Reviewerly/exHarmony huggingface dataset:
Revolutionizing education system with interactive augmented reality for quality education

Augmented reality would soon impact the modern learning cycle in education. AR has the ability to alter research location and timing, adding new and additional forms and methods. Augmented Reality technology’s capabilities will make classes more interactive and knowledgeable. We all are 3D creatures. Our brain is the most powerful 3D computer technology in the world. All of us have evolved to think and store memory in 3-dimensions i.e when we look at the information in a flat piece of paper or computer screen, our brain takes its own time to translate it back to a 2D visual and store it. This is how our education system is, it started with textbook material and recently educators have started encouraging the use of smart classes which again shows videos. As a result, there is no practical exposure that lags us a step behind. This is where a promising technology of the future called Augmented Reality holds for us. Learners will have a 360-degree view of the real-world entities, interacting with those entities by touching them which will eventually make them have better insight about the concepts. Hence our learning curve decreases increasing the brain's productivity. With the development of smartphones, Augmented Reality is applied to a broader range. Augmented reality can turn an ordinary classroom into an engaging experience. The aim is to implement an interactive Augmented Reality Experience using Vuforia, Unity 3D and Blender. The purpose is to promote situational learning, experimental learning, transformative learning as the learning theory basis of mobile Augmented Reality.
Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer's disease (AD) classification performance.T1-weighted brain MRI scans from 151 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer's Coordinating Center (NACC, n = 565) were used for model validation.The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets.This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.
Dark Web—Onion Hidden Service Discovery and Crawling for Profiling Morphing, Unstructured Crime and Vulnerabilities Prediction

The mainstream use of the Internet and mobile technology has made it easier to reach a wide range of globalized resources. By having the numerous lawful design principles offered by Internet merchants, unregulated cyber terrorist and hacking markets by intruders actively working at online platform. Illicit web store and markets are capitalizing on the privacy and at globalized existence on the Internet, posing problems, hidden unsecure environment and obstacles for policing and security agencies. The proposed work offers a summary of the anonymous crime favored place at Dark Web and recent research on illegal Internet drug trafficking, human trafficking, terrorist funding and recruitment, money laundering, contract hacking, organ trafficking, cracked key distribution, killing contracts, forged passport, illegal post sharing and forum discussion, fraud and credit card selling, weapons order, and cybercrime and child sexual abuse markets in the Dark Web. The work presented here outlines the Tor network crawling procedure and evaluation of hidden links for analysis with the crawling of drug trafficking, Criminal activity-related signatures and posts put light on the negative side of the Dark Web platform and their services, techniques and methods use for data crawling, pattern recognition and behavior understanding of criminals followed by terrorist organizations, campaigning on social network platform using hidden identities for recruitment, fundraising and radicalization.
Blockchain-Based Attack Detection on Machine Learning Algorithms for IoT-Based e-Health Applications

The application of machine learning (ML) algorithms are massively scaling up due to rapid digitization and emergence of new tecnologies like the Internet of Things (IoT). In today's digital era, we can find ML algorithms being applied in the areas of healthcare, IoT, engineering, finance, and more. However, all these algorithms need to be trained in order to predict/solve a particular problem. There is high possibility of tampering with the training datasets and producing biased results. Hence, in this article, we propose a blockchain-based solution to secure the datasets generated from IoT devices for e-health applications. The proposed blockchain-based solution uses private cloud to tackle the aforementioned issue. For evaluation, we have developed a system that can be used by dataset owners to secure their data.
CNN-based bi-directional and directional long-short term memory network for determination of face mask

