Reviewerly/exHarmony huggingface dataset:
Early literacy with augmented reality

Augmented reality is currently one of the most rapidly emerging technologies, with applications in the field of education. AR technology, according to a review of numerous publications, provides new options for teachers and makes learning more appealing to pupils of all ages. Students can use mobile AR apps to observe a real-world environment with virtual things layered or composited on top of it. This is especially true for young children. The article highlights the activities that allow a child to have a unique AR experience. There is a comparison of available augmented reality (AR) applications for learning the alphabet. The author describes a novel augmented reality application that was created with Unity, C#, and Vuforia. The developed mobile AR application allows students to learn the Ukrainian alphabet, number names, and animal sounds. The learning process is enhanced by three-dimensional visualization and sounding of each letter and number thanks to this program. An analysis of a survey of teachers and parents found that utilizing an AR application greatly boosted children's motivation and self-efficacy in learning letters and numbers. The AR application aided in the retention of the child's attention while learning new content by speeding up the process of memorization.
Sentiment Analysis in Healthcare: Motives, Challenges &amp; Opportunities pertaining to Machine Learning

Sentiment analysis has been increasingly popular in the present digital era which attempts to analyse the consumer reviews acquired from websites, blogs and social media platforms. The rich textual information contained data sources, thus understood as consumers’ reviews are very important to any particular domain as businesses are able to improve themselves in several aspects. This paper focuses on empirical research on sentiment analysis or opinion mining in the healthcare domain. With the careful analysis of all the relevant techniques, the sentiment analysis has secured the leading position in making vital business decisions. It outlines crucial topics pertaining to the sentiment analysis such as the motivation for using sentiment analysis, vital sentiment analysis techniques, new opportunities produced by the analysis, the challenges, and pertinent future directions. It also discusses the relevant data mining techniques and machine learning algorithms involved in the sentiment analysis in the healthcare domain. The study concludes by discussing the main future emphasis of the sentiment analysis in-terms of processing the medical documents to have a better understanding of the medical service consumers.
Detection of Different Types of Blood Cells: A Comparative Analysis

Object detection is known as the pinnacle of Artificial Intelligence. Object detection is utilized in various fields like healthcare, agriculture, sports, manufacturing, security, and many more. Object detection has numerous different techniques which provide efficient outcomes in different ways. Some algorithms provide better accuracy whereas some are faster than others. In this research, four major image processing algorithms are compared: You Only Look Once, Single Shot Multibox Detector, Faster Region-Based Convolutional Network, and Region-Based Fully Convolutional Network. The comparison of these algorithms is done using the Blood Cells Dataset of Microsoft (Common Object in Context) based on speed and accuracy. The dataset consists of three classes, namely, Platelets, Red Blood Cells, and White Blood Cells. Altogether, the dataset has 364 images and 4888 labels. After completion of the comparative analysis of these four algorithms, it is analyzed that yolo_v3 outperforms all other models in terms of both speed and accuracy.
Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

In this paper, the problem of vehicle guidance by means of an external leader is described. The objective is to navigate a four-wheeled vehicle through unstructured environments, characterized by the lack of availability of typical guidance infrastructure like lane markings or HD maps. The trajectory-following approach is based on an estimate of the leader’s path. For that, position measurements are stored over time with respect to an inertial frame. A new strategy is proposed to rate the significance of position measurements and ensure that a certain threshold of stored samples is not exceeded. Having an estimate of the leader path is essential to prevent the cutting-corner phenomenon and for exact path following in general. A spline-approximation technique is applied to obtain a smooth reference path for the underlying lateral and longitudinal motion controllers. For longitudinal tracking, a constant time-headway policy was implemented, to follow the leader with a constant time gap along the estimated path. The algorithm was first developed and tested in a simulation framework and then deployed in a demonstrator vehicle for validation under real operating conditions. The presented experimental results were achieved using only on-board sensors of the demonstrator vehicle, while high-accuracy differential GPS-based position measurements serve as the ground truth data for visualization.
Binarization of grayscale quantum image denoted with novel enhanced quantum representations

This paper proposes a new algorithm for binarizing the grayscale quantum images represented by novel enhanced quantum image representation(NEQR) through pixel qubit flipping. The algorithm consists of two steps: the first step performs the qubit state checking, and the second completes pixel qubit flipping based on the results in previous step. The key data analysis shows that our algorithm has the following advantages, which include: (1) Contrasting with the probable algorithm, the result of this paper is deterministic( without error). (2) Comparing with the latest result, our paper has some superior characters: fewer quantum gates, fewer auxiliary qubits and less quantum delay.
Bio Inspired Approaches for Indoor Path Navigation and Spatial Map Formation by Analysing Depth Data

