Reviewerly/exHarmony huggingface dataset:
A decision support system for selecting the most suitable machine learning in healthcare using user parameters and requirements

The application of machine learning in the medical field is still limited. The main reason behind the lack of use is the unavailability of an easy-to-use machine learning system that targets non-technical users. The objective of this paper is to propose an automated machine learning system to aid non-technical users. The proposed system provides the user with simple choices to provide suggestions to the system. The system uses the combination of the user’s choices and performance evaluation to select the most suited model from available options. In this study, we employed the system on a Parkinson’s disease dataset. The templates for support vector machine and random forest algorithms are provided to the system. Support vector machines and random forests were able to produce 80% and 75% accuracy, respectively. The system used performance parameters of the system and user choices to select the most suited models for each test case. The support vector machine was selected as the most suited model in three test cases, while random forest was selected as the most suited for one test case. The test cases also showed that the weighted time parameter impacted the results heavily.
Computationally Efficient Feature Significance and Importance for Predictive Models

We develop a simple and computationally efficient significance test for the features of a predictive model. Our forward-selection approach applies to any model specification, learning task and variable type. The test is non-asymptotic, straightforward to implement, and does not require model refitting. It identifies the statistically significant features as well as feature interactions of any order in a hierarchical manner, and generates a model-free notion of feature importance. This testing procedure can be used for model and variable selection. Experimental and empirical results illustrate its performance.
An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods

Nowadays, in the topics related to prediction, in addition to increasing the accuracy of existing algorithms, the reduction of computational time is a challenging issue that has attracted much attention. Since the existing methods may not have enough efficiency and accuracy, we use a combination of machine-learning algorithms and statistical methods to solve this problem. Furthermore, we reduce the computational time in the testing model by automatically reducing the number of trees using penalized methods and ensembling the remaining trees. We call this efficient combinatorial method “ensemble of clustered and penalized random forest (ECAPRAF)”. This method consists of four fundamental parts. In the first part, k-means clustering is used to identify homogeneous subsets of data and assign them to similar groups. In the second part, a tree-based algorithm is used within each cluster as a predictor model; in this work, random forest is selected. In the next part, penalized methods are used to reduce the number of random-forest trees and remove high-variance trees from the proposed model. This increases model accuracy and decreases the computational time in the test phase. In the last part, the remaining trees within each cluster are combined. The results of the simulation and two real datasets based on the WRMSE criterion show that our proposed method has better performance than the traditional random forest by reducing approximately 12.75%, 11.82%, 12.93%, and 11.68% and selecting 99, 106, 113, and 118 trees for the ECAPRAF–EN algorithm.
Position-guided transformer for image captioning

Transformer-based frameworks have shown superiorities in image captioning. However, such frameworks are strenuous to consider geometric interrelations among visual contents in an image, as well as fail to prevent changes in the distribution of each layer’s input in self-attention. In this work, we first propose a Bi-Positional Attention (BPA) module, which incorporates absolute and relative position encoding to precisely explore internal relations between objects and their geometric information in an image. Additionally, we use a Group Normalization (GN) method inside BPA to relieve shifts of the distribution and better exploit the channel dependence of visual features. To validate our proposals, we apply BPA and GN into the original Transformer to constitute our Position-Guided Transformer (PGT) network, which learns a more comprehensive positional representations to augment spatial interactions among objects for image captioning. We conduct extensive experiments to verify the effectiveness of our model. Compared with non-pretraining state-of-the-art methods, experimental results on the MSCOCO benchmark dataset demonstrate that our PGT achieves competitive performance, reaching 134.2% CIDEr score on the Karpathy split with a single model, and 136.2% CIDEr score on the official testing server with an ensemble configuration.
Semi-quantum key distribution with two classical users