The COVID-19 virus, exactly like in numerous other diseases, can be contaminated from person to person by inhalation. In order to prevent the spread of this virus, which led to a pandemic around the world, a series of rules have been set by governments that people must follow. The obligation to use face masks, especially in public spaces, is one of these rules. The aim of this study is to determine whether people are wearing the face mask correctly by using deep learning methods. A dataset consisting of 2000 images was created. In the dataset, images of a person from three different angles were collected in four classes, which are “masked”, “non-masked”, “masked but nose open”, and “masked but under the chin”. Using this data, new models are proposed by transferring the learning through AlexNet and VGG16, which are the Convolutional Neural network architectures. Classification layers of these models were removed and, Long-Short Term Memory and Bi-directional Long-Short Term Memory architectures were added instead. Although there are four different classes to determine whether the face masks are used correctly, in the six models proposed, high success rates have been achieved. Among all models, the TrVGG16 + BiLSTM model has achieved the highest classification accuracy with 95.67%. The study has proven that it can take advantage of the proposed models in conjunction with transfer learning to ensure the proper and effective use of the face mask, considering the benefit of society.
RadBERT: Adapting Transformer-based Language Models to Radiology

Purpose To investigate if tailoring a transformer-based language model to radiology is beneficial for radiology natural language processing (NLP) applications. Materials and Methods This retrospective study presents a family of bidirectional encoder representations from transformers (BERT)–based language models adapted for radiology, named RadBERT. Transformers were pretrained with either 2.16 or 4.42 million radiology reports from U.S. Department of Veterans Affairs health care systems nationwide on top of four different initializations (BERT-base, Clinical-BERT, robustly optimized BERT pretraining approach [RoBERTa], and BioMed-RoBERTa) to create six variants of RadBERT. Each variant was fine-tuned for three representative NLP tasks in radiology: (a) abnormal sentence classification: models classified sentences in radiology reports as reporting abnormal or normal findings; (b) report coding: models assigned a diagnostic code to a given radiology report for five coding systems; and (c) report summarization: given the findings section of a radiology report, models selected key sentences that summarized the findings. Model performance was compared by bootstrap resampling with five intensively studied transformer language models as baselines: BERT-base, BioBERT, Clinical-BERT, BlueBERT, and BioMed-RoBERTa. Results For abnormal sentence classification, all models performed well (accuracies above 97.5 and F1 scores above 95.0). RadBERT variants achieved significantly higher scores than corresponding baselines when given only 10% or less of 12 458 annotated training sentences. For report coding, all variants outperformed baselines significantly for all five coding systems. The variant RadBERT–BioMed-RoBERTa performed the best among all models for report summarization, achieving a Recall-Oriented Understudy for Gisting Evaluation–1 score of 16.18 compared with 15.27 by the corresponding baseline (BioMed-RoBERTa, P < .004). Conclusion Transformer-based language models tailored to radiology had improved performance of radiology NLP tasks compared with baseline transformer language models. Keywords: Translation, Unsupervised Learning, Transfer Learning, Neural Networks, Informatics Supplemental material is available for this article. Published under a CC BY-NC-ND 4.0 license. See also commentary by Wiggins and Tejani in this issue.
A parallel image encryption algorithm using intra bitplane scrambling

More and more privacy-sensitive images have been produced daily. Chaotic encryption guarantees that only the targeted receiver can obtain the raw image. Many works perform encryption on bit-level for better permutation but incur an unbearable time consumption caused by generating additional 3D coordinates. High-dimensional chaotic system, suitable for encrypting images, needs to iterate repeatedly for several rounds, resulting in low encrypting efficiency. This paper proposes a parallel image encryption algorithm using intra bitplane scrambling. Four threads are utilized to encrypt the images in bit-level during the permutation, where each thread scrambles two bitplanes. In diffusion, we build multiple threads to generate keystream segments to reduce encryption time. We make cryptographic analyses and compare the encryption speed with the advanced works, demonstrating that our scheme is secure and efficient. • A parallel bit-level permutation algorithm by considering the intrinsic features of digital images is proposed, it offers a good trade-off between permutation efficiency and effect. • To save the keystreams generation in diffusion stage, multi-threading technology is used to generate them in parallel. • Sufficient security analyses are carried out, and the results show the proposed cryptosystem has high efficiency compared with other state-of-the-art algorithms.
BEHAVE: Dataset and Method for Tracking Human Object Interactions

