Reviewerly/exHarmony huggingface dataset:
Learning Data Manipulation for Augmentation and Weighting

Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning different manipulation schemes with the same gradient-based algorithm. Our approach builds upon a recent connection of supervised learning and reinforcement learning (RL), and adapts an off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training. Different parameterization of the ``data reward'' function instantiates different manipulation schemes. We showcase data augmentation that learns a text transformation network, and data weighting that dynamically adapts the data sample importance. Experiments show the resulting algorithms significantly improve the image and text classification performance in low data regime and class-imbalance problems.
Deep Graph Matching Consensus

This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs. We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process. Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently. We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-the-art.
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning

Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired. This property makes these algorithms appealing for real world problems such as robot control. In practice, however, standard off-policy algorithms fail in the batch setting for continuous control. In this paper, we propose a simple solution to this problem. It admits the use of data generated by arbitrary behavior policies and uses a learned prior -- the advantage-weighted behavior model (ABM) -- to bias the RL policy towards actions that have previously been executed and are likely to be successful on the new task. Our method can be seen as an extension of recent work on batch-RL that enables stable learning from conflicting data-sources. We find improvements on competitive baselines in a variety of RL tasks -- including standard continuous control benchmarks and multi-task learning for simulated and real-world robots.
A Literature Review of Quantitative Persona Creation

Quantitative persona creation (QPC) has tremendous potential, as HCI researchers and practitioners can leverage user data from online analytics and digital media platforms to better understand their users and customers. However, there is a lack of a systematic overview of the QPC methods and progress made, with no standard methodology or known best practices. To address this gap, we review 49 QPC research articles from 2005 to 2019. Results indicate three stages of QPC research: Emergence, Diversification, and Sophistication. Sharing resources, such as datasets, code, and algorithms, is crucial to achieving the next stage (Maturity). For practitioners, we provide guiding questions for assessing QPC readiness in organizations.
SD-RSIC: Summarization-Driven Deep Remote Sensing Image Captioning

Deep neural networks (DNNs) have been recently found popular for image captioning problems in remote sensing (RS). Existing DNN-based approaches rely on the availability of a training set made up of a high number of RS images with their captions. However, captions of training images may contain redundant information (they can be repetitive or semantically similar to each other), resulting in information deficiency while learning a mapping from the image domain to the language domain. To overcome this limitation, in this article, we present a novel summarization-driven RS image captioning (SD-RSIC) approach. The proposed approach consists of three main steps. The first step obtains the standard image captions by jointly exploiting convolutional neural networks (CNNs) with long short-term memory (LSTM) networks. The second step, unlike the existing RS image captioning methods, summarizes the ground-truth captions of each training image into a single caption by exploiting sequence to sequence neural networks and eliminates the redundancy present in the training set. The third step automatically defines the adaptive weights associated with each RS image to combine the standard captions with the summarized captions based on the semantic content of the image. This is achieved by a novel adaptive weighting strategy defined in the context of LSTM networks. Experimental results obtained on the RSCID, UCM-Captions, and Sydney-Captions data sets show the effectiveness of the proposed approach compared with the state-of-the-art RS image captioning approaches. The code of the proposed approach is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitlab.tubit.tu-berlin.de/rsim/SD-RSIC</uri> .
Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.
A digital rights management system based on a scalable blockchain

Even though the Internet promotes data sharing and transparency, however it does not protect digital content. In today’s digital world, it has become a difficult task to release a DRM (Digital Rights Management) system that can be considered well-protected. Digital content that becomes easily available in open-source environments will in time be worthless to the creator. There may only be a one-time payment to creators upon initial upload to a given platform after which time the rights of the intellectual property are shifted to the platform itself. However, due to the online availability of content, anyone can download content and make copies. The value of digital content slowly decreases, because the value of content can usually be determined through the difficulty of it’s accessibility. There is no way to track the leakage or copyright for the spread of digital material. In this paper, a distributed media transaction framework for DRM is proposed, which is based on the digital watermarking and a scalable blockchain model. In this paper, our focus is on improving the classic blockchain systems to make it suitable for a DRM model. The DRM model in this paper allows only authorized users to use online content and provide original multimedia content. While the digital watermarking is used to reclaim the copyright ownership of offline contents in the event when the contents are leaked.
Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

