Reviewerly/exHarmony huggingface dataset:
Application of social media in supply chain 4.0 practices: a bibliometric analysis and research trends

In recent years, although scholars and practitioners in the supply chain (SC) field have become increasingly aware of the importance of social media for multipurpose applications, the subject remains unclarified about its functions and outcomes, especially in the 4.0 context. This review paper aims to visualize the big picture and structure of knowledge related to the application of social media in managing supply chains by adopting a bibliometric analysis on a sample of 354 research articles retrieved from the Web of Science during the period between 2008 and 2022 with two widely used bibliometric techniques: co-citation and co-word analysis. We discovered five major clusters of how social media is used in the supply chain context, including (1) in enhancing supply chain sustainability and business transformation, (2) social media analytics as an effective technique to extract business value, (3) in facilitating communication and coordination in the supply chain network, (4) in promoting social interaction and customer relationship management, and (5) in the development of supply chain relationship management. These will enable practitioners to adopt early changes in their SC, allowing them to become competitive and gain market leadership. Supply chain managers can connect with other SC stakeholders and share information through social media to improve operations, establish relationships, reduce risks, and altogether reach for sustainability. Potential research ideas are also offered for future investigation.
Distributed Ledger Technology Based Integrated Healthcare Solution for Bangladesh

Healthcare data is sensitive and requires great protection. Encrypted electronic health records (EHRs) contain personal and sensitive data such as names and addresses. Having access to patient data benefits all of them. This paper proposes a blockchain-based distributed healthcare application platform for Bangladeshi public and private healthcare providers. Using data immutability and smart contracts, the suggested application framework allows users to create safe digital agreements for commerce or collaboration. Thus, all enterprises may securely collaborate using the same blockchain network, gaining data openness and read/write capacity. The proposed application consists of various application interfaces for various system users. For data integrity, privacy, permission and service availability, the proposed solution leverages Hyperledger fabric and Blockchain as a Service. Everyone will also have their own profile in the portal. A unique identity for each person and the installation of digital information centres across the country have greatly eased the process. It will collect systematic health data from each person which will be beneficial for research institutes and health-related organisations. A national data warehouse in Bangladesh is feasible for this application and It is also possible to keep a clean health sector by analysing data stored in this warehouse and conducting various purification algorithms using technologies like Data Science. Given that Bangladesh has both public and private health care, a straightforward digital strategy for all organisations is essential.
UAV remote sensing image stitching via improved VGG16 Siamese feature extraction network

UAV remote sensing image stitching can provide a more comprehensive and continuous area by stitching several overlapping region images. It has been widely used in many fields, such as airborne reconnaissance and surveillance tasks, agriculture production, geological disaster monitoring, and so on. However, traditional feature-based image stitching methods often require hand-crafted features and multiple execution steps, which could extract many redundant feature points in non-overlapping regions and may fail to stitch images with weak textures. To solve this problem, we propose an improved VGG16 Siamese network for the UAV remote sensing image stitching model to achieve end-to-end stitching. The first 13 layers of the VGG16 network were intercepted to form a shared weights feature extraction network, and an improved Squeeze-and-Excitation module was introduced to effectively extract the features of the overlapping areas of the images. A network of regressing affine matrix was designed by using the LeakyReLu activation function to protect the relevant feature maps after feature fine matching, which improves the accuracy of image stitching. Besides, we constructed datasets to train our network: a single UAV remote sensing image was used to form a training image pair based on a limited degree of affine transformation, and the real label of affine transformation between the image pairs was obtained. We compared the SIFT + RANSAC, APAP, and deep homography estimation-based image stitching algorithm on the UAV remote sensing datasets. Experiments show the value of structural similarity(SSIM) increased by 24.1%, and the root mean square error(RMSE) was reduced by 14.69%. Moreover, the proposed stitching model also has good stitching results for UAV remote sensing images as well as weak texture images in subjective visual effects.
AI-based chatbots in conversational commerce and their effects on product and price perceptions