Modelling interactions between humans and objects in natural environments is central to many applications including gaming, virtual and mixed reality, as well as human behavior analysis and human-robot collaboration. This challenging operation scenario requires generalization to vast number of objects, scenes, and human actions. Unfortunately, there exist no such dataset. Moreover, this data needs to be acquired in diverse natural environments, which rules out 4D scanners and marker based capture systems. We present BEHAVE dataset, the first full body human-object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them. We record ~15k frames at 5 locations with 8 subjects performing a wide range of interactions with 20 common objects. We use this data to learn a model that can jointly track humans and objects in natural environments with an easy-to-use portable multi-camera setup. Our key insight is to predict correspondences from the human and the object to a statistical body model to obtain human-object contacts during interactions. Our approach can record and track not just the humans and objects but also their interactions, modeled as surface contacts, in 3D. Our code and data can be found at: http://virtualhumans.mpi-inf.mpg.de/behave.
FlowNet: A Deep Learning Framework for Clustering and Selection of Streamlines and Stream Surfaces

For effective flow visualization, identifying representative flow lines or surfaces is an important problem which has been studied. However, no work can solve the problem for both lines and surfaces. In this paper, we present FlowNet, a single deep learning framework for clustering and selection of streamlines and stream surfaces. Given a collection of streamlines or stream surfaces generated from a flow field data set, our approach converts them into binary volumes and then employs an autoencoder to learn their respective latent feature descriptors. These descriptors are used to reconstruct binary volumes for error estimation and network training. Once converged, the feature descriptors can well represent flow lines or surfaces in the latent space. We perform dimensionality reduction of these feature descriptors and cluster the projection results accordingly. This leads to a visual interface for exploring the collection of flow lines or surfaces via clustering, filtering, and selection of representatives. Intuitive user interactions are provided for visual reasoning of the collection with ease. We validate and explain our deep learning framework from multiple perspectives, demonstrate the effectiveness of FlowNet using several flow field data sets of different characteristics, and compare our approach against state-of-the-art streamline and stream surface selection algorithms.
Nested Markov properties for acyclic directed mixed graphs

Conditional independence models associated with directed acyclic graphs (DAGs) may be characterized in at least three different ways: via a factorization, the global Markov property (given by the d-separation criterion), and the local Markov property. Marginals of DAG models also imply equality constraints that are not conditional independences; the well-known ``Verma constraint'' is an example. Constraints of this type are used for testing edges, and in a computationally efficient marginalization scheme via variable elimination. We show that equality constraints like the ``Verma constraint'' can be viewed as conditional independences in kernel objects obtained from joint distributions via a fixing operation that generalizes conditioning and marginalization. We use these constraints to define, via ordered local and global Markov properties, and a factorization, a graphical model associated with acyclic directed mixed graphs (ADMGs). We prove that marginal distributions of DAG models lie in this model, and that a set of these constraints given by Tian provides an alternative definition of the model. Finally, we show that the fixing operation used to define the model leads to a particularly simple characterization of identifiable causal effects in hidden variable causal DAG models.
Long-Form Video Question Answering via Dynamic Hierarchical Reinforced Networks

Open-ended long-form video question answering is a challenging task in visual information retrieval, which automatically generates a natural language answer from the referenced long-form video contents according to a given question. However, the existing works mainly focus on short-form video question answering, due to the lack of modeling semantic representations from long-form video contents. In this paper, we introduce a dynamic hierarchical reinforced network for open-ended long-form video question answering, which employs an encoder-decoder architecture with a dynamic hierarchical encoder and a reinforced decoder. Concretely, we first propose a frame-level dynamic long-short term memory (LSTM) network with binary segmentation gate to learn frame-level semantic representations according to the given question. We then develop a segment-level highway LSTM network with a question-aware highway gate for segment-level semantic modeling. Furthermore, we devise the reinforced decoder with a hierarchical attention mechanism to generate natural language answers. We construct a large-scale long-form video question answering dataset. The extensive experiments on the long-form dataset and another public short-form dataset show the effectiveness of our method.
A quantum classification algorithm for classification incomplete patterns based on entanglement measure

