Reviewerly/exHarmony huggingface dataset:
Revolutionizing education sector by leveraging blockchain technology: State of art

Blockchain Technology has as of late got a great deal of interest from specialists and professionals. This is generally attributable to its particular qualities, which incorporate decentralization, security, trustworthiness, and information honesty. Regardless of this rising interest, little is had some familiarity with the current degree of information and practice with regards to the utilization of blockchain innovation in instruction. This paper presents an exhaustive outline of examination on blockchain-based instructive applications. Its concentrations on three primary themes: (1) educational applications manufactured using blockchain technology, (2) the benefits that blockchain technology might offer to education, and (3) the obstacles of implementing blockchain in education. For each subject, a full analysis of the data was performed, as well as an in-depth discussion based on the findings. Other educational fields that might benefit from blockchain technology are also discussed in this report. The present article focused on blockchain technology's potential educational uses and looked at how it may be used to address certain educational concerns. Before looking into some of the current educational blockchain applications, this article first examined the capabilities and benefits of blockchain technology. The merits and downsides of applying blockchain technology in education were discussed, as well as several unique uses of blockchain technology.
Single Image Dehazing Through Feed Forward Artificial Neural Network

Due to light scattering by air particles, images taken under bad lighting conditions (such as haze, fog, mist, or smog) have a reduced level of visibility. One blurry image can be made visible again using single image dehazing techniques. Due to the ill-posedness of the Single image dehazing problem, it is difficult to solve. Even though, the Dark channel prior (DCP) has been the most prevalent method for image dehazing algorithms, but it suffers from huge computational time and picture quality. The accurate transmission map estimation is one of the best preferred ways to achieve the least computational time and high picture quality in DCP. Hence, this work is focused on constructing transmission map of a hazy image based on feed forward artificial neural network. The recommended method uses a feed forward ANN to conduct the transmission map straight from the minimum channel and a normalizing procedure to increase the re-covered image information. With a training set of eighty images, the network is trained by means of mean square error (MSE). The proposed method utilizing peak signal to noise ratio (PSNR) and structural similarity (SSIM) index measures are used to assess the restoration quality. The investigational conclusions have demonstrated that, the suggested method outperforms the dehazing of an input image without degrading visible quality (PSNR = 69.16, SSIM index = 0.8913). In addition, the proposed method is suited for real-time applications given the average computing time it achieves is 1.03 s.
Visual Question Generation From Remote Sensing Images

Visual question generation (VQG) is a fundamental task in vision-language understanding that aims to generate relevant questions about the given input image. In this paper, we propose a paragraph-based VQG approach for generating intelligent questions in natural language about remote sensing (RS) images. Specifically, our proposed framework consists of two transformer-based vision and language models. First, we employ a swin-transformer encoder to generate a multi-scale representative visual feature from the image. Then, this feature is used as a prefix to guide a generative pre-trained transformer-2 (GPT-2) decoder in generating multiple questions in the form of a paragraph to cover the abundant visual information contained in the RS scene. To train the model, the language decoder is fine-tuned on RS dataset to generate a set of relevant questions from the RS image. We evaluate our model on two visual question-answering (VQA) datasets in RS. Additionally, we construct a new dataset termed TextRS-VQA for better evaluation for our VQG model. This dataset consists of questions completely annotated by humans which addresses the high redundancy of the questions in prior VQA datasets. Extensive experiments using several accuracy and diversity metrics demonstrate the effectiveness of our proposed VQG model in generating meaningful, valid, and diverse questions from RS images. The TextRS-VQA dataset is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yakoubbazi/TextRS</uri> .
Information Technology, Inequality, and Adult Literacy in Developing Countries

Abstract The study assesses linkages between information technology, inequality, and adult literacy in 57 developing countries for the period 2012–2016. Income inequality is measured with the Gini coefficient while six dynamics of information technology are taken on board, namely use of a virtual social network, Internet access in schools, Internet penetration, mobile phone penetration, fixed broadband subscription, and a number of personal computer users. The empirical evidence is based on interactive Tobit regressions. The findings show that only Internet access in schools unconditionally promotes adult literacy. The corresponding inequality threshold that should not be exceeded for Internet access in schools to continue promoting adult literacy is 0.739 of the Gini coefficient. Policy implications are discussed.
Endoscopic ultrasound diagnosis system based on deep learning in images capture and segmentation training of solid pancreatic masses

