Reviewerly/exHarmony huggingface dataset:
CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation

Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features for the detection, segmentation, and tracking of objects and they suffer in the video scenario due to several distinct challenges such as motion blur and drastic appearance change. To eliminate ambiguities introduced by only using single-frame features, we propose a novel comprehensive feature aggregation approach (CompFeat) to refine features atboth frame-level and object-level with temporal and spatial context information. The aggregation process is carefully designed with a new attention mechanism which significantly increases the discriminative power of the learned features. We further improve the tracking capability of our model through a siamese design by incorporating both feature similarities and spatial similarities. Experiments conducted on the YouTube-VIS dataset validate the effectiveness of proposed CompFeat.
R2-D2: A Modular Baseline for Open-Domain Question Answering

This work presents a novel four-stage opendomain QA pipeline R2-D2 (RANK TWICE, READ TWICE).The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system's components.We demonstrate its strength across three open-domain QA datasets: Natu-ralQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two.Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets 1 .
MAGAN: Unsupervised low-light image enhancement guided by mixed-attention

Most learning-based low-light image enhancement methods typically suffer from two problems. First, they require a large amount of paired data for training, which are difficult to acquire in most cases. Second, in the process of enhancement, image noise is difficult to be removed and may even be amplified. In other words, performing denoising and illumination enhancement at the same time is difficult. As an alternative to supervised learning strategies that use a large amount of paired data, as presented in previous work, this paper presents an mixed-attention guided generative adversarial network called MAGAN for low-light image enhancement in a fully unsupervised fashion. We introduce a mixed-attention module layer, which can model the relationship between each pixel and feature of the image. In this way, our network can enhance a low-light image and remove its noise simultaneously. In addition, we conduct extensive experiments on paired and no-reference datasets to show the superiority of our method in enhancing low-light images.
RETRACTED ARTICLE: Privacy for Healthcare Data Using the Byzantine Consensus Method

The Healthcare Byzantine consensus method (HBFT) has poor scalability, arbitrary master node selection, and high network overhead in the healthcare scenario. This research constructs and proposes an optimized Byzantine consensus algorithm for the alliance chain. First, create roles for nodes in the cluster, provide them additional permissions based on their responsibilities, and build a dynamic network access method for nodes with various permissions. Second, reputation-based voting and FTS are meant for production node selection. The tree-combined voting algorithm assures election security and fairness. Finally, the HBFT consensus mechanism is optimized, reducing network overhead. The suggested POC consensus algorithm has high features dynamics, election security, and minimal overhead when compared to the HBFT algorithm.
Land Registry Framework Based on Self-Sovereign Identity (SSI) for Environmental Sustainability

Providing a system user with a unique and secure identity is a prerequisite for authentication and authorization aspects of a security system. It is generally understood that the existing digital identity systems store the identity details in centralized databases, and users store the identity details in centralized databases in which users do not have any control over them. These vulnerabilities in the traditional digital identities make them susceptible to various malicious assaults and modifications. Users’ personally identifiable information (PII) may leak through these identity solutions that can consequently affect other applications being used by the users, and they have no control over them. Land registration is a major domain of governance that defines civilians’ well-being and needs to be handled properly to avoid conflict and to support Environmental Sustainability. These traditional land registry applications also lack identity parameters due to weaknesses in identity solutions. A secure and reliable digital identity solution is the need of the hour. Self-sovereign identity (SSI), a new concept, is becoming more popular as a secure and reliable identity solution for users based on identity principles. SSI provides users with a way to control their personal information and consent for it to be used in various ways. In addition, the user’s identity details are stored in a decentralized manner, which helps to overcome the problems with digital identity solutions. This article reviews existing SSI solutions and analyzes them using SSI principles. It also assesses the SSI components required for constructing SSI frameworks that adhere to the SSI principles. Furthermore, it defines the procedures for establishing an SSI ecosystem, explores the laws governing digital identity that governments have adopted, and identifies SSI applications in several fields. Finally, a review of SSI applications in the domain of land registry systems is given in order to propose an SSI-based land registry framework for a secure and reliable land registry system.
Cyber Security through Blockchain Technology

