Reviewerly/exHarmony huggingface dataset:
Real-Time Object Detection Network in UAV-Vision Based on CNN and Transformer

Unmanned aerial vehicles (UAVs) play an important role in conducting automatic patrol inspections of cities, which can ensure the safety of urban residents’ life and property and the normal operation of cities. However, during the inspection process, problems may arise. For example, numerous small objects in UAV images are difficult to detect, objects in UAV images are severely occluded, and requirements for real-time performances are posed. To address these issues, we first propose a real-time object detection network (RTD-Net) for UAV images. Besides, to deal with the lack of visual features of small objects, we design a feature fusion module (FFM) to interact and fuse features at different levels and improve the feature expression ability of small objects. To achieve real-time detection, we design a lightweight feature extraction module (LEM) to build the backbone network to control the calculation quantity and parameters. To solve the issue of discontinuous features of occluded objects, an efficient convolutional transformer block (ECTB)-based convolutional multihead self-attention (CMHSA) is designed to improve the recognition ability of occluded objects by extracting the context information of objects. Compared with multihead self-attention (MHSA) in the traditional transformer, CMHSA uses convolutional projection to replace the position-linear projection, which can reduce a large amount of calculation without performance loss. Finally, an attention prediction head (APH) is designed based on the attention mechanism to improve the ability of the model to extract attention regions in complex scenarios. The proposed method reaches a detection accuracy of 86.4% mean average precision (mAP) in our UAV image dataset. In addition, it achieves a detection accuracy of 86.0% mAP and a detection speed of 33.4 frames/s in the NVIDIA Jeston TX2 embedded device.
Reliable Mutual Distillation for Medical Image Segmentation Under Imperfect Annotations

Convolutional neural networks (CNNs) have made enormous progress in medical image segmentation. The learning of CNNs is dependent on a large amount of training data with fine annotations. The workload of data labeling can be significantly relieved via collecting imperfect annotations which only match the underlying ground truths coarsely. However, label noises which are systematically introduced by the annotation protocols, severely hinders the learning of CNN-based segmentation models. Hence, we devise a novel collaborative learning framework in which two segmentation models cooperate to combat label noises in coarse annotations. First, the complementary knowledge of two models is explored by making one model clean training data for the other model. Secondly, to further alleviate the negative impact of label noises and make sufficient usage of the training data, the specific reliable knowledge of each model is distilled into the other model with augmentation-based consistency constraints. A reliability-aware sample selection strategy is incorporated for guaranteeing the quality of the distilled knowledge. Moreover, we employ joint data and model augmentations to expand the usage of reliable knowledge. Extensive experiments on two benchmarks showcase the superiority of our proposed method against existing methods under annotations with different noise levels. For example, our approach can improve existing methods by nearly 3% DSC on the lung lesion segmentation dataset LIDC-IDRI under annotations with 80% noise ratio. Code is available at: https://github.com/Amber-Believe/ReliableMutualDistillation.
Developing an anti-counterfeit system using blockchain technology

Blockchain is an up-and-coming technology designed to ease the process of verifying legitimate products without the need of a centralized system. An example of a well-known technology using Blockchain is Bitcoin and Ethereum cryptocurrency. Blockchain technology will ensure that data residing within each block cannot be tampered with by anyone other than the owner. This paper uses Blockchain technology to develop a system where customers can validate a product legitimacy without the need of a corresponding merchant. This system will be able to be used by manufacturers and companies to ensure that their products will be harder to counterfeit, therefore they will gain more trust from customers. The Ethereum blockchain is used to build the proposed model that is capable of tracing every item's creation and transactions, ensuring the credibility of an item's genuineness. Our simulation results in an effective model and a cost analysis that shows that the model uses an average gas amount of 715,046.3 gwei.
You Can’t Hide Behind Your Headset: User Profiling in Augmented and Virtual Reality