The rise of AI-based chatbots has gradually changed the way consumers shop. Natural language processing (NLP) technology and artificial intelligence (AI) are likely to accelerate this trend further. However, consumers still prefer to engage with humans and resist chatbots, which are often perceived as impersonal and lacking the human touch. While the predominant tendency is to make chatbots appear more humanlike, little is known about how anthropomorphic verbal design cues in chatbots influence perceived product personalization and willingness to pay a higher product price in conversational commerce contexts. In the current work, we set out to test this through one pre-test (N = 135) and two online experiments (N = 180 and 237). We find that anthropomorphism significantly and positively affects perceived product personalization, and that this effect is moderated by situational loneliness. Moreover, the results show that the interaction between anthropomorphism and situational loneliness has an impact on the willingness to pay a higher product price. The research findings can be used for future applications of AI-driven chatbots where there is a need to provide personalized and data-driven product recommendations.
Toxic Comment Classification

Presently in the world social media is ruling among the people. It is used by the people to share the views and contact others. It became the powerful weapon to the people. Users nowadays leave countless comments on various social media platforms, news portals, and forums. Some of the remarks are harmful or aggressive in nature. Because manually moderating comments is impractical due to the large volume of them, most systems rely on machine learning models to detect toxicity automatically. So, to protect people from the illegal comments and fake conversations and toxic words in the comments and posts. We used the Deep Learning models like Convolution Neural Networks (CNNs), LSTMs, to detect the toxic words in the comments based on the toxic percentage. We conclude our work with a detailed list of existing research gaps and recommendations for future research topics connected to the subject of online harmful comment classification.
A target-free video structural motion estimation method based on multi-path optimization

The vibration data are quite important for structural health monitoring (SHM). This paper proposed a novel method, to adaptively estimate video motions of the structure in subpixel accuracy, without attaching any targets. The proposed method includes three steps. In the first step, to remove outliers and simultaneously preserve feature points, the Gaussian range kernel is used along with the Gaussian spatial kernel, and calculated by the polynomial fitting and recursive integrals computing. In the second step, to calculate video pixel motions varied with spatial coordinates in the region of interest (ROI) for testing, the ROI is divided into multiple grid cells. Motions in each grid cell are modeled as local spatially-variant homography matrices, and their spatial consistency are enhanced by a shape-preserving constraint. The third step is to enhance both spatial and temporal correlations of the calculated homography matrices, achieved by the data term and the smoothness term in both space and time domains. The superiority of the proposed method over traditional methods was validated in several case studies for analyzing structural motions. Among the comparisons, the proposed method can produce image denoising, camera motions, structural motions, and structural modal information in subpixel accuracy, and with the best accuracy.
Privacy Protection of Digital Images Using Watermarking and QR Code-based Visual Cryptography

The increase in information sharing in terms of digital images imposes threats to privacy and personal identity. Digital images can be stolen while in transfer and any kind of alteration can be done very easily. Thus, privacy protection of digital images from attackers becomes very important. Encryption, steganography, watermarking, and visual cryptography techniques to protect digital images have been proposed from time to time. The present paper is focused on the enhancement of privacy protection of digital images utilizing watermarking and a QR code-based expansion-free and meaningful visual cryptography approach which generates visually appealing QR codes for transmitting meaningful shares. The original secret image is processed with a watermark image (copyright logo, signature, and so on), and then halftoning of the watermarked image has been done to limit pixel expansion. Then, the halftoned image has been partitioned using VC into two shares. These shares are embedded with a QR code to make the shares meaningful. Lossless compression has been performed on the QR code-based shares. The compression method employed in visual cryptography would save space and time. The proposed approach keeps the beauty of visual cryptography, i.e., computation-free decryption, and the size of the recovered image the same as the original secret image. The experimental results confirm the effectiveness of the proposed approach.
Blockchain’s Scope and Purpose in Carbon Markets: A Systematic Literature Review