Semi-quantum key distribution (SQKD) is an important research issue which allows one quantum participant equipped with advanced quantum devices to distribute a shared secret key securely with one classical user who has restricted capabilities. In this paper, we propose a SQKD protocol which allows one quantum user to distribute two different private secret keys to two classical users respectively at the same time. Alice distributes two particle sequences from Bell states to Bob and Charlie respectively. Once the particles have been processed and returned, Alice can simultaneously detect reflected particles by Bob and Charlie based on Bell-state measurement and generate two different raw keys. To enable more participants in sharing keys, this protocol can be extended to the m + 1 party communication scheme by employing m -particle GHZ state. In large-scale communication networks, this extended model significantly reduces the complexity of communication compared to the traditional SQKD scheme. Security analyses show that the presented protocol is free from several general attacks, such as the entangle-measure attack, the modification attack, the double CNOT attack, and so on.
Functional anomaly detection: a benchmark study

The increasing automation in many areas of the Industry expressly demands to design efficient machine learning solutions for the detection of abnormal events. With the ubiquitous deployment of sensors monitoring nearly continuously the health of complex infrastructures, anomaly detection can now rely on measurements sampled at a very high frequency, providing a very rich representation of the phenomenon under surveillance. In order to exploit fully the information thus collected, the observations cannot be treated as multivariate data anymore and a functional analysis approach is required. It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets. After an overview of the state of the art and a visual-descriptive study, a variety of anomaly detection methods are compared. While taxonomies of abnormalities (e.g., shape, location) in the functional setup are documented in the literature, assigning a specific type to the identified anomalies appears to be a challenging task. Thus, strengths and weaknesses of the existing approaches are benchmarked in view of these highlighted types in a simulation study. Anomaly detection methods are next evaluated on two datasets, related to the monitoring of helicopters in flight and to the spectrometry of construction materials namely. The benchmark analysis is concluded by a recommendation guidance for practitioners.
Unified feature extraction framework based on contrastive learning

Feature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. Contrastive learning (CL), which is a popular self-supervised learning method, has recently attracted considerable attention. In this study, based on a new perspective of CL, we propose a unified framework that is suitable for both unsupervised and supervised feature extraction. In the framework, two CL graphs are first constructed to define the positive and negative pairs uniquely. Subsequently, the projection matrix is determined by minimizing the contrastive loss function. Moreover, the proposed framework considers positive and negative pairs to unify the unsupervised and supervised feature extraction. We propose three specific methods under this framework: unsupervised CL, supervised CL without local preservation, and supervised CL with local preservation. Finally, numerical experiments on six real datasets demonstrate the superior performance of the proposed framework compared to existing methods. • A unified feature extraction framework based on contrastive learning for unsupervised and supervised cases is proposed. • The framework proposes a novel approach to define positive and negative pairs. • The framework can be regarded as maximizing the mutual information of positive pairs.
Garfinkel’s Studies of Work

Abstract In the early 1970s, Harold Garfinkel launched a research program on studies of work. Such work was not limited to on-the-job performances in specific occupations or professions. Although it included labor practices associated with particular organizations and occupations, it also encompassed the work of driving in traffic, queuing for a service, and other everyday practices. “Work” thus was salient in at least two ways: first, as a reference to occupational, professional, and organized recreational activities; and second, as a reference to the practical accomplishment of a broad range of what Garfinkel called “naturally organized ordinary activities.” This chapter focuses on both aspects of such studies of work: the study of specialized organizational activities and the study of everyday activities. In addition to reviewing the studies of work program and tracing its influence in fields such as science and technology studies (STS) and computer-supported cooperative work (CSCW), the chapter focuses on distinctive themes, such as “the unique adequacy requirement of methods” and “instructed actions” in the production of social order. Finally, the chapter discusses practical and professional implications of Garfinkel’s proposal for the development of “hybrid” fields that would integrate ethnomethodology with the practices studied.
Iterative Dual CNNs for Image Deblurring