Blockchain technology has seen adoption in many industries and most predominantly in finance through the use of cryptocurrencies. However, the technology is viable in cybersecurity. This paper looked at several use cases of Blockchain in the cybersecurity industry as envisioned by 30 researchers. It found that most researchers are concentrating on the adoption of Blockchain to protect IoT (Internet of Things) devices, networks, and data. The paper examined the ways highlighted by previous researchers through which Blockchain can afford security to the three problematic areas in IT. Lastly, the paper recommended that future researchers focus on a single Blockchain on which to develop cybersecurity applications to allow for integration and uniformity among solutions.
Energy Efficient Consensus Approach of Blockchain for IoT Networks with Edge Computing

Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.
Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies

About 30%-40% breast cancer patients suffer from recurrence and metastasis, even after targeted therapy like trastuzumab. Since breast cancer recurrence and metastasis are intrinsically related to mortality, it is critical to predict the recurrence and metastasis risk of an individual patient, which is essential for adjuvant therapy and early intervention. In this study, we developed a novel breast cancer recurrence and metastasis risk assessment framework from histopathological images using image features and machine learning technologies. The detection framework was applied on a manually collected clinical dataset from the Cancer Hospital, Chinese Academy of Medical Sciences, consisting of 127 breast cancer patients with known prognostic information; and further independently validated on 88 formalin-fixed, paraffin-embedded (FFPE) samples downloaded from The Cancer Genome Atlas (TCGA) with known recurrence and metastasis status. As a result, the XGBoost-based method performed well using only 8 texture and color features, obtained internal testing AUC of 0.75 on clinical data and external testing AUC of 0.72 on TCGA FFPE data, respectively. In addition, this study found two important potential predictors, i.e., the second moment of the B color component and the detail level mean square error of the wavelet multi-sub-bands co-occurrence matrix. Our study benchmarked the performances of histopathological image features and machine learning technologies in the recurrence and metastasis risk assessment, and holds promise for relieving pathologists' workload and boosting the survival chances of the breast cancer patients.
Gamification of virtual reality assembly training: Effects of a combined point and level system on motivation and training results

Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.
Learning reliable modal weight with transformer for robust RGBT tracking

Many Siamese-based RGBT trackers have been prevalently designed in recent years for fast-tracking. However, the correlation operation in them is a local linear matching process, which may easily lose semantic information required inevitably by high-precision trackers. In this paper, we propose a strong cross-modal model based on transformer for robust RGBT tracking. Specifically, a simple dual-flow convolutional network is designed to extract and fuse dual-modal features, with comparably lower complexity. Besides, to enhance the feature representation and deepen semantic features, a modal weight allocation strategy and a backbone feature extracted network based on modified Resnet-50 are designed, respectively. Also, an attention-based transformer feature fusion network is adopted to improve long-distance feature association to decrease the loss of semantic information. Finally, a classification regression subnetwork is investigated to accurately predict the state of the target. Sufficient experiments have been implemented on the RGBT234, RGBT210, GTOT and LasHeR datasets, demonstrating more outstanding tracking performance against the state-of-the-art RGBT trackers.
A method for satellite time series anomaly detection based on fast-DTW and improved-KNN

In satellite anomaly detection, there are some problems such as unbalanced sample distribution, fewer fault samples, and unobvious anomaly characteristics. These problems cause the extisted anomaly detection methods are difficult to train accurate classification model, and the accuracy of anomaly detection is hard to improve. At the same time, the monitoring data of satellite has high dimension and is difficult to extract effective features. Based on the DTW over-sampling method, this paper realizes the over-sampling of fault samples in satellite time series, and constructs a distributed and balanced time series data set. The Fast-DTW method is applied to calculate the distance between different time series, which can improve the speed of similarity calculation. KNN (K-Nearest Neighbor) method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k. The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.
Manhattan-distance IOU loss for fast and accurate bounding box regression and object detection

