Reviewerly/exHarmony huggingface dataset:
Enhancing egocentric 3D pose estimation with third person views

We propose a novel approach to enhance the 3D body pose estimation of a person computed from videos captured from a single wearable camera. The main technical contribution consists of leveraging high-level features linking first- and third-views in a joint embedding space. To learn such embedding space we introduce First2Third-Pose, a new paired synchronized dataset of nearly 2000 videos depicting human activities captured from both first- and third-view perspectives. We explicitly consider spatial- and motion-domain features, combined using a semi-Siamese architecture trained in a self-supervised fashion. Experimental results demonstrate that the joint multi-view embedded space learned with our dataset is useful to extract discriminatory features from arbitrary single-view egocentric videos, with no need to perform any sort of domain adaptation or knowledge of camera parameters. An extensive evaluation demonstrates that we achieve significant improvement in egocentric 3D body pose estimation performance on two unconstrained datasets, over three supervised state-of-the-art approaches. The collected dataset and pre-trained model are available for research purposes.1
Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network

Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning.
PQuAD: A Persian question answering dataset

We present the Persian Question Answering Dataset (PQuAD), a crowdsourced reading comprehension dataset on Persian Wikipedia articles. It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine various properties of the dataset to show the diversity and the level of its difficulty as a MRC benchmark. By releasing this dataset, we aim to ease research on Persian reading comprehension and the development of Persian question answering systems. Our experiments on different state-of-the-art pre-trained contextualized language models show 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.
Fundamental Study of Adversarial Examples Created by Fault Injection Attack on Image Sensor Interface

Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been proposed as an attack method on image classification systems using deep neural networks (DNNs). Physical AEs created by attaching stickers to traffic signs have been reported, which are a threat against the traffic-sign-recognition DNNs used in advanced driver assistance systems (ADAS). We previously proposed an attack method that generates a noise area on images by superimposing an electrical signal on the mobile industry processor interface (MIPI) and showed that it can generate a single adversarial mark that triggers a backdoor attack on the input image. As the advanced approach, we propose the targeted misclassification attack method on DNN by the AEs which are generated by small perturbations to various places on the image by the fault injection. The perturbation position for AEs is precalculated in advance against the target traffic-sign image, which will be captured on future driving. The perturbation image (5.2-5.5% area is tampered with) is successfully created by the fault injection attack on MIPI, which is connected to Raspberry Pi. As the experimental results, we confirmed that the traffic-sign-recognition DNN on a Raspberry Pi was successfully misclassified when the target traffic sign was captured.
Characteristics of Blockchain and its Relationship with Trust

Trust is the concept that lies amidst the rhetoric on empowering users with blockchain. Bitcoin's eminence hyped as “trust-free,” poignantly made trust an indispensable central element associated with the complexities and uncertainties of online transactions abound in the use and development of blockchain - leaving many users unacquainted with the use of it. Studies focusing on assessing the trustworthiness of the trustee using various characteristics are scant in this context. Mapping the characteristics which constitute the basis of the formation of trust from the viewpoint of the trustor, this study explores the characteristics of blockchain. Semi-structured interviews were conducted with 14 individuals (aged 20–40 years, from the technical and/or business prospect), using blockchain. Three overlapping procedures; data reduction; data display; and conclusion drawing and verification, were used to identify categories and themes. The findings show that the characteristics of blockchain which influence trust were Reliability, Integrity, Tamper-proofing & Immutability, Versatility, Transparency and Privacy. The blockchain is a functionally versatile, tamper-proof, immutable, asset database on a decentralised network. Its reliable technical features that ensure data integrity, and provide a transparent privacy protected environment, have meant that users are willing to trust it, to generate value.
Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks

The undeniable computational power of artificial neural networks has granted the scientific community the ability to exploit the available data in ways previously inconceivable. However, deep neural networks require an overwhelming quantity of data in order to interpret the underlying connections between them, and therefore, be able to complete the specific task that they have been assigned to. Feeding a deep neural network with vast amounts of data usually ensures efficiency, but may, however, harm the network's ability to generalize. To tackle this, numerous regularization techniques have been proposed, with dropout being one of the most dominant. This paper proposes a selective gradient dropout method, which, instead of relying on dropping random weights, learns to freeze the training process of specific connections, thereby increasing the overall network's sparsity in an adaptive manner, by driving it to utilize more salient weights. The experimental results show that the produced sparse network outperforms the baseline on numerous image classification datasets, and additionally, the yielded results occurred after significantly less training epochs.
ECONOMIC FREEDOM, ENTREPRENEURSHIP AND ECONOMIC GROWTH: EVIDENCE FROM PANEL DATA