Image deblurring attracts research attention in the field of image processing and computer vision. Traditional deblurring methods based on statistical prior largely depend on the selected prior type, which limits their restoring ability. Moreover, the constructed deblurring model is difficult to solve, and the operation is comparatively complicated. Meanwhile, deep learning has become a hotspot in various fields in recent years. End-to-end convolutional neural networks (CNNs) can learn the pixel mapping relationships between degraded images and clear images. In addition, they can also obtain the result of effectively eliminating spatial variable blurring. However, conventional CNNs have some disadvantages in generalization ability and details of the restored image. Therefore, this paper presents an iterative dual CNN called IDC for image deblurring, where the task of image deblurring is divided into two sub-networks: deblurring and detail restoration. The deblurring sub-network adopts a U-Net structure to learn the semantical and structural features of the image, and the detail restoration sub-network utilizes a shallow and wide structure without downsampling, where only the image texture features are extracted. Finally, to obtain the deblurred image, this paper presents a multiscale iterative strategy that effectively improves the robustness and precision of the model. The experimental results showed that the proposed method has an excellent effect of deblurring on a real blurred image dataset and is suitable for various real application scenes.
A hybrid LBP-DCNN based feature extraction method in YOLO: An application for masked face and social distance detection

COVID-19 is an ongoing pandemic and the WHO recommends at least one-meter social distance, and the use of medical face masks to slow the disease's transmission. This paper proposes an automated approach for detecting social distance and face masks. Thus, it aims to help the reduction of diseases transferred by respiratory droplets such as COVID-19. For this system, a two-cascaded YOLO is used. The first cascade detects humans in the environment and computes the social distance between them. Then, the second cascade detects human faces with or without a mask. Finally, red bounding boxes encircle the people's images that did not follow the rules. Also, in this paper, we propose a two-part feature extraction approach used with YOLO. The first part of the proposed feature extraction method extracts general features using the transfer learning approach. The second part extracts better features specific to the current task using the LBP layer and classification layers. The best average precision for the human detection task was obtained as 66% using Resnet50 in YOLO. The best average precision for the mask detection was obtained as 95% using Darknet19+LBP with YOLO. Also, another popular object detection network, Faster R-CNN, have been used for comparison purpose. The proposed system performed better than the literature in human and mask detection tasks.
Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images

In the age of deep learning, researchers have looked at domain adaptation under the pre-training and fine-tuning paradigm to leverage the gains in the natural image domain. These backbones and subsequent networks are designed for object detection in the natural image domain. They do not consider some of the critical characteristics of document images. Document images are sparse in contextual information, and the graphical page objects are logically clustered. This paper investigates the effectiveness of deep and robust backbones in the document image domain. Further, it explores the idea of learnable object proposals through Sparse R-CNN. This paper shows that simple domain adaptation of top-performing object detectors to the document image domain does not lead to better results. Furthermore, empirically showing that detectors based on dense object priors like Faster R-CNN, Mask R-CNN, and Cascade Mask R-CNN are perhaps not best suited for graphical page object detection. Detectors that reduce the number of object candidates while making them learnable are a step towards a better approach. We formulate and evaluate the Sparse R-CNN (SR-CNN) model on the IIIT-AR-13k, PubLayNet, and DocBank datasets and hope to inspire a rethinking of object proposals in the domain of graphical page object detection.
Deepfake attack prevention using steganography GANs

Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally impossible. A deepfake is created by swapping videos, images, or audio with the target, consequently raising digital media threats over the internet. Much work has been done to detect deepfake videos through feature detection using a convolutional neural network (CNN), recurrent neural network (RNN), and spatiotemporal CNN. However, these techniques are not effective in the future due to continuous improvements in GANs. Style GANs can create fake videos with high accuracy that cannot be easily detected. Hence, deepfake prevention is the need of the hour rather than just mere detection.Recently, blockchain-based ownership methods, image tags, and watermarks in video frames have been used to prevent deepfake. However, this process is not fully functional. An image frame could be faked by copying watermarks and reusing them to create a deepfake. In this research, an enhanced modified version of the steganography technique RivaGAN is used to address the issue. The proposed approach encodes watermarks into features of the video frames by training an "attention model" with the ReLU activation function to achieve a fast learning rate.The proposed attention-generating approach has been validated with multiple activation functions and learning rates. It achieved 99.7% accuracy in embedding watermarks into the frames of the video. After generating the attention model, the generative adversarial network has trained using DeepFaceLab 2.0 and has tested the prevention of deepfake attacks using watermark embedded videos comprising 8,074 frames from different benchmark datasets. The proposed approach has acquired a 100% success rate in preventing deepfake attacks. Our code is available at https://github.com/shahidmuneer/deepfakes-watermarking-technique.
Phase-transition-like behavior in information retrieval of a quantum scrambled random circuit system