Indoor Navigation presents a significant domain for carrying out with research issues. Literature review depicts inspite of having various existing graph theoretic optimization approaches for safe path exploration, the mobile robot faces difficulties in achieving robust and efficient computation. The cause of difficulties include limitation in sensor availability, limitation in reachability, having dynamic on path obstacles, and space-time complexity. The paper presents a working model for mobile robot navigation by detecting objects analysing depth data in the concerned environment followed by optimized path exploration using bio inspired path planning techniques. This is accompanied by spatial map formation incorporating with Robot Operating System (ROS) environment for getting an idea of visual inference of the taken indoor environment. Analysis of working principle and respective methodology of the algorithms have been done and experimental results are noted. Comparative ease in computation and optimization are obtained and presented neatly in subsequent sections. The analysis would prove to be remarkable citation for future research in the field of Optimized Collision-free Indoor Navigation (OCIN).
A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smat grid

Recently, multi-agent systems (MASs) have received attention due to the consideration of distributed optimization and control in blockchain (BC) based energy trading applications. However, due to the dynamic behavior of uncertain and random variables, the coordination and control of MAS are still challenging to attain resilience and dynamicity. Traditional trust systems, which rely on access control and cryptography, cannot deal with dynamic behavior of agents. Furthermore, they are inefficient in addressing the computational overhead of cryptographic primitives. To overcome these limitations, this work proposes a BC-based trusted suite (TS) for MAS to handle privacy and anonymity issues during energy trading in a smart grid (SG). In this work, three objectives are simultaneously achieved: trust, cooperation, and confidentiality. Firstly, the proposed trust system is employed to perform trust credibility of agents based on trust deformation, coherence, and stability. The credibility evaluation is used to determine the dynamic behavior of agents and to detect dishonest agents in the system. Secondly, a tri-tit-for-tat (TTFT) repeated game approach is used to improve the cooperation among agents. The proposed strategy is more forgiving than the existing Di-TFT (DTFT) and TFT techniques. It motivates scammers and deceptive agents to regain their trust by cooperating in three consecutive rounds of a game. Furthermore a proof-of-cooperation (PoC) consensus mechanism is introduced to facilitate agent cooperation in block creation and validation. Thirdly, the publicly verifiable secret sharing (PVSS) technique is introduced to preserve the privacy of the agents. Unlike VSS, PVSS provides the immunity against different types of security threats. Where, the dealer agent maintained the trust, while the verification of the dealer and combiner is maintained within agent-to-agent cooperation. Simulation results show that the devised BTS model is superior to the existing benchmark model such as fuzzy logic trust (FLT) in terms of detecting the cheating and deceptive behavior of agents in the system. Besides, the devised TTFT allows cheating agents to effectively regain the trust if they cooperate thrice in a row as compared to the existing DTFT and TFT strategies. Furthermore, This study analyzes two trust-related attacks: bad-mouthing and on-off. Analysis shows that the proposed system is protected from trust related attacks.
Experimental research in automatic subtitling

Abstract Recent developments in neural machine translation, and especially speech translation, are gradually but firmly entering the field of audiovisual translation (AVT). Automation in subtitling is extending from a machine translation (MT) component to fully automatic subtitling, which comprises MT, auto-spotting and automatic segmentation. The rise of this new paradigm renders MT-oriented experimental designs inadequate for the evaluation and investigation of automatic subtitling, since they fail to encompass the multimodal nature and technical requirements of subtitling. This paper highlights the methodological gaps to be addressed by multidisciplinary efforts in order to overcome these inadequacies and obtain metrics and methods that lead to rigorous experimental research in automatic subtitling. It presents a review of previous experimental designs in MT for subtitling, identifies their limitations for conducting research under the new paradigm and proposes a set of recommendations towards achieving replicability and reproducibility in experimental research at the crossroads between AVT and MT.
Research on low illumination coal gangue image enhancement based on improved Retinex algorithm

Aiming at the problems of coal and gangue image recognition in low illumination or high dust concentration environment, an adaptive enhancement method based on improved Retinex algorithm, is proposed: H-GF-MSR algorithm. The algorithm is processed in HSV color space, uses guided filter for multi-scale Retinex algorithm in brightness component, and carries out adaptive saturation stretching in saturation component. Then the images are processed by histogram equalization, and the two algorithms are fused to improve the accuracy. Compared with SSR algorithm, MSRCR algorithm and other classical algorithms, this algorithm is improved in suppressing the generation of halo and image edge enhancement. The experimental results show that in terms of image evaluation indexes such as information entropy, image mean, image average gradient and peak signal-to-noise ratio, compared with SSR algorithm, the algorithm improves by 5.4%, 41.1%, 33.2% and 0.4% respectively; compared with MSRCR algorithm, the average improvement is 2.9%, 32.4%, 31.7% and 0.7% respectively. Through the enhancement experiments of coal gangue image in the illumination range of 10 lux ~ 200 lux, the algorithm has better enhancement effect. It can effectively suppress the bright light and restore the original details of the object.
Precision medical image hash retrieval by interpretability and feature fusion

