Reviewerly/exHarmony huggingface dataset:
Bare-hand gesture occlusion-aware interactive augmented reality assembly

Augmented reality (AR) has increasingly been applied to benefit manual assembly tasks by conveying the paper-based instructions into visual guidance intuitively, alleviating the cognitive load while improving assembly efficiency. Nevertheless, most current AR assemblies focus on spatial alignment by superimposing a 3D virtual model on the real scene directly, ignoring the occlusion awareness between virtual guidance and the real scene. To this end, we proposed a bare-hand occlusion-aware interactive AR assembly method based on the monocular image, and a lightweight deep neural network is established to infer the depth relationship between the 3D virtual model and real scene including gesture manipulation, and thus the ambiguous AR instruction by inaccurate occlusion deduction would be prevented, leading to more realistic bare-hand interactive AR guidance for manual assembly. And then, a quantitative evaluation criterion is established to illustrate the gesture occlusion awareness performance during manual assembly operations. Finally, comprehensive experiments are carried out and the results illustrate that the bare-hand occlusion-aware system can alleviate the cognitive load for the operators within interactive AR assembly tasks, providing a more human-center intelligent assembly application. • A lightweight network involving a global context module is applied to predict occlusion. • The intuitive perception evaluation criteria for occlusion awareness are established. • A bare-hand gesture occlusion-aware interactive AR assembly system is built for a human-centric manual assembly action.
Multiple instance relation graph reasoning for cross-modal hash retrieval

The similarity calculation is too simple in most cross-modal hash retrieval methods, which do not consider the impact of the relations between instances. To solve this problem, this paper proposes a reasoning method based on multiple instance relation graphs. By constructing similarity matrices, we establish global and local instance relation graphs, which fully exploit fine-grained relations between instances. First, we perform relation reasoning based on the relation graphs of the image and text modalities; then, we map the relations within the two modalities into the instance graphs; finally, we perform relation reasoning based on the instance graphs. Furthermore, to accommodate the features of both the image and text modalities, we employ a step-by-step training strategy to train the proposed neural network model. According to the results of experiments on the MIRFlickr and NUS-WIDE datasets, our method has apparent advantages in terms of mAP and has a good effect on the topK-precision curve. This shows that our method realizes the in-depth mining of instance relations, which can improve the retrieval performance significantly.
Marine predator inspired naked mole-rat algorithm for global optimization

This paper proposes a hybrid version of marine predator algorithm (MPA) and naked mole-rat algorithm (NMRA) to aggregate the strengths of both algorithms. The new proposed algorithm is named as MpNMRA and designed to overcome the inherent drawbacks of MPA (slow exploitation) and NMRA (limited exploration). The algorithm adds the basic structure of MPA to the worker phase of NMRA, while keeping all the major parameters of both the algorithm. The major parameters of both the algorithms are subjected to four different mutation strategies namely, exponential, linear, simulated annealing and logarithmic mutation strategies. The concept of simulated annealing-based mutation is found to be best for most of the parameters, whereas in some cases exponentially decreasing weights provide better results. Leveraging on the best mutation strategies for all the parameters, the proposed MpNMRA is tested on CEC2005, CEC2014 and CEC 2019 benchmark problems. The experimental results demonstrate that MpNMRA provides best results when compared to other algorithms in the literature on higher-dimensional problems. This work also considers solving three real-world optimization problems and training of a multi-layer perceptron using the proposed algorithm. Statistical results obtained from Wilcoxon’s rank-sum test, Freidman’s test and computational complexity further proves that the proposed algorithm is highly efficient and provide superior results.1
A Topic Modeling and Sentiment Analysis Model for Detection and Visualization of Themes in Literary Texts

Despite the growing emergence of new computer analytic software programs, the adoption and application of computer-based data mining and processing methods remain sparse in literary studies and analyses. This study proposes a text analytics lifecycle to detect and visualize the prevailing themes in a corpus of literary texts. Two objectives are to be pursued: First, the study seeks to apply a Topic Modeling approach with selected algorithms of LDA, LSI, NMF, and HDP that can effectively detect the recurring topics about the major themes developed in the dataset. Second, the project aims to apply a Sentiment Analysis model that can analyze the polarity of writers’ discourse on the detected thematic topics with the algorithms of Vader and TextBlob. The implementation of Topic Modeling has detected six thematic topics of sex, family, revolution, imprisonment, intellectual, and death. The adoption of the Sentiment Analysis model also revealed that the feelings attached to all the identified themes are largely negative sentiments expressed towards socio-political issues.
Blockchain and NFTs for Time-Bound Access and Monetization of Private Data