Bounding box regression is a crucial step in most object detection algorithms, and directly affects the positioning accuracy and regression speed of convolutional neural networks (CNN). The existing loss functions commonly used in bounding box regression suffer two main disadvantages: firstly, the ln-norm loss does not match the evaluation metric Intersection over Union (IOU), leading to poor regression performance. Second, some recently proposed IOU-based loss functions are beneficial to IOU metric, but the negative effects of some terms in these loss functions on bounding box regression lead to slow convergence and inaccurate regression results. To solve these shortcomings, we proposed a Manhattan-Distance IOU (MIOU) loss function here. It takes into account that the Euclidean distance term in the Complete IOU (CIOU) loss and the Efficient IOU (EIOU) loss is unstable in training due to the huge gradient in the early stage of regression, and the Manhattan distance is added to effectively alleviate this defect. In addition, the denominator of the Euclidean distance term in the two loss functions discussed above has an antagonistic effect on loss reduction, and setting it as a normalized coefficient without participating in backpropagation can effectively improve the convergence speed. The effectiveness of the proposed MIOU loss was verified with designed simulation experiments. Moreover, object detection is usually applied to natural scenes and remote sensing scenes, but the application of detection methods are often limited due to varied image characteristics in different scene settings. We incorporated the MIOU loss into YOLO v4 and other mainstream object detection networks to examine its effectiveness in remote sensing and natural object detection scenarios. The experimental results on real remote sensing datasets DOTA and natural datasets MS COCO demonstrate that the MIOU loss has strong robustness in both remote sensing object detection tasks and natural object detection tasks. In summary, as a general regression loss function, the MIOU loss shows excellent performance in the above two types of scenes.
A Novel CNN-based Bi-LSTM parallel model with attention mechanism for human activity recognition with noisy data

Boosted by mobile communication technologies, Human Activity Recognition (HAR) based on smartphones has attracted more and more attentions of researchers. One of the main challenges is the classification time and accuracy in processing long-time dependent sequence samples with noisy or missed data. In this paper, a 1-D Convolution Neural Network (CNN)-based bi-directional Long Short-Term Memory (LSTM) parallel model with attention mechanism (ConvBLSTM-PMwA) is proposed. The original features of sensors are segmented into sub-segments by well-designed equal time step sliding window, and fed into 1-D CNN-based bi-directional LSTM parallel layer to accelerate feature extraction with noisy and missed data. The weights of extracted features are redistributed by attention mechanism and integrated into complete features. At last, the final classification results are obtained with the full connection layer. The performance is evaluated on public UCI and WISDM HAR datasets. The results show that the ConvBLSTM-PMwA model performs better than the existing CNN and RNN models in both classification accuracy (96.71%) and computational time complexity (1.1 times faster at least), even if facing HAR data with noise.
Offensive language detection in Tamil YouTube comments by adapters and cross-domain knowledge transfer

Over the past few years, researchers have been focusing on the identification of offensive language on social networks. In places where English is not the primary language, social media users tend to post/comment using a code-mixed form of text. This poses various hitches in identifying offensive texts, and when combined with the limited resources available for languages such as Tamil, the task becomes considerably more challenging. This study undertakes multiple tests in order to detect potentially offensive texts in YouTube comments, made available through the HASOC-Offensive Language Identification track in Dravidian Code-Mix FIRE 2021.1 To detect the offensive texts, models based on traditional machine learning techniques, namely Bernoulli Naïve Bayes, Support Vector Machine, Logistic Regression, and K-Nearest Neighbor, were created. In addition, pre-trained multilingual transformer-based natural language processing models such as mBERT, MuRIL (Base and Large), and XLM-RoBERTa (Base and Large) were also attempted. These models were used as fine-tuner and adapter transformers. In essence, adapters and fine-tuners accomplish the same goal, but adapters function by adding layers to the main pre-trained model and freezing their weights. This study shows that transformer-based models outperform machine learning approaches. Furthermore, in low-resource languages such as Tamil, adapter-based techniques surpass fine-tuned models in terms of both time and efficiency. Of all the adapter-based approaches, XLM-RoBERTa (Large) was found to have the highest accuracy of 88.5%. The study also demonstrates that, compared to fine-tuning the models, the adapter models require training of a fewer parameters. In addition, the tests revealed that the proposed models performed notably well against a cross-domain data set.
ConvPatchTrans: A script identification network with global and local semantics deeply integrated