In this paper, a novel quantum classification algorithm that is based on competitive learning is presented to classify an input pattern that results from the failures of some sensors. As long as an incomplete pattern is presented to our model, the proposed algorithm performs the competitions betwee n the neurons by applying some unitary transformations then measures the degree of entanglement using concurrence measure to find the winner class based on the winner-take-all technique. The proposed algorithm finds the most likely winning class label in between two binary competitive classes for an incomplete pattern presented to the proposed model. Because larger scale quantum computers are still in the lab, we studied the proposed algorithm on a case study.
A two-stage filter for high density salt and pepper denoising

Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%.
"They Just Don't Get It": Towards Social Technologies for Coping with Interpersonal Racism

Over 35% of Americans belong to racial minority groups. Racism targeting these individuals results in a range of harmful physical, psychological, and practical consequences. The present work aims to shed light on the current sense-making and support-seeking practices exhibited by targets of racism, as well as to identify the core needs and barriers that future socio-technical interventions could potentially address. The long-term goal of this work is to understand how CSCW researchers and designers could best support members of marginalized groups to make sense of and to seek support for experiences with racism. Narrative episode interviews with targets of racism revealed a number of key entry points for intervention. For example, participants' personal stories confirmed that uncertainty, both about the nature and consequences of the experience of racism, is a key motivator for support-seeking. In addition, despite the need for support, participants largely do not trust public forms of social media for support-seeking. We discuss how participants' accounts of the complex labor involved in determining who "gets it" in identifying potential supporters, and in navigating the complexities of trust and agency in sharing their experiences, present clear implications for the design of new socio-technical platforms for members of racial minority groups.
An innovative approach in combating economic crime using forensic accounting techniques

Purpose The purpose of this study is to develop an innovative approach of combating economic crime using the forensic accounting techniques. Design/methodology/approach The approach considered the identification of the effective forensic accounting techniques from the available literature and also explored the anti-economic crime policy, capable of assisting in the combating of economic crime. This brought about the development of two conceptual models, which incorporate all the requirements for the implementation of forensic accounting and the integration of forensic accounting technique into the organizational control system for effective fraud mitigation. Findings The analysis of the literature review indicated that one of the drawbacks, which has continue to mitigate the implementation of forensic accounting as a tool for combating fraud is lack of a suitable framework. This was the major focal point of this work, which produced two simplified conceptual models suitable for effective fraud mitigation. Research limitations/implications This study is limited to the development of conceptual models for fraud mitigation only. Practical implications The simplified model can easily be adopted into the structure of an organization to provide a sustainable solution to mitigate fraud occurrences. Originality/value The novelty of this study lies in the development of two simplified conceptual models. The first model addressed the incorporation of forensic accounting into the organization structure while the second captured the detailed investigation and comprehensive data analysis processes of uncovering fraud. The development of conceptual models with all these peculiarities for fraud mitigation has not been widely reported by the existing literature.
Trans Time

Trans people often use social media to connect with others, find and share resources, and post transition-related content. However, because most social media platforms are not built with trans people in mind and because online networks include people who may not accept one's trans identity, sharing trans content can be difficult. We studied Trans Time, a social media site developed particularly for trans people to document transition and build community. We interviewed early Trans Time users (n = 6) and conducted focus groups with potential users (n = 21) to understand how a trans-specific site uniquely supports its users. We found that Trans Time has the potential to be a safe space, encourages privacy, and effectively enables its users to selectively view content using content warnings. Together, safety, privacy, and content warnings create an online space where trans people can simultaneously build community, find support, and express both the mundanity and excitement of trans life. Yet in each of these areas, we also learned ways that the site can improve. We provide implications for how social media sites may better support trans users, as well as insular communities of people from other marginalized groups.