Augmented and Virtual Reality (AR and VR), collectively known as Extended Reality (XR), are increasingly gaining traction thanks to their technical advancement and the need for remote connections, recently accentuated by the pandemic.Remote surgery, telerobotics, and virtual offices are only some examples of their successes.As users interact with XR, they generate extensive behavioral data usually leveraged for measuring human activity, which could be used for profiling users' identities or personal information (e.g., gender).However, several factors affect the efficiency of profiling, such as the technology employed, the action taken, the mental workload, the presence of bias, and the sensors available.To date, no study has considered all of these factors together and in their entirety, limiting the current understanding of XR profiling.In this work, we provide a comprehensive study on user profiling in virtual technologies (i.e., AR, VR).Specifically, we employ machine learning on behavioral data (i.e., head, controllers, and eye data) to identify users and infer their individual attributes (i.e., age, gender).Toward this end, we propose a general framework that can potentially infer any personal information from any virtual scenarios.We test our framework on eleven generic actions (e.g., walking, searching, pointing) involving low and high mental loads, derived from two distinct use cases: an AR everyday application (34 participants) and VR robot teleoperation (35 participants).Our framework limits the burden of creating technology-and actiondependent algorithms, also reducing the experimental bias evidenced in previous work, providing a simple (yet effective) baseline for future works.We identified users up to 97% F1-score in VR and 80% in AR.Gender and Age inference was also facilitated in VR, reaching up to 82% and 90% F1-score, respectively.Through an in-depth analysis of sensors' impact, we found VR profiling resulting more effective than AR mainly because of the eye sensors' presence.
Differentiable Multi-Granularity Human Parsing

In this work, we study the challenging problem of instance-aware human body part parsing. We introduce a new bottom-up regime which achieves the task through learning category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. The output is a compact, efficient and powerful framework that exploits structural information over different human granularities and eases the difficulty of person partitioning. Specifically, a dense-to-sparse projection field, which allows explicitly associating dense human semantics with sparse keypoints, is learnt and progressively improved over the network feature pyramid for robustness. Then, the difficult pixel grouping problem is cast as an easier, multi-person joint assembling task. By formulating joint association as maximum-weight bipartite matching, we develop two novel algorithms based on projected gradient descent and unbalanced optimal transport, respectively, to solve the matching problem differentiablly. These algorithms make our method end-to-end trainable and allow back-propagating the grouping error to directly supervise multi-granularity human representation learning. This is significantly distinguished from current bottom-up human parsers or pose estimators which require sophisticated post-processing or heuristic greedy algorithms. Extensive experiments on three instance-aware human parsing datasets (i.e., MHP-v2, DensePose-COCO, PASCAL-Person-Part) demonstrate that our approach outperforms most existing human parsers with much more efficient inference. Our code is available at https://github.com/tfzhou/MG-HumanParsing.
An Adaptive Batch Size-Based-CNN-LSTM Framework for Human Activity Recognition in Uncontrolled Environment

Human activity recognition (HAR) is a process of identifying the daily living activities of an individual using a set of sensors and appropriate learning algorithms. Most of the works on HAR are done using a mix of sensor data that is collected in a simulated environment, and due to that, the real-time recognition suffers. This article proposes an efficient adaptive batch size-based-CNN-LSTM model for recognizing different human activities in an uncontrolled environment. It uses adaptive batch sizes from 128 to 1024 for iterative model training and validation. The proposed model can handle imbalanced classes and un-normalized data efficiently. A state-of-art HAR dataset is also generated in an open environment to get the activity data of an uncontrolled scenario. With minimal data preprocessing and data augmentation, the model is tested, and the proposed model managed to get the highest accuracy of 99.29% with an average loss of 0.08 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.136%. The presented model is also tested with two public datasets named- mHealth and MotionSense and achieved an accuracy of 99.5% and 99.8%, respectively. The proposed model outperforms all the previous benchmarks and approaches by a good margin.
Ensemble Machine Learning Paradigms in Software Defect Prediction