Optical Character Recognition (OCR) system serves the need of reading text from images. Script identification that identifies the language of the text in the image is an important part of OCR technology and an indispensable role in the stability and accuracy of the OCR system. The most challenging for script identification is the interference caused by similarities between texts in different languages. In this paper, a two-branch network named ConvPatchTrans is designed to process global and local semantic features separately, focusing on the text and each word in a picture. The ConvPatchTrans extracts feature from different stages of the Visual Geometry Group network (VGGNet) as global and local semantics. For the global branch, the linear classifier is recommended. For the local branch, text image data is converted to image sequence data. Then, multi-layers convolution-enhanced Transformer (MCET) is proposed to bring about the deep fusion of sequence. Finally, the global and local branches are fused by an adaptive weighted fusion method to get the best result. In order to verify the effectiveness of our proposed method, four public script identification datasets are used for comparative experiments. Our method has obtained the highest values among currently published methods on the CVSI2015 and MLE2E datasets, which are 98.90% and 97.50%, respectively. At the same time, satisfactory results are also obtained on the other two datasets.
A meta‐analysis and systematic review of the effect of virtual reality technology on users' learning outcomes

Abstract With the rapid development of science and technology, virtual reality (VR) technologies have obtained great popularity in education, coupled with many studies on the effect of VR technologies on learning outcomes. However, few studies have systematically summarized their effect on educational outcomes. This study systematically and meta‐analytically examined the effect of VR technologies on educational outcomes using Stata 14.0. The researchers reviewed 43 empirical VR studies in education, ranging from 1993 to 2020, in high‐impact journals of educational technology. Researchers included the subgroups such as VR users' learning outcomes consisting of intellectual skills, cognitive strategies, attitudes, motor skills, and educational levels of participants, as well as countries or regions where the sample was recruited. It is found that VR technologies can positively influence learning outcomes in education, improve learning outcomes across the world except Europe, and facilitate learning outcomes at different educational levels except the primary school level. Future designers and teachers should attempt to stimulate their learning interest in the VR‐based approach by integrating interesting components into VR technologies. Future design of VR‐based approach can also attach enough importance to motivation and satisfaction of learners, construction of VR labs in education, and the impacts of VR on students.
The impact of anthropomorphism on customer satisfaction in chatbot commerce: an experimental study in the food sector

Food retailers are lagging behind other industries in implementing innovative mobile solutions offering their services and purchasing processes on their online platforms. Chatbots can be leveraged as an application to provide customer-centric services while retailers benefit from collecting consumer data. Previous literature on chatbot technology provides evidence that human characteristics enhance the customer experience. This is the first experimental study to investigate consumer attitudes and satisfaction with anthropomorphic chatbots in food e-commerce. A sample of 401 participants was tested to verify the proposed hypotheses. The test group interacted with a standard chatbot without human-like characteristics, while the control group communicated with the anthropomorphically designed agent. The results confirm the vast potential of anthropomorphic cues in chatbot applications and show that they are positively associated with customer satisfaction and mediated by the variables enjoyment, attitude, and trust. The findings suggest that to remain competitive, food retailers should immediately adopt innovative technologies for their omnichannel strategy and incorporate anthropomorphic design cues.