The existing literature is filled with mixed findings on the effect of entrepreneurship on economic growth. In particular, previous literature treats the effect of entrepreneurship on economic growth in isolation, while we emphasize the institutional environment, and especially, economic freedom. Using data from the Global Entrepreneurship Monitor, we re-explore the relationship between entrepreneurship and growth and attempt to clarify the mixed findings. The systemic link between economic freedom, entrepreneurship and economic growth is investigated by examining a panel of 54 countries (2008-2018). The results validate that overall economic freedom and its subcomponents, such as the rule of law, limited government size, regulatory efficiency and market openness promote the positive effects of entrepreneurial activity on economic growth. The results suggest that economic freedom not only directs individual efforts to entrepreneurial activity, but also affects the extent to which entrepreneurial activity encourages higher growth rates. The findings contradict policy recommendations that some measures of economic freedom are not significantly associated with entrepreneurship.
Online language education recommendation based on personalized learning and edge computing

The rapid development of the Internet has promoted the emergence of a large number of learning resources and online education platforms. However, massive resources and learning approaches have also led to the emergence of the information overload dilemma. This paper combines recommendation system and edge computing to implement personalized learning in online language education. First, the language learning resources are disposed from cloud centralized framework to edge‐cloud based framework. Second, the frequent language resources are selected by using a waterfall hybrid recommendation algorithm in which the language resources are roughly filtered by a collaborative filtering and then the roughly filtered resources are further processed by a content based recommendation algorithm to sort according to the similarity score. The experimental results demonstrate the effectiveness of the proposed personalized learning strategy.
Thermalization and disentanglement with a nonlinear Schrödinger equation

Abstract We study a recently proposed modified Schrödinger equation having an added nonlinear term. For the case where a stochastic term is added to the Hamiltonian, the fluctuating response is found to resemble the process of thermalization. Disentanglement induced by the added nonlinear term is explored for a system made of two coupled spins. A butterfly-like effect is found near fully entangled states of the spin–spin system. A limit cycle solution is found when one of the spins is externally driven.
Emotion Recognition on StackOverflow Posts Using BERT

Social programming websites like GitHub and StackOverflow have become an increasingly important aspect of software development and the publicly available datasets provide a rich source of data for exploring challenging NLP problems. One such problem is emotion recognition. This work applies deep NLP methods for detecting emotions in StackOverflow content. Several BERT models were trained and fine-tuned on a small, sparse, hand-labeled and highly-imbalanced dataset of Stack-Overflow comments. Text augmentation techniques were used to balance the data and the model’s vocabulary was enhanced with common domain-specific terms and emoticons. Unsupervised post-training was applied on a large unlabeled StackOverflow dataset to learn representations for added vocabulary before fine-tuning on labeled data. The final model was benchmarked and compared to prior studies on the same dataset.
VWC-BERT: Scaling Vulnerability–Weakness–Exploit Mapping on Modern AI Accelerators