Early detection of solid pancreatic masses through contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) is important. But CH-EUS is difficult to learn.To design a deep learning-based CH-EUS diagnosis system (CH-EUS MASTER) for real-time capture and segmentation of solid pancreatic masses and to verify its value in the training of pancreatic mass identification under endoscopic ultrasound (EUS).We designed a real-time capture and segmentation model for solid pancreatic masses and then collected 4530 EUS images of pancreatic masses retrospectively, used for training and testing of this model at a ratio of 8:2. The model is loaded into the EUS host computer to establish the CH-EUS MASTER system. A crossover trial was then conducted to evaluate the model's value in EUS trainee training by successfully conducting two groups of EUS trainees in model learning and trainer-guided training. The intersection over union (IoU) and the time to find the lesion were used to evaluate the model performance metrics, and the Mann-Whitney test was used to compare the IoU and the time to find the lesion in different groups of subjects. Paired t-test was used to compare the effects before and after training. When α ≤ 0.05, it is considered to have a significant statistical difference.The model test showed that the model successfully captured and segmented the pancreatic solid mass region in 906 EUS images. The real-time capture and segmentation model had a Dice coefficient of 0.763, a recall rate of 0.941, a precision rate of 0.642, and an accuracy of 0.842 (when the threshold is set to 0.5), and the median IoU of all cases was 0.731. For the AI training effect, the average IoU of eight trainees improved from 0.80 to 0.87 (95% CI, 0.032-0.096; p = 0.002). The average time for identifying lesions in the pancreatic body and tail improved from 22.75 to 17.98 s (95% CI, 3.664-5.886; p < 0.01). The average time for identifying lesions in the pancreatic head and uncinate process improved from 34.21 to 25.92 s (95% CI, 7.661-8.913; p < 0.01).CH-EUS MASTER can provide an effect equivalent to trainer guidance in training pancreatic solid mass identification and segmentation under EUS.
Joint-phase attention network for breast cancer segmentation in DCE-MRI

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in the screening and treatment evaluation of high-risk breast cancer. The segmentation of cancerous regions is an essential step for the comprehensive analysis of breast MRI. Nevertheless, automatic and robust segmentation is still very challenging because of the large differences of cancers in size, morphology, and intensity appearance. To alleviate these issues, we propose a simple yet effective two-stage approach, which simultaneously exploits pre- and post-contrast enhanced information to segment the breast cancer. In particular, we first offer a breast segmentation network to predict the breast region of interest (ROI) and therefore excluding confounding information from thorax region in the whole MRI scans. Moreover, inspired by the radiologists' examination routine which takes full advantage of the MRI sequences to make the diagnosis, we suggest a joint-phase attention network in order to mine both pre- and post-contrast representations for the segmentation of cancerous regions. The accuracy and generalizability of the proposed network is validated on our collected DCE-MRI dataset containing 550 subjects (with 748 biopsy-proven breast cancers) from 3 different centers (one as internal dataset and two as external datasets). The primary evaluation metrics are Dice similarity coefficient (Dice), Jaccard index (Jaccard), and average symmetric surface distance (ASSD). Our network consistently achieves satisfactory segmentation results, by generating an average Dice of 88.77%/82.77%/83.03%, Jaccard of 81.27%/71.89%/73.23%, and ASSD of 2.21/3.63/2.69, on one internal and two external datasets, respectively. Our method offers an effective cancer segmentation approach for the breast DCE-MRI examination. The code is publicly available at https://github.com/ryandok/JPA.
DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