Predicting faults in software aims to detect defects before the testing phase, allowing for better resource allocation and high-quality software development, which is a requisite for any organization. Machine learning techniques aid in the resolution of such issues and a variety of predictive models are being developed to categorize the software into defective modules and the one which is non-defective ones. Though applying these advanced machine learning techniques results in better utilization of time and other resources, there is still poor prediction as reported in many studies. This is because of several challenges that block defective software data, including redundancy, correlation, feature irrelevance, missing samples, and an imbalanced distribution between the faulty and non-faulty classes. Ensemble Machine learning has been adopted by practitioners and researchers globally to deal with such problems, and it is proven to demonstrate some improvement in defect prediction performance. In this review paper, all ensemble-based machine learning techniques developed for software defect prediction from 2018 to 2021 have been critically analyzed. The nucleus of this paper is to get a deep insight into why the various hybrid models still suffer from poor performance on the available datasets. A detailed review with a focus on multiple perspectives viz. faulty and non-defective datasets, performance evaluation criteria, and machine learning techniques have revealed certain gaps that can be addressed by developing more robust hyperparameter optimization algorithms, feature engineering, developing stacking and averaging models.
Collaborative Filtering with Temporal Features for Movie Recommendation System

Nowadays, recommender systems play a vital role in every human being's life due to the time retrieving the items. The matrix factorization (MF) technique is one of the main methods among collaborative filtering (CF) techniques that have been widely used after the Netflix competition. Traditional MF techniques are static in nature. However, the perception and popularity of products are constantly changing with time. Similarly, the users’ tastes are changed with time. Hence, traditional MF cannot handle the dynamic effect of the user-item interaction. To tackle the temporal and dynamic effect of user-item interaction, we proposed a collaborative filtering model for movie recommendations that include temporal effects. To justify the significance of the proposed technique, we evaluated our model on a standard dataset (Movielens) and compared it with state-of-art models. The exploratory outcomes signify that the proposed technique obtains a better result than a state-of-art model with an improvement of 1.35% and 1.28% on ML-100K and 1M datasets, respectively.
Detecting and prioritizing product defects using social media data and the two-phased QFD method

Recently, social media data have been leveraged for product defect discovery. But the considerable number of defects reflected via social media inhibits manufacturers from solving product defects promptly. Extant studies focus on identifying defect-relevant texts and then deriving defects discussed in texts. They omit to assess the importance of discovered defects and find the defects with high priorities. In this study, we first developed a topic model named Defect Analysis Model (DAM) to discover product defects from defect-related texts, which are identified by the integrated BERT and Random Forest. Then we propose the Two-Phased Quality Function Deployment for Defect (TPQFDD) to prioritize discovered product defects. With the consideration of defect frequencies and defect costs, TPQFDD evaluates the importance of defective components, defects, and defect causes for more inspired managerial insights. We compare our approaches with baseline approaches using an online thread dataset of automobiles. Comparison results prove that our methods effectively detect and prioritize product defects that occurred in the aftersales stage.
A personalized recommendation framework based on MOOC system integrating deep learning and big data

Finding the courses that users are interested in quickly in the massive data can make a very important contribution to the accurate dissemination of knowledge. In this paper, we integrate the deep learning and big data technology to investigate a personalized recommendation method based on Massive Open Online Course (MOOC) system. Based on the Bidirectional Encoder Representations from Transformers (BERT) model, we propose some corresponding strategies to improve the accuracy of the recommendation system. First, we introduce the acquisition and preprocessing of the open dataset. Second, we design a recommendation model framework by taking advantage of the BERT model and incorporating a self-attention mechanism. Finally, to obtain deep feature information between course texts, we design a domain feature difference learning strategy to improve the model's recommendation performance. The results of our experiments prove that the proposed model in this paper performs good recommendation results compared with other methods.
Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.
Adoption and Applications of Blockchain Technology in Marketing: A Retrospective Overview and Bibliometric Analysis