Digital data has enabled organizations to anticipate future threats, opportunities, and trends. However, digital data owners do not know how their data is accessed, shared, and monitized. In this paper, we propose using blockchain technology and non-fungible tokens (NFTs) to enable time-bound access and monitization of private data. Our approach allows users to upload encrypted content and mint it into NFTs. Other users can access the NFTs’ content by requesting a purchase or a license. Purchasing content transfers the ownership of the NFTs to the buyer; whereas, licensing them permits accessing the private data for a limited period of time, after which the data gets automatically deleted. Our developed approach uses the decentralized application (DApp), proxy reencryption (PRE), InterPlanetary File System (IPFS), and trusted execution environment (TEE) for managing a fully decentralized and robust system. We implement a proof-of-concept system in an Ethereum-based environment, which is used for testing and vulnerability checks. We present the cost and security analyses and discuss the generalization aspect of the solution. Our smart contracts and testing scripts are publicly available under an open-source license.
A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

Feature selection is employed to reduce feature dimensions and computational complexity by eliminating irrelevant and redundant features. A vast amount of increasing data and its processing generate many feature sets, that are reduced by the feature selection process to improve the performance in all sorts of classification, regression, clustering models. This research performs a detailed analysis of motivation and concentrates on the fundamental architecture of feature selection. The study aims to establish a structured formation related to popular methods such as filter, wrapper, embedded into search strategies, evaluation criteria, and learning methods. Different methods organize a comparison of benefits and drawbacks followed by multiple classification algorithms and standard validation measures. The diversity of applications in multiple domains such as data retrieval, prediction analysis, and medical, intrusion, and industrial applications are efficiently highlighted. The study focused on some additional feature selection methods for handling big data. Nonetheless, new challenges have surfaced in the analysis of such data, which are also addressed in this study. Reflecting on commonly encountered challenges and clarifying how to obtain the absolute feature selection method are the significant components of this study.
Transformer for Image Harmonization and Beyond

Image harmonization, aiming to make composite images look more realistic, is an important and challenging task. The composite, synthesized by combining foreground from one image with background from another image, inevitably suffers from the issue of inharmonious appearance caused by distinct imaging conditions, i.e., lights. Current solutions mainly adopt an encoder-decoder architecture with convolutional neural network (CNN) to capture the context of composite images, trying to understand what it should look like in the foreground referring to surrounding background. In this work, we seek to solve image harmonization with Transformer, by leveraging its powerful ability of modeling long-range context dependencies, for adjusting foreground light to make it compatible with background light while keeping structure and semantics unchanged. We present the design of our two vision Transformer frameworks and corresponding methods, as well as comprehensive experiments and empirical study, demonstrating the power of Transformer and investigating the Transformer for vision. Our methods achieve state-of-the-art performance on the image harmonization as well as four additional vision and graphics tasks, i.e., image enhancement, image inpainting, white-balance editing, and portrait relighting, indicating the superiority of our work. Code, models, more results and details can be found at the project website http://ouc.ai/project/HarmonyTransformer.
Super‐resolution of brain tumor MRI images based on deep learning

To explore and evaluate the performance of MRI-based brain tumor super-resolution generative adversarial network (MRBT-SR-GAN) for improving the MRI image resolution in brain tumors.A total of 237 patients from December 2018 and April 2020 with T2-fluid attenuated inversion recovery (FLAIR) MR images (one image per patient) were included in the present research to form the super-resolution MR dataset. The MRBT-SR-GAN was modified from the enhanced super-resolution generative adversarial networks (ESRGAN) architecture, which could effectively recover high-resolution MRI images while retaining the quality of the images. The T2-FLAIR images from the brain tumor segmentation (BRATS) dataset were used to evaluate the performance of MRBT-SR-GAN contributed to the BRATS task.The super-resolution T2-FLAIR images yielded a 0.062 dice ratio improvement from 0.724 to 0.786 compared with the original low-resolution T2-FLAIR images, indicating the robustness of MRBT-SR-GAN in providing more substantial supervision for intensity consistency and texture recovery of the MRI images. The MRBT-SR-GAN was also modified and generalized to perform slice interpolation and other tasks.MRBT-SR-GAN exhibited great potential in the early detection and accurate evaluation of the recurrence and prognosis of brain tumors, which could be employed in brain tumor surgery planning and navigation. In addition, this technique renders precise radiotherapy possible. The design paradigm of the MRBT-SR-GAN neural network may be applied for medical image super-resolution in other diseases with different modalities as well.
One-Shot Multiple Object Tracking in UAV Videos Using Task-Specific Fine-Grained Features

Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos is a fundamental task and can be applied in many fields. MOT consists of two critical procedures, i.e., object detection and re-identification (ReID). One-shot MOT, which incorporates detection and ReID in a unified network, has gained attention due to its fast inference speed. It significantly reduces the computational overhead by making two subtasks share features. However, most existing one-shot trackers struggle to achieve robust tracking in UAV videos. We observe that the essential difference between detection and ReID leads to an optimization contradiction within one-shot networks. To alleviate this contradiction, we propose a novel feature decoupling network (FDN) to convert shared features into detection-specific and ReID-specific representations. The FDN searches for characteristics and commonalities between the two tasks to synergize detection and ReID. In addition, existing one-shot trackers struggle to locate small targets in UAV videos. Therefore, we design a pyramid transformer encoder (PTE) to enrich the semantic information of the resulting detection-specific representations. By learning scale-aware fine-grained features, the PTE empowers our tracker to locate targets in UAV videos accurately. Extensive experiments on VisDrone2021 and UAVDT benchmarks demonstrate that our tracker achieves state-of-the-art tracking performance.
Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-recurrence Brain Tumor MRI Scans

Registration of pre-operative and post-recurrence brain images is often needed to evaluate the effectiveness of brain gliomas treatment. While recent deep learning-based deformable registration methods have achieved remarkable success with healthy brain images, most of them would be unable to accurately align images with pathologies due to the absent correspondences in the reference image. In this paper, we propose a deep learning-based deformable registration method that jointly estimates regions with absent correspondence and bidirectional deformation fields. A forward-backward consistency constraint is used to aid in the localization of the resection and recurrence region from voxels with absence correspondences in the two images. Results on 3D clinical data from the BraTS-Reg challenge demonstrate our method can improve image alignment compared to traditional and deep learning-based registration approaches with or without cost function masking strategy. The source code is available at https://github.com/cwmok/DIRAC .
Model Inversion Attacks Against Graph Neural Networks

Many data mining tasks rely on graphs to model relational structures among individuals (nodes). Since relational data are often sensitive, there is an urgent need to evaluate the privacy risks in graph data. One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns. Despite its success in grid-like domains, directly applying model inversion attacks on non-grid domains such as graph leads to poor attack performance. This is mainly due to the failure to consider the unique properties of graphs. To bridge this gap, we conduct a systematic study on model inversion attacks against Graph Neural Networks (GNNs), one of the state-of-the-art graph analysis tools in this paper. First, in the white-box setting where the attacker has full access to the target GNN model, we present GraphMI to infer the private training graph data. Specifically in GraphMI, a projected gradient module is proposed to tackle the discreteness of graph edges and preserve the sparsity and smoothness of graph features; a graph auto-encoder module is used to efficiently exploit graph topology, node attributes, and target model parameters for edge inference; a random sampling module can finally sample discrete edges. Furthermore, in the hard-label black-box setting where the attacker can only query the GNN API and receive the classification results, we propose two methods based on gradient estimation and reinforcement learning (RL-GraphMI). With the proposed methods, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our methods. We also evaluate our attacks under two defenses: one is the well-designed differential private training, and the other is graph preprocessing. Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.
Critical Success Factors for Transforming CRM to SCRM for building E-CRM

In the current scenario, the entire Middle East is witnessing drastic digitalization, and consumer behavior is also widely affected by the technological development. This has created a platform for the growth of e-commerce (Ecom) in the Middle East through social networking. The success factors that facilitate in transforming the customer relationship management (CRM) to social customer relationship management (SCRM) for achieving electronic customer relationship management (ECRM) are customer values and customer loyalty. These success factors are digitally termed as electronic customer values (ECV) and electronic customer loyalty (ECL). This study has involved 100 digital consumers of Ecom on various social apps and social networking in the Middle East. This study is an empirical analysis where 10 critical success factors are measured to achieve the positiveness of ECV to contribute in ECL success. ECL success can transform CRM to SCRM and build strong ECRM.
A Survey on Safety Regulation Technology of Blockchain Application and Blockchain Ecology