Abstract Health monitoring, rehabilitation, and fitness are just a few domains where human activity recognition can be applied. In this study, a deep learning approach has been proposed to recognise ambulation and fitness activities from data collected by five participants using smart insoles. Smart insoles, consisting of pressure and inertial sensors, allowed for seamless data collection while minimising user discomfort, laying the baseline for the development of a monitoring and/or rehabilitation system for everyday life. The key objective has been to enhance the deep learning model performance through several techniques, including data segmentation with overlapping technique (2 s with 50% overlap), signal down-sampling by averaging contiguous samples, and a cost-sensitive re-weighting strategy for the loss function for handling the imbalanced dataset. The proposed solution achieved an Accuracy and F1-Score of 98.56% and 98.57%, respectively. The Sitting activities obtained the highest degree of recognition, closely followed by the Spinning Bike class, but fitness activities were recognised at a higher rate than ambulation activities. A comparative analysis was carried out both to determine the impact that pre-processing had on the proposed core architecture and to compare the proposed solution with existing state-of-the-art solutions. The results, in addition to demonstrating how deep learning solutions outperformed those of shallow machine learning, showed that in our solution the use of data pre-processing increased performance by about 2%, optimising the handling of the imbalanced dataset and allowing a relatively simple network to outperform more complex networks, reducing the computational impact required for such applications.
Disentangling interest and conformity for eliminating popularity bias in session-based recommendation

Session-based recommendation (SBR) is to predict the next item, given an anonymous interaction sequence. Recently, many advanced SBR models have shown great recommending performance, but few studies note that they suffer from popularity bias seriously: the model tends to recommend popular items and fails to recommend long-tail items. The only few debias works relieve popularity bias indeed. However, they ignore individual’s conformity toward popular items and thus decrease recommending performance on popular items. Besides, conformity is always entangled with individual’s real interest, which hinders extracting one’s comprehensive preference. To tackle the problem, we propose an SBR framework with Disentangling InteRest and Conformity for eliminating popularity bias in SBR. In this framework, two groups of item encoders and session modeling modules are devised to extract interest and conformity, respectively, and a fusion module is designed to combine these two types of preference. Also, a discrepancy loss is utilized to disentangle the representation of interest and conformity. Besides, our devised framework can integrate with several SBR models seamlessly. We conduct extensive experiments on three real-world datasets with four advanced SBR models. The results show that our framework outperforms other state-of-the-art debias methods consistently.
Designing a Secure E Voting System Using Blockchain with Efficient Smart Contract and Consensus Mechanism

These days, most people are not satisfied with the final result of the voting system. This is because the current system for voting is centralized and fully controlled by the election commission. So, there is a chance that the central body can be compromised or hacked and the final result can be tampered. In this direction, a decentralized, Blockchain and Internet of Things (IoT) based methodology for voting system is devised and presented in this paper. The efficient smart contracts and consensus mechanism are applied in the proposed system to enhance the security. Blockchain is totally transparent, secured and immutable technique because it uses concept like encryption, decryption, hash function, consensus and Merkle tree etc. which make Blockchain Technology an appropriate platform for storing and sharing the data in a secured and anonymous manner. IoT makes use of biometric sensors using which people can cast their votes in not only physical mode but also in digital mode. As a response, a message is received to the owner for casting his vote to ensure the authentication. In this way, we can make the present voting system more secure and trust-worthy using the properties of both Blockchain and IoT, and therefore, we can give more value to the election process in the democratic countries. The proposed method ensures security as well as reduces the computational time as compared to the existing approaches.
Understanding Adoption Barriers to Dwell-Free Eye-Typing: Design Implications from a Qualitative Deployment Study and Computational Simulations

Eye-typing is a slow and cumbersome text entry method typically used by individuals with no other practical means of communication. As an alternative, prior HCI research has proposed dwell-free eye-typing as a potential improvement that eliminates time-consuming and distracting dwell-timeouts. However, it is rare that such research ideas are translated into working products. This paper reports on a qualitative deployment study of a product that was developed to allow users access to a dwell-free eye-typing research solution. This allowed us to understand how such a research solution would work in practice, as part of users' current communication solutions in their own homes. Based on interviews and observations, we discuss a number of design issues that currently act as barriers preventing widespread adoption of dwell-free eye-typing. The study findings are complemented with computational simulations in a range of conditions that were inspired by the findings in the deployment study. These simulations serve to both contextualize the qualitative findings and to explore quantitative implications of possible interface redesigns. The combined analysis gives rise to a set of design implications for enabling wider adoption of dwell-free eye-typing in practice.
A Network Traffic Classification Method Based on Dual-Mode Feature Extraction and Hybrid Neural Networks