The recent development of machines exhibiting intelligent characteristics involves numerous techniques including computer hardware and software architecture development.Many different hardware devices, wearable sensors, machine learning, and deep learning model implementations are being applied in human activity recognition (HAR) applications in recent times.However, to develop high accuracy classification systems for activity recognition using low-cost hardware technology is of significant importance.To achieve this goal this study uses sensor data from two low-cost sensors, gyroscope and accelerometer along with the implementation of an Artificial Neural Network (ANN) based deep learning model for HAR.In particular, Deep Stacked Multilayered Perceptron (DS-MLP) has been proposed.In the implementation of DS-MLP, an ANN model has been used as a meta-learner while five MLP models have been used as base-learners.In this study, these base-learners and meta-learner have been combined using a stack ensemble technique.The performance evaluations have been done first on the applicability of individual base-models followed by the application of DS-MLP, the results prove the high accuracy of 97.3% and 99.4% for heterogeneous datasets used for testing.The performance of the proposed DS-MLP models has been compared to some existing machine learning classifiers and several state-of-the-art activity recognition systems.The comparative result analysis also proves that the proposed system performed better than these classification approaches in terms of important performance metrics such as accuracy, precision, recall, F-score, Cohen's Kappa, and Mathew correlation coefficient.
Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model's generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.
Face Recognition and Identification using Deep Learning Approach

Abstract Human face is the significant characteristic to identify a person. Everyone has their own unique face even for twins. Thus, a face recognition and identification are required to distinguish each other. A face recognition system is the verification system to find a person’s identity through biometric method. Face recognition has become a popular method nowadays in many applications such as phone unlock system, criminal identification and even home security system. This system is more secure as it does not need any dependencies such as key and card but only facial image is needed. Generally, human recognition system involves 2 phases which are face detection and face identification. This paper describes the concept on how to design and develop a face recognition system through deep learning using OpenCV in python. Deep learning is an approach to perform the face recognition and seems to be an adequate method to carry out face recognition due to its high accuracy. Experimental results are provided to demonstrate the accuracy of the proposed face recognition system.
Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios. However, while designing these systems, machine learning approaches suffer from the problem of imbalanced data, i.e. imbalanced class distribution. Therefore, balancing the dataset becomes an imperative sub-task. Investigation of state-of-the-art approaches reveals that there is a need for a systematic study of class imbalance handling strategies to design an intelligent and capable system to detect the fraudulent transaction. This work aims to provide a comparative study of different class imbalance handling methods. To compare the effectiveness and efficiency of different class imbalance approaches in conjunction with state-of-the-art classification approaches, we have performed an extensive experimental study. We compared these methods on many performance indicators such as Precision, Recall, K-fold Cross-validation, AUC-ROC curve and execution time. In this study, we found that the Oversampling followed by Undersampling methods performs well for ensemble classification models such as AdaBoost, XGBoost and Random Forest.
Neural Head Avatars from Monocular RGB Videos

We present Neural Head Avatars, a novel neural representation that explicitly models the surface geometry and appearance of an animatable human avatar that can be used for teleconferencing in AR/VR or other applications in the movie or games industry that rely on a digital human. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> philgras.github.io/neural_head_avatars/neural_head_avatars.html Our representation can be learned from a monocular RGB portrait video that features a range of different expressions and views. Specifically, we propose a hybrid representation consisting of a morphable model for the coarse shape and expressions of the face, and two feed-forward networks, predicting vertex offsets of the underlying mesh as well as a view- and expression-dependent texture. We demonstrate that this representation is able to accurately extrapolate to unseen poses and view points, and generates natural expressions while providing sharp texture details. Compared to previous works on head avatars, our method provides a disentangled shape and appearance model of the complete human head (including hair) that is compatible with the standard graphics pipeline. Moreover, it quantitatively and qualitatively outperforms current state of the art in terms of reconstruction quality and novel-view synthesis.
Uncertainty Propagation in Fuzzy Grey Cognitive Maps With Hebbian-Like Learning Algorithms

This paper is focused on an innovative fuzzy cognitive maps extension called fuzzy grey cognitive maps (FGCMs). FGCMs are a mixture of fuzzy cognitive maps and grey systems theory. These have become a useful framework for facing problems with high uncertainty, under discrete small and incomplete datasets. This paper deals with the problem of uncertainty propagation in FGCM dynamics with Hebbian learning. In addition, this paper applies differential Hebbian learning (DHL) and balanced DHL to FGCMs for the first time. We analyze the uncertainty propagation in eight different scenarios in a classical chemical control problem. The results give insight into the propagation of the uncertainty or greyness in the iterations of the FGCMs. The results show that the nonlinear Hebbian learning is the choice with less uncertainty in steady final grey states for Hebbian learning algorithms.
The Definition of Reuse