Blockchain technology has disrupted the traditional approaches of marketing and introduced altogether contemporary marketing frameworks using its unique capabilities of decentralization, security and transparency. In recent times, this technology has attracted both academicians and practitioners to study the underlying processes and opportunities of the application of blockchain technology in marketing. Although there is an increasing deliberation in researching the use of blockchain in marketing, there exists an intermittent gap in holistic overview of the current trends and future boundaries. This article is an attempt to present a comprehensive current state and prospects of expansion in the research of the application of blockchain technology in marketing using state of the art bibliometric review analysis. The bibliometric analysis using 161 articles from Scopus database revealed the influential aspects of research such as prolific authors, influential documents, countries, affiliations, sources and keywords. Moreover, emerging research streams about the application of blockchain is identified and enumerated using network synthesis and visualization of co-citation, and keywords co-occurrence networks. These findings pave the ways for future research expansion. The results shows that the research on applications of blockchain technology has been maturing over time. However, the research streams reveal that blockchain based marketing framework is still in its infancy stage.
Low-Illumination Image Enhancement Based on Deep Learning Techniques: A Brief Review

As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. Nevertheless, recent advances in this area are dominated by deep-learning-based solutions, and consequently, various deep neural networks have been proposed and applied to this field. Therefore, this paper briefly reviews the latest low-illumination image enhancement, ranging from its related algorithms to its unsolved open issues. Specifically, current low-illumination image enhancement methods based on deep learning are first sorted out and divided into four categories: supervised learning methods, unsupervised learning methods, semi-supervised learning methods, and zero-shot learning methods. Then, existing low-light image datasets are summarized and analyzed. In addition, various quality assessment indices for low-light image enhancement are introduced in detail. We also compare 14 representative algorithms in terms of both objective evaluation and subjective evaluation. Finally, the future development trend of low-illumination image enhancement and its open issues are summarized and prospected.
Being seen… by human or machine? Acknowledgment effects on customer responses differ between human and robotic service workers

Intelligent machines are steadily replacing human workforce in retail and service. Although customers' short-term responses toward service robots are well understood, little is known about the potential long-term effects of replacing humans with robots in these settings. Drawing on social perception theory, the Stimulus–Organism–Response (SOR) model, and speciesism theory, the current research examines whether and how human and robotic service workers influence customers' loyalty responses depending on whether these living and nonliving entities acknowledge customers. Across three experiments and a series of different shopping settings (N = 1541), we expose customers to human (vs. robotic) service workers who do (vs. do not) acknowledge them through eye contact. A human (vs. robotic) acknowledgment was found to result in significantly more favorable loyalty responses; an effect mediated by the perceived warmth and, to a lesser extent, the perceived competence of the service workers. These findings hold across embarrassing (e.g., shopping for lingerie or sex toys) and non-embarrassing (e.g., shopping for shoes) settings, offering generalizable insights for managers and scholars concerned with the outcomes of implementing innovative human-centric technologies in various consumption contexts.
Artificial intelligence based real-time earthquake prediction

Earthquake prediction is considered a vital endeavour for human safety. Effective earthquake prediction can drastically reduce human damage, which is of utmost importance to the community and individuals. In the current research world, there is a boom in scientific interest in the prediction of seismic events. With the technological revolution in data acquisition, communication networks, edge–cloud computing, the Internet of Things (IoT), and big data analysis, it is feasible to develop an intelligent earthquake prediction model for early warnings at vulnerable locations. Conspicuously, a collaborative IoT–Edge-centered smart earthquake monitoring and prediction framework using cloud and edge computing are proposed. IoT technology is utilized to acquire real-time sensor data, which is forwarded to the edge layer for feature classification utilizing a novel bayesian belief model technique. Furthermore, Adaptive Neuro-Fuzzy Inference System (ANFIS) mechanism is employed to forecast the magnitude of earthquakes in the cloud layer. Based on the experimental simulation, enhanced effectiveness is acquired for the presented framework in terms of classification performance (Precision (92.52%), Sensitivity (91.72%), and Specificity (91.01%)). Additionally, results show that the utilization of edge computing significantly reduces computational delay (23.06s). Moreover, enhanced accuracy and throughput are acquired for the presented model in terms of reliability (95.26%) and stability (92.16%).