To address the problem of low accuracy of medical image retrieval due to high inter-class similarity and easy omission of lesions, a precision medical image hash retrieval method combining interpretability and feature fusion is proposed, taking chest X-ray images as an example.Firstly, the DenseNet-121 network is pre-trained on a large dataset of medical images without manual annotation using the comparison to learn (C2L) method to obtain a backbone network model containing more medical representations with training weights. Then, a global network is constructed by using global image learning to acquire an interpretable saliency map as attention mechanisms, which can generate a mask crop to get a local discriminant region. Thirdly, the local discriminant regions are used as local network inputs to obtain local features, and the global features are used with the local features by dimension in the pooling layer. Finally, a hash layer is added between the fully connected layer and the classification layer of the backbone network, defining classification loss, quantization loss and bit-balanced loss functions to generate high-quality hash codes. The final retrieval result is output by calculating the similarity metric of the hash codes.Experiments on the Chest X-ray8 dataset demonstrate that our proposed interpretable saliency map can effectively locate focal regions, the fusion of features can avoid information omission, and the combination of three loss functions can generate more accurate hash codes. Compared with the current advanced medical image retrieval methods, this method can effectively improve the accuracy of medical image retrieval.The proposed hash retrieval approach combining interpretability and feature fusion can effectively improve the accuracy of medical image retrieval which can be potentially applied in computer-aided-diagnosis systems.
Super-resolution of Solar Magnetograms Using Deep Learning

Abstract Currently, data-driven models of solar activity forecast are investigated extensively by using machine learning. For model training, it is highly demanded to establish a large database which may contain observations coming from different instruments with different spatio-temporal resolutions. In this paper, we employ deep learning models for super-resolution (SR) of magnetogram of Michelson Doppler Imager (MDI) in order to achieve the same spatial resolution of Helioseismic and Magnetic Imager (HMI). First, a generative adversarial network (GAN) is designed to transfer characteristics of MDI onto downscaled HMI, getting low-resolution HMI magnetogram in the same domain as MDI. Then, with the paired low-resolution and high-resolution HMI magnetograms, another GAN is trained in a supervised learning way, which consists of two streams, one is for generating high-fidelity image content, the other is explicitly optimized for generating elaborate image gradients. Thus, these two streams work together to guarantee both high-fidelity and photorealistic super-resolved images. Experimental results demonstrate that the proposed method can generate super-resolved magnetograms with perceptual-pleasant visual quality. Meanwhile, the best PSNR, LPIPS, RMSE, comparable SSIM and CC are obtained by the proposed method. The source code and data set can be accessed via https://github.com/filterbank/SPSR .
CSDM: A context-sensitive deep matching model for medical dialogue information extraction

Medical Dialogue Information Extraction has attracted increasing attention due to its great potential for application in electronic medical record generation, automatic disease diagnosis, etc. Recent methods have achieved considerable success but still suffer from two inherent limitations. On the one hand, a medical dialogue is composed of multiple utterances from two speaker roles, i.e., doctor and patient. Existing methods often ignore the transition and interaction of speaker roles. It makes the model unable to handle personal pronoun ambiguity. On the other hand, traditional methods have a weak ability to capture the information of multi-turn dialogue and lack the guidance of global context. In this paper, we propose a novel Context-Sensitive Deep Matching model for medical information extraction in multi-turn dialogue, dubbed CSDM. Specifically, we first introduce a multi-view aware channel, which exploits multiple mask templates to capture different information in dialogues. Thus, the transition and interaction of speaker roles are considered in the model. Second, we utilize a bi-directional attention mechanism to assess the relative importance of different contexts. Therefore, the proposed model can perceive information of multi-turn dialogue. Extensive experiments on a public benchmark dataset show that our method achieves new state-of-the-art performance, which demonstrates the effectiveness of CSDM.
Fine-Grained Multilevel Fusion for Anti-Occlusion Monocular 3D Object Detection

We propose a deep fine-grained multi-level fusion architecture for monocular 3D object detection, with an additionally designed anti-occlusion optimization process. Conventional monocular 3D object detection methods usually leverage geometry constraints such as keypoints, object shape relationships, and 3D to 2D optimizations to offset the lack of accurate depth information. However, these methods still struggle against directly extracting rich information for fusion from the depth estimation. To solve the problem, we integrate the monocular 3D features with the pseudo-LiDAR filter generation network between fine-grained multi-level layers. Our network utilizes the inherent multi-scale and promotes depth and semantic information flow in different stages. The new architecture can obtain features that incorporate more reliable depth information. At the same time, the problem of occlusion among objects is prevalent in natural scenes yet remains unsolved mainly. We propose a novel loss function that aims at alleviating the problem of occlusion. Extensive experiments have proved that the framework demonstrates a competitive performance, especially for the complex scenes with occlusion.
The Music Playlist as a Method of Education Research