The ability to reuse research data is now considered a key benefit for the wider research community. Researchers of all disciplines are confronted with the pressure to share their research data so that it can be reused. The demand for data use and reuse has implications on how we document, publish and share research in the first place, and, perhaps most importantly, it affects how we measure the impact of research, which is commonly a measurement of its use and reuse. It is surprising that research communities, policy makers, etc. have not clearly defined what use and reuse is yet. We postulate that a clear definition of use and reuse is needed to establish better metrics for a comprehensive scholarly record of individuals, institutions, organizations, etc. Hence, this article presents a first definition of reuse of research data. Characteristics of reuse are identified by examining the etymology of the term and the analysis of the current discourse, leading to a range of reuse scenarios that show the complexity of today’s research landscape, which has been moving towards a data-driven approach. The analysis underlines that there is no reason to distinguish use and reuse. We discuss what that means for possible new metrics that attempt to cover Open Science practices more comprehensively. We hope that the resulting definition will enable a better and more refined strategy for Open Science.
Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non‐linear regression models: A comparison

Abstract In this study, abnormalities in medical images are analysed using three classifiers, and the results are compared. Breast cancer remains a major public health problem among women worldwide. Recently, many algorithms have evolved for the investigation of breast cancer diagnosis through medical imaging. A computer‐aided microcalcification detection method is proposed to categorise the nature of breast cancer as either benign or malignant from input mammogram images. The standard mammogram image corpus, the Mammogram Image Analysis Society database is utilised, and feature extraction is performed using five different wavelet families at level 4 and level 6 decomposition. The work is accomplished through firefly algorithm (FA), extreme learning machine (ELM) and least‐square‐based non‐linear regression (LSNLR) classifiers. The performance of the classifiers is compared by benchmark metrics, such as total error rate, specificity, sensitivity, area under the receiver operating characteristic curve, precision, F1 score and the Matthews correlation coefficient. As validation of the classifier results, a kappa analysis is included to determine the agreement among classifiers. The LSNLR classifier attains a 3% to 7% improvement in average accuracy compared with the average classification accuracy of the FA (86.75%) and ELM (90.836%) classifiers.
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation

Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples. Adversarial examples are carefully crafted samples that force machine learning models to make mistakes during testing time. These malicious samples have been shown to be highly effective in misguiding classification tasks. However, research on the influence of adversarial examples on segmentation is significantly lacking. Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models. Specifically, we expose the vulnerability of these models to adversarial examples by proposing the Adaptive Segmentation Mask Attack (ASMA). This novel algorithm makes it possible to craft targeted adversarial examples that come with (1) high intersection-over-union rates between the target adversarial mask and the prediction and (2) with perturbation that is, for the most part, invisible to the bare eye. We lay out experimental and visual evidence by showing results obtained for the ISIC skin lesion segmentation challenge and the problem of glaucoma optic disc segmentation. An implementation of this algorithm and additional examples can be found at https://github.com/utkuozbulak/adaptive-segmentation-mask-attack .
Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction

Many statistical histogram-based methods perform intensity transformation on gray levels in the statistical histogram. This may cause over-enhancement due to dominating portions of the histogram. Various methods tackle this problem by overwhelming the dominating portions and improving the inferior components. Though, this may change the natural appearances of the image and results in degraded visual quality. In order to attenuate such limitations, an efficient method called Fuzzy Dissimilarity Adaptive Histogram Equalization with Gamma Correction (FDAHE-GC) algorithm is proposed. In this work, a Fuzzy Dissimilarity Histogram (FDH) is obtained from the neighborhood characteristics of an intensity. An intensity mapping function, constructed from FDH is applied to enhance the contrast and natural characteristics of an image. Finally, the gamma correction is employed to enhance the dark regions. In order to tune the fine details and to improve visual appearance of an image, the proposed FDAHE-GC algorithm is applied to the intensity value of HSI space. The performance of the presented method is evaluated with different existing methods using image quality assessment tools such as entropy, Colorfulness (C), Hue Deviation Index (HDI), Saturation, Contrast Enhancement Factor (CEF) and Gradient (G). The investigational results tested on standard benchmark test images with visual inspection shows the superiority of the proposed FDAHE-GC algorithm.