Defending cybersystems needs accurate mapping of software and hardware vulnerabilities to generalized descriptions of weaknesses, and weaknesses to exploits. These mappings enable cyber defenders to build plans for effective defense and assessment of potential risks to a cybersystem. With close to 200k vulnerabilities, manual mapping is not a feasible option. However, automated mapping is challenging due to limited training data, computational intractability, and limitations in computational natural language processing. Tools based on breakthroughs in Transformer-based language models have been demonstrated to classify vulnerabilities with high accuracy. We make three key contributions in this paper: (1) We present a new framework, VWC-BERT, that augments the Transformer-based hierarchical multi-class classification framework of Das et al. (V2W-BERT) with the ability to map weaknesses to exploits. (2) We implement VWC-BERT on modern AI accelerator platforms using two data parallel techniques for the pre-training phase and demonstrate nearly linear speedups across NVIDIA accelerator platforms. We observe nearly linear speedups for up to 16 V100 and 8 A100 GPUs, and about 3.4× speedup for A100 relative to V100 GPUs. Enabled by scaling, we also demonstrate higher accuracy using a larger language model, RoBERTa-Large. We show up to 87% accuracy for strict and up to 98% accuracy for relaxed classification. (3) We develop a novel parallel link manager for the link prediction phase and demonstrate up to 21× speedup with 16 V100 GPUs relative to one V100 GPU, and thus reducing the runtime from 2.5 hours to 10 minutes. We believe that generalizability and scalability of VWC-BERT will benefit both the theoretical development and practical deployment of novel cyberdefense solutions and vulnerability classification.
Multimodal Road Sign Interpretation for Autonomous Vehicles

Autonomous vehicles (AVs) are becoming increasingly prevalent. However, current AVs are unable to handle unexpected traffic signs (e.g., construction zones, road closures) encountered on the roads. To address this limitation, we propose MOSER, a Multimodal rOad Sign intERpretation system, to enable automated detection and interpretation of diverse road signs. Our system consists of a pipeline architecture with three main components, including perception, text processing, and planning. The perception component detects arbitrary road signs and extracts the sign text into proper groups and orders. The text processing component then identifies the high-level semantics of the text and determines whether any actions are required for the autonomous vehicle. Based on the interpretation of the signs, the planning component provides navigation guidance, such as instructing the vehicle to stop at a specific location or adding rules to its internal map. To the best of our knowledge, this is the first attempt to address the interpretation of arbitrary road signs using a multimodal processing strategy. Our work provides important insights and capabilities to support Level 4 autonomous vehicles, ensuring their safety and smoothness of operation.
Oversampling techniques for predicting COVID-19 patient length of stay

COVID-19 is a respiratory disease that caused a global pandemic in 2019. It is highly infectious and has the following symptoms: fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, the new loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. These symptoms vary in severity; some people with many risk factors have been known to have lengthy hospital stays or die from the disease. In this paper, we analyze patients’ electronic health records (EHR) to predict the severity of their COVID-19 infection using the length of stay (LOS) as our measurement of severity. This is an imbalanced classification problem, as many people have a shorter LOS rather than a longer one. To combat this problem, we synthetically create alternate oversampled training data sets. Once we have this oversampled data, we run it through an Artificial Neural Network (ANN), which during training has its hyperparameters tuned by using bayesian optimization. We select the model with the best F1 score and then evaluate it and discuss it.
HAR-CTGAN: A Mobile Sensor Data Generation Tool for Human Activity Recognition

Human activity recognition (HAR) is the process of using mobile sensor data to determine the physical activities performed by individuals. HAR is the backbone of many mobile healthcare applications, such as passive health monitoring systems, early diagnosing systems, and fall detection systems. Effective HAR models rely on deep learning architectures and big data in order to accurately classify activities. Unfortunately, HAR datasets are expensive to collect, are often mislabeled, and have large class imbalances. State-of-the-art approaches to address these challenges utilize Generative Adversarial Networks (GANs) for generating additional synthetic data along with their labels. Problematically, these HAR GANs only synthesize continuous features — features that are represented by real numbers — recorded from gyroscopes, accelerometers, and other sensors that produce continuous data. This is limiting since mobile sensor data commonly has discrete features that provide additional context such as device location and the time-of-day, which have been shown to substantially improve HAR classification. Hence, we studied Conditional Tabular Generative Adversarial Networks (CTGANs) for data generation to synthesize mobile sensor data containing both continuous and discrete features, a task never been done by state-of-the-art approaches. We show HAR-CTGANs generate data with greater realism resulting in allowing better downstream performance in HAR models, and when state-of-the-art models were modified with HAR-CTGAN characteristics, downstream performance also improves.
Demystify scrambled and encoded images with auto-encoders