Abstract As technologies are woven deep into the fabric of our postdigital society and universities, there is a need to devise new research methods, and to seek out new kinds of research material, in order to better understand our complex and changing surroundings. One such approach, I argue in this article, involves creating and analysing music playlists as a way of critically exploring the learning spaces and practices of higher education. To make this argument, I describe and discuss the ways that music playlists contributed towards an ethnographic study of undergraduate courses in Architecture and History at a UK university. This involved inviting students to participate in the creation of ‘study playlists’, as I sought to understand how their learning spaces and practices were affected by digital technologies. This approach initially helped to establish rapport and trust with participants, as well as eliciting conversation and interview discussion which surfaced how students use streamed playlists and other digital technologies to negotiate personalised learning spaces. By helping to reveal these and other rituals, the music playlist was shown to work as an ethnographic artefact, while at the same time exposing the postdigital character of the contemporary university.
Good Keyframes to Inpaint

Diminished Reality (DR) propagates pixels from a keyframe to subsequent frames for real-time inpainting. Keyframe selection has a significant impact on the inpainting quality, but untrained users struggle to identify good keyframes. Automatic selection is not straightforward either, since no previous work has formalized or verified what determines a good keyframe. We propose a novel metric to select good keyframes to inpaint. We examine the heuristics adopted in existing DR inpainting approaches and derive multiple simple criteria measurable from SLAM. To combine these criteria, we empirically analyze their effect on the quality using a novel representative test dataset. Our results demonstrate that the combined metric selects RGBD keyframes leading to high-quality inpainting results more often than a baseline approach in both color and depth domains. Also, we confirmed that our approach has a better ranking ability of distinguishing good and bad keyframes. Compared to random selections, our metric selects keyframes that would lead to higher-quality and more stably converging inpainting results. We present three DR examples, automatic keyframe selection, user navigation, and marker hiding.
Classification of acute myeloid leukemia M1 and M2 subtypes using machine learning

Classification of acute myeloid leukemia (AML) relies on manual analysis of bone marrow or peripheral blood smear images. We aimed to construct a machine learning model for automatic classification of AML-M1 and M2 subtypes in bone marrow smear images.Bone marrow smear images of AML patients were extracted from the Cancer Imaging Archive (TCIA) open database. Classification criteria of AML subtypes were based on the French-American-British (FAB) classification system. Random forest method and broad learning system (BLS) were used to develop the classification model. Morphological features, radiomics features, and clinical features were extracted. The performance of the classification model was evaluated by calculating accuracy, precision, recall, F1-score, and area under the curve (AUC). A total of 50 bone marrow smear images (AML-M1, 31 cases; AML-M2, 19 cases) with 500 slices were included in this study.A total of 43 morphological features, 276 radiomics features, and 1 clinical feature were extracted. Finally, 9 variables including 2 morphological features, 6 radiomics features, and 1 clinical feature were selected into the classification model. The best classification performance was observed in the random forest model with 9 variables, with the average accuracy, AUC, F1-score, recall, and precision of the model being 0.998 ± 0.003, 0.998 ± 0.004, 0.998 ± 0.004, 0.996 ± 0.009, and 1 ± 0, respectively.The random forest model performed well for the classification of AML-M1 and M2, which may provide a tool for clinicians to classify AML-M1 and M2.
Towards generalizable detection of face forgery via self-guided model-agnostic learning

Face forgery detection is an important yet challenging task that aims to distinguish whether a face video has been modified. As various types of face forgery are constantly produced and made available, existing methods usually overfit to the manipulation methods they are trained for, and cannot generalize well to detect the unseen or unknown forgery types. To address this issue, we present a systematic study on a more generalizable solution of face forgery detection, which endows the model an ability to recognize fake videos with unpredictable forgery types. Specifically, we develop a model-agnostic learning approach with a gradient-based meta-train and meta-test procedure to simulate the domain shift from known to unknown forgery types. To further emphasize the relative importance of different available forgery types during training, we propose a self-guided importance sampling strategy, which is integrated with a general video-level classification network. We also build a dataset with a wide range of 10 different forgery types to benchmark the generalization ability of face forgery detection. Extensive experiments on multiple testing protocols of evaluating generalization ability show that our method generalizes significantly better on unknown forgery manipulations.