Carbon markets, particularly emission trading schemes (ETS) and carbon offset projects, are significant mechanisms in climate change mitigation. However, there are still a number of unresolved issues regarding their attractiveness and efficient functioning. Blockchain, as the core of “3D’s concept” (including decentralization, decarbonization and digitalization), could be considered as a candidate solution for carbon markets’ improvement. A systematic literature review was conducted to identify the role of blockchain in ETS and carbon offset projects, its key features, implementation challenges and proposed applications, by analyzing and discussing the content of relevant studies, and grouping the results into domains. This study’s findings show that blockchain has great potential to be adopted in carbon markets. However, there is no data on blockchain use cases in energy efficiency, chemical processes and industrial manufacturing, waste disposal, and agriculture. Blockchain-based household and transportation carbon offset projects are linked to renewables through energy trading. Renewables and forestry are the most appropriate domains for blockchain adoption, considering various criteria of quality for carbon offset projects. Blockchain is currently immature in carbon markets because of its own drawbacks and challenges. This study also highlights research gaps and offers research directions to inspire researchers for conducting related investigations.
Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent

This study describes an applied and enhanced real-time vehicle-counting system that is an integral part of intelligent transportation systems. The primary objective of this study was to develop an accurate and reliable real-time system for vehicle counting to mitigate traffic congestion in a designated area. The proposed system can identify and track objects inside the region of interest and count detected vehicles. To enhance the accuracy of the system, we used the You Only Look Once version 5 (YOLOv5) model for vehicle identification owing to its high performance and short computing time. Vehicle tracking and the number of vehicles acquired used the DeepSort algorithm with the Kalman filter and Mahalanobis distance as the main components of the algorithm and the proposed simulated loop technique, respectively. Empirical results were obtained using video images taken from a closed-circuit television (CCTV) camera on Tashkent roads and show that the counting system can produce 98.1% accuracy in 0.2408 s.
Artificial Neural Network Based Effective Detection of Breast Cancer By Using Mammogram Data

Progressive MS lesion formation is a primary cause of cognitive decline and physical impairment, making MS one of the major disorders. The rate of disease progression and the efficacy of treatments depend critically on having a quick and accurate method for assessing the number and size of MS lesions in the brain. The position, size, morphological variation, intensity resemblance with normal brain tissues, and inter-subject anatomical variance of MS lesions in brain MRI make precise diagnosis, characterisation, and quantification exceedingly challenging. In this paper, we explored a technique for detecting and segmenting MS lesions in which the fundamental processes are background creation and binarization with a global threshold. To link the normal tissues close to the margins, we perform a three-phase level set and then add a third-phase segmented region using a contour of the brain. To create an adaptable setting, we must first eliminate lesions from the brain, saving only the brain's largest linked area and the corpus callosum. Selecting the threshold using entropy and standard deviation via the binarization approach is followed by non-gamut image enhancement. The proposed method detects lesions in the same locations as a radiologist and with the same precision in terms of size and number. As a result of its flexibility, the suggested method has a range of applications in clinical practise for identifying MS lesions in MRI. When compared to more contemporary methods, the proposed method has a smaller margin of error and higher accuracy.
ChainDiscipline - Towards a Blockchain-IoT-Based Self-Sovereign Identity Management Framework

In today's complex Internet platform, online users need help to protect their online identity. Only sometimes, websites are very transparent about how user data will be collected, stored and processed by them. Sometimes Internet entities collect more online user information than required. These entities often share user identity-related data with third parties without consent. Existing traditional identity schemes need to be improved to stop and counter new ways of digital identity theft and fraud. Blockchain is a promising technology to strengthen the preservation of online users’ digital identity due to its decentralised nature and robust data security features. In this paper, we proposed and implemented a generic blockchain-IoT-based self-sovereign identity management framework called ChainDiscipline. We have demonstrated the framework's operability and functionality by implementing healthcare and smart home data management-based use cases.
Quantum Machine Learning—An Overview

Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning.
DeepVulSeeker: A novel vulnerability identification framework via code graph structure and pre-training mechanism