Monitoring systems have become more and more popular. A typical example is a smart home monitoring system which provides peace of mind for people staying far away from their homes. This system can be deployed for different purposes, such as to take care elderly staying at home alone or to provide anti-theft when nobody is at home. In this type of monitoring systems, there is always a problem with protecting the privacy and security of captured images. This problem is often addressed by either scrambling or encoding techniques that add noise in or to distort the images. In the IEEE Big Data 2022 Cup, the challenge is to detect and perform matching between an original image and its scrambled or encoded version. Generally, if we are able to train a machine learning model that would correctly identify the matching, it will be proof that the applied scrambling or encoding technique is not strong enough to protect data and hence it needs to be strengthened further. In this paper, we present an auto-encoder architecture that allows us to demystify scrambled or encoded images as long as the scrambling or encoding techniques are deterministic. Source code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Two-Stage Sampling: A Framework for Imbalanced Classification With Overlapped Classes

Class imbalance and overlapping instances problems have long been recognized as one of the major causes of the performance deterioration of the classification model. Moreover, the majority class may have an irrelevant and noisy instance that shifts the decision boundary of the classification far away from the ideal one. We propose a framework for balancing the class distribution and mitigating the class overlap problem in a dataset. The key feature of our framework is its ability to detect the overlapping instances between classes and then remove the problematic instances from the majority class. Thus, it will have more precise information for the oversampling method to generate the synthetic minority instances. We evaluated the proposed framework using the Lending club and ten other datasets from the KEEL repository. We demonstrate the implementations of our framework using Tomek and Edited Nearest Neighbor for removing the overlapping instances from the majority class and SWIM-MD for generating the synthetic minority instances. Also, we used eight well-known classifiers to show that our proposed framework can improve the performance of various classifiers. Lastly, we present a detailed analysis of the experimental result that shows the superiority of our proposed framework. Our proposed framework outperformed the state-of-the-art methods in terms of geometry mean classification performance metric.
A Personalized Federated Learning Framework Using Side Information for Heterogeneous Data Classification

Federated learning (FL) allows a large number of clients to improve their respective models through training a shared global model. However, passing the same global model is not conducive to the training of a few clients and leads to a large loss of localization information. In practical, there are often some prior information that can be shared between clients. Our study takes into account the use of such prior information to calculate a personalized global model for each client, resulting in an enhanced personalized federated learning framework, dubbed PerFL for short, that takes advantage of available client features that can be shared with other clients. More specifically, PerFL calculates the incidence matrix of all involved clients by using the permitted shareable side information and then updates the local models by using their similar clients instead of all clients. Employing the neural network as the classification model, PerFL learns the parameter matrices at each client in an iterative manner. On three publicly available image datasets, PerFL can benefit from the employed similarity and achieve an improved classification performance in comparison with the state-of-the-art FL models.
Distributional Shift Adaptation using Domain-Specific Features

Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming big data can be Out-Of-Distribution (OOD), rendering these algorithms ineffective. Prior solutions to the OOD challenge seek to identify invariant features across different training domains. The underlying assumption is that these invariant features should also work reasonably well in the unlabeled target domain. By contrast, this work is interested in the domain-specific features that include both invariant features and features unique to the target domain. We propose a simple yet effective approach that relies on correlations in general regardless of whether the features are invariant or not. Our approach uses the most confidently predicted samples identified by an OOD base model (teacher model) to train a new model (student model) that effectively adapts to the target domain. Empirical evaluations on benchmark datasets show that the performance is improved over the SOTA by ∼10-20%.
Multimodal Deep Homography Estimation Using a Domain Adaptation Generative Adversarial Network

Multimodal image registration is a challenging task. To begin with, the variation of parallax in the images makes the process intrinsically tricky. Additionally, due to phenomenology differences in modalities, the appearance of the same feature may vary significantly between the images making the registration laborious. To help mitigate these issues, we propose a two-step approach targeted at visible and infrared imagery. First, we train a generative adversarial network to learn the domain transfer function between the visible and the infrared domain, thereby mitigating the impact of the visual dissimilarity between the images. Second, we train a deep Siamese network to compute a homography in an unsupervised setting. Both elements are combined and trained sequentially. Our method is evaluated on a publicly available dataset. Our results show that the proposed method provides a reduction of more than 30% on average from the previous state-of-the-art, and outperforms several baselines and recent deep homography methods.