of feature transformation layers to its encoder and a regulariser to the disentangled semantic content latent code. With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic. Extensive experiments show that our style-agnostic model yields state-of-the-art performance for both category-level and instance-level SBIR.
Multi-party Virtual State Channels

Smart contracts are self-executing agreements written in program code and are envisioned to be one of the main applications of blockchain technology. While they are supported by prominent cryptocurrencies such as Ethereum, their further adoption is hindered by fundamental scalability challenges. For instance, in Ethereum contract execution suffers from a latency of more than 15 s, and the total number of contracts that can be executed per second is very limited. State channel networks are one of the core primitives aiming to address these challenges. They form a second layer over the slow and expensive blockchain, thereby enabling instantaneous contract processing at negligible costs. In this work we present the first complete description of a state channel network that exhibits the following key features. First, it supports virtual multi-party state channels, i.e. state channels that can be created and closed without blockchain interaction and that allow contracts with any number of parties. Second, the worst case time complexity of our protocol is constant for arbitrary complex channels. This is in contrast to the existing virtual state channel construction that has worst case time complexity linear in the number of involved parties. In addition to our new construction, we provide a comprehensive model for the modular design and security analysis of our construction.
Measuring the Effects of Data Parallelism on Neural Network Training

Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured by the number of steps necessary to reach a goal out-of-sample error. We study how this relationship varies with the training algorithm, model, and data set, and find extremely large variation between workloads. Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes. We find no evidence that larger batch sizes degrade out-of-sample performance. Finally, we discuss the implications of our results on efforts to train neural networks much faster in the future. Our experimental data is publicly available as a database of 71,638,836 loss measurements taken over the course of training for 168,160 individual models across 35 workloads.