Information in a chaotic quantum system will scramble across the system, preventing any local measurement from reconstructing it. The scrambling dynamics is key to understanding a wide range of quantum many-body systems. Here, we use Holevo information to quantify the scrambling dynamics, which shows a phase-transition-like behavior. When applying long random Clifford circuits to a large system, no information can be recovered from a subsystem of less than half the system size. When exceeding half the system size, the amount of stored information grows by two bits of classical information per qubit until saturation through another sharp unanalytical change. We also study critical behavior near the transition points. Finally, we use coherent information to quantify the scrambling of quantum information in the system, which shows similar phase-transition-like behavior.
An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems

Abstract We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child’s current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child’s performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments.
Effect of Virtual Reality News Presentation on News Learning

This is the first study in which the effects of viewing VR/360-degree video with and without a Cardboard device on news learning outcomes are examined. Cognitive load theory and the dual-coding hypothesis were adopted as the theoretical framework. An experiment was performed with 213 participants watching news clips. The findings show that the participants learned news information better from normal broadcast video or when they watched VR/360-degree video without a Cardboard device than when they watched VR/360-degree video with a Cardboard device. The theoretical and practical implications of these findings are discussed.
Effects of enactment in virtual reality: a comparative experiment on memory for action

Abstract Virtual reality (VR) is thought of as a promising educational medium, especially for learning actions, as it enables learning by enactment. Learning by enactment is associated with the enactment effect which describes a superior memory for enacted actions compared to actions which have not been enacted. To date, however, little is known about whether the enactment effect across different conditions of action learning can be found in VR which sets the stage for our first research question. Additionally, as a second research question, this study explores the extent to which the memory performance of learning by enactment in VR corresponds to learning by enactment in physical reality. We conducted a VR between subjects experiment with four groups ( N = 112) that differed in terms of condition or environment. Participants were asked to remember short action phrases for a subsequent memory test. The results indicate that learning by enactment in VR outperforms learning by reading in VR but does not exceed observational learning in VR. Furthermore, the results demonstrate that the memory performance of learning by enactment in VR is similar to that in physical reality. These findings are highly relevant as they demonstrate the potential of VR as a new educational medium supporting learning by enactment.
The role of design patterns in the development and legal assessment of lawful technologies

Novel technologies such as smart personal assistants integrate digital services into everyday life. These services use personal data to offer personalized services. While they are subject to special data protection regulations at the time of development, there are few guidelines describing the transition from legal requirements to implementation. To reduce risks, services depend on external legal assessments. With developers and legal experts often missing either legal or technical knowledge, the challenge lies in bridging this gap. We observe that design patterns support both developers and legal experts, and we present an approach in which design patterns are leveraged to provide twofold value for both developers and legal experts when dealing with novel technologies. We conducted a revelatory case study for smart personal assistants and scaffolded the case interpretation through cognitive fit theory. On the basis of the findings, we develop a theoretical model to explain and predict the twofold value of design patterns to develop and assess lawful technologies.
Deep learning models for multiple answers extraction and classification of scientific publications

This paper 1 presents an overview of a data augmented classification and multi-span (multiple) answer system for extracting key information from academic publications. This study consists of two sections: (a) implementing a new fine-tuned model to solve the multiple answer extraction issue (b) reporting results of sub-classification in various RF-EMF topics. In our previous study, it has been found that the essential cause of the low performance of the extractive question answering (EQA) system for certain types of questions was the multiple answer issue. To solve this problem, this study applies the TASE (TAg-based Span Extraction) technique and introduces the results. Our approach can retrieve multiple answers spreading over a given text by referring to the pre-trained TASE model with fine accuracy. In addition, this work adopts 'PEO (Population, Exposure, Outcome)' from the 'PECO' of the WHO-funded study on RF-EMF safety, as our holistic research framework. Based on the PEO perspective, the results of three sub-topic (RF, SAR, Causal Relationship) classifications are presented. For both models of multi-span answer and classification tasks, the data-augmenting method plays an important role. In particular, it is found that our proposed system outperforms the pre-trained BERT model in multi-span answer tasks with our RF-EMF dataset.