Software vulnerabilities can pose severe harms to a computing system. They can lead to system crash, privacy leakage, or even physical damage. Correctly identifying vulnerabilities among enormous software codes in a timely manner is so far the essential prerequisite to patch them. Unfortunately, the current vulnerability identification methods, either the classic ones or the deep-learning-based ones, have several critical drawbacks, making them unable to meet the present-day demands put forward by the software industry. To overcome the drawbacks, in this paper, we propose DeepVulSeeker, a novel fully automated vulnerability identification framework, which leverages both code graph structures and the semantic features with the help of the recently advanced Graph Representation Self-Attention and pre-training mechanisms. Our experiments show that DeepVulSeeker not only reaches an accuracy as high as 0.99 on traditional CWE datasets, but also outperforms all other existing methods on two highly-complicated datasets. We also testified DeepVulSeeker based on three case studies, and found that DeepVulSeeker is able to understand the implications of the vulnerabilities. We have fully implemented DeepVulSeeker and open-sourced it for future follow-up research.
FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction

Monitoring the health status of patients in the Intensive Care Unit (ICU) is a critical aspect of providing superior care and treatment. The availability of large-scale electronic health records (EHR) provides machine learning models with an abundance of clinical text and vital sign data, enabling them to make highly accurate predictions. Despite the emergence of advanced Natural Language Processing (NLP) algorithms for clinical note analysis, the complex textual structure and noise present in raw clinical data have posed significant challenges. Coarse embedding approaches without domain-specific refinement have limited the accuracy of these algorithms. To address this issue, we propose FineEHR, a system that utilizes two representation learning techniques, namely metric learning and fine-tuning, to refine clinical note embeddings, while leveraging the intrinsic correlations among different health statuses and note categories. We evaluate the performance of FineEHR using two metrics, namely Area Under the Curve (AUC) and AUC-PR, on a real-world MIMIC III dataset. Our experimental results demonstrate that both refinement approaches improve prediction accuracy, and their combination yields the best results. Moreover, our proposed method outperforms prior works, with an AUC improvement of over 10%, achieving an average AUC of 96.04% and an average AUC-PR of 96.48% across various classifiers.
Fuzzy Logic-based Steganographic Scheme for high Payload Capacity with high Imperceptibility

When communicating confidential information between two parties, it is crucial to consider the security of the information. Cryptography, as well as steganography, are essentially used to keep hidden, secret data. Cryptography mainly focuses on changing private messages into an unreadable format. However, cryptography is questionable because it shows the existence of covert communication; hence there is a need to use it combined with other more secure techniques. On the other hand, steganography conceals confidential data in a cover media to avoid suspicion of covert communication. Most practitioners use different steganographic algorithms selected based on their capacity to carry large payloads with a quality cover image. This paper introduces a new algorithm to hide confidential data using fuzzy logic in digital images with increased payload and high imperceptibility. We use fuzzy logic to identify an image’s edges, which shows the level with which a pixel is attached to any edge during image processing; then, we apply a factor to the data to hide before embedding it to the pixels with low values. Our method effectively conceals large amounts of secret bits while producing a quality stego image that ensures confidentiality in communication through the insecure public network. Experimental results demonstrate the outperformance of our method over the existing schemes for both the peak signal-to-noise ratio and the payload size.
Physical Activity in Immersive Virtual Reality: A Scoping Review

Physical activity has benefits for health, but many adolescents are inactive. However, video games such as Immersive Virtual Reality (IVR) have grown in popularity as a leisure activity among young people, allowing them to manipulate objects in virtual environments increasing the practice of physical activity. The evidence indicates that the interest in physical activity through IVR is greater than in traditional methods, and different experiences have been reported. However, few studies indicate the sample evaluated, the effects found, or the IVR instruments used. Due to this, is the aim of this study is to identify the publications referring to IVR and physical activity, characterize them, and present the obtained main findings. For this, the guidelines described in the PRISMA-ScR for scoping reviews were applied. After the use of the inclusion and exclusion criteria, eight articles were included. Results show evidence regarding physiological outcomes, perceptual variables, interest and enjoyment, and psychological effects regarding physical activity through IVR. Additionally, the use of different devices and their prescriptions are explored. It is concluded that there is interest from the scientific community for the practice of physical activity through IVR, as well as for its application for the maintenance of active habits. This is important as it positions IVR as a method that can be a more experiential and effective way to develop and maintain a healthy lifestyle.