Blockchain's technological characteristics, such as decentralization, robustness, and anti-modification, represent a significant challenge to the regulation of existing networks and data security. Effective regulation of blockchain applications is one of the keys to maintain the healthy and sustainable development of the blockchain ecology. We analyze the merits and demerits of the existing technologies in the research directions of tracking and visualization of blockchain nodes, consortium blockchain penetration regulation technology, public chain active discovery and exploration disposition techniques, and chain governance. In the end, we present the future research directions in respect of blockchain traceability and compliance regulation.
Augmented Reality in K–12 Education: A Systematic Review and Meta-Analysis of the Literature from 2000 to 2020

With its capacity to support student-centered learning through digital transformation and shared experience, augmented reality (AR) has received increasing attention from both researchers and practitioners as an emerging technology to achieve innovative and sustainable education. Therefore, this study systematically reviewed the literature on the application of augmented reality in K–12 education settings between 2000 and 2020. After two stages of screening, 129 articles were selected, and the key research results were analyzed and integrated by adopting a coding scheme including basic information, instruction contexts, technical features, instructional design, and research results. The results revealed interesting findings regarding the augmented reality literature in terms of publication patterns, application fields, technological affordances, instructional designs, and methods. Furthermore, a meta-analysis was conducted to examine the effectiveness of augmented reality-based instruction, and the results showed a large overall effect size (g = 0.919) with three significant moderators. Finally, the practical significance of AR-based instruction and a future research agenda are discussed.
Low Power Blockchained E-Vote Platform for University Environment

With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected people. One of the remedies to this is precaution, and particularly social distancing. In the same vein, this paper proposes a remote voting system, which has to be secure, anonymous, irreversible, accessible, and simple to use. It therefore allows voters to have the possibility to vote for their candidate without having to perform the operation on site. This system will be used for university elections and particularly for student elections. We propose a platform based on a decentralized system. This system will use two blockchains communicating with each other: the public Ethereum blockchain and the private Quorum blockchain. The private blockchain will be institution-specific. All these blockchains send the necessary data to the public blockchain which manages different data related to the universities and the ministry. This system enables using encrypted data with the SHA-256 algorithm to have both security and information security. Motivated by the high energy consumption of blockchain and by the performance improvements in low-power, a test is performed on a low-power embedded platform Raspberry PI4 showing the possibility to use the Blockchain with limited resources.
Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation

The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets. In this paper, we focus on a variation of this problem in which we aim to predict repurchases, i.e. we wish to recommend a user only items she had purchased before. We coin this problem Next Basket Repurchase Recommendation (NBRR). Over the years, a variety of models have been proposed to address the problem of NBR, however, the problem of NBRR has been overlooked. Although being highly related problems, which are often solved by the same methods, the problem of repurchase recommendation calls for a different approach. In this paper, we share insights from our experience of facing the challenge of NBRR. In light of these insights, we propose a novel hyper-convolutional model to leverage the behavioral patterns of repeated purchases. We demonstrate the effectiveness of the proposed model on three publicly available datasets, where it is shown to outperform other existing methods across multiple metrics.
Conditional GAN with 3D discriminator for MRI generation of Alzheimer’s disease progression

Many studies aim to predict the degree of deformation on affected brain regions as Alzheimer's disease (AD) progresses. However, those studies have been often limited since it is difficult to obtain sequential longitudinal MR data of affected patients. Recently, conditional generative adversarial networks (cGANs) have been used to estimate the changes between unpaired images by modeling their differences. However, generating high-quality 3D magnetic resonance (MR) brain images with cGANs requires a large amount of computation. Previous models have been mostly designed to operate in 2D space taking individual slices or down-sampled 3D space, but these approaches often cause spatial artifacts such as discontinuities between slices or unnatural changes in 3D space. To address these limitations, we propose a novel cGAN that can synthesize high-quality 3D MR images at different stages of AD by integrating an additional module that ensures smooth and realistic transitions in 3D space. Specifically, the proposed cGAN model consists of an attention-based 2D generator, a 2D discriminator, and a 3D discriminator that is able to synthesize continuous 2D slices along the axial view resulting in good quality 3D MR volumes. Moreover, we propose an adaptive identity loss so that relevant transformations take place without compromising the features to identify patients. In our experiments, the proposed method showed better image generation performance than previously proposed GAN methods in terms of image quality and image generation suitable for the condition.