Network traffic classification is a key foundation of traffic management and network security. With the development of traffic encryption technologies and more attention given to user privacy, traditional rule-based and payload-based traffic classification methods have become less effective. To address this problem, recent studies have introduced deep learning-based methods. However, most of these studies do not consider both the flow-level and packet-level characteristics, which we believe are significant in network traffic classification. To further improve the accuracy of traffic classification, this paper proposed DM-HNN, a hybrid neural network based on dual-mode features. First, we treat the packet length sequence as the flow-level feature and the initial byte of the packet as the packet-level feature. Then, we diverge into two paths to analyze the dual-mode features using neural networks. Finally, we combine the two-path features and output the final classification results. We have performed the experiments on public datasets, the results comparing to single-mode and dual-mode traffic classifiers indicate that DM-HNN can achieve excellent performance and has certain effectiveness.
Internet of Things based Natural Disaster Detection and Personalized Notification System

Consistently, normal and human-instigated catastrophes result in infrastructural hurt, monetary costs, emergencies, wounds, and passings. Worldwide environmental change conjointly fortifies the harming force of catastrophic events. during this unique circumstance, net of Things (IoT) based generally calamity discovery and reaction frameworks are wanted to deal with debacles and crises by up catastrophe location. Thusly, IoT gadgets are acclimated to gather data and working with to recognize contrasting sorts of normal and synthetic debacles. This study presents an overall framework with an assortment of sensors sight strange things. The significant qualification between this strategy and existing frameworks is the decentralized and customized cautioning framework. Here, the general information from the disaster recognized space can be obtained and with that information the people present in that space will be monitored and a caution notification regarding the calamity before evidence gets critical. This will be used as an early warning system in the event of the most unexpected events.
Microseismic Event Classification With Time-, Frequency-, and Wavelet-Domain Convolutional Neural Networks

Passive seismics help us understand subsurface processes, e.g. landslides, mining, geothermal systems etc. and help predict and mitigate their effects. Continuous monitoring results in long seismic records that may contain various sources, which need to be classified. Manual detection and labeling of recorded seismic events is not only time consuming but can also be inconsistent when done manually, even in the case where it is done by the same expert. Therefore, an automated approach for classification of continuous microseismic recordings based on a Convolutional Neural Network (CNN) is proposed, with a multiclassifier architecture that classifies earthquakes, rockfalls and low signal to noise ratio quakes. Furthermore, we propose three CNN architectures that take as input time series data, Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) maps. The suitability of these three networks is rigorously assessed over five months of continuous seismometer recordings from the active Super-Sauze landslide in France. We observe that all three architectures have excellent and very similar performance. Furthermore, we evaluate transferability to a geographically distinct seismically active site in Larissa, Greece. We demonstrate that the proposed network is able to detect all 86 catalogued earthquake events, having only been trained on the Super-Sauze dataset and shows good agreement with manually detected events. This is promising as it could replace painstaking manual labelling of events in large recordings.
A Blockchain-Based Approach to Revolutionizing Healthcare

Recent developments in blockchain technology have made it an increasingly interesting subject of research. In addition to financial applications, many studies are being conducted to explore its potential outside of this realm. The healthcare sector is one of the most relevant fields where blockchain has the potential to have a major impact. There have been rapid advances in blockchain implementation and understanding, particularly in this field in recent years. With superior privacy capabilities, blockchain technology is an excellent tool to deliver information and services in the healthcare sector. While these developments are a positive sign, a few issues still need to be addressed in order to make a significant difference. The chapter discusses key fundamentals of blockchain technology, potential applications in several fields of health care, obstacles to the adoption of blockchain technology, as well as the concerns it aims to alleviate in the healthcare industry going forward.
An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets

Exploiting meta-heuristic algorithms is common and popular in financial research areas, because of their ability to efficiently discover the large search spaces in the stock portfolio optimization problem. Increasing the speed of convergence and accuracy in stock selection and creating a portfolio with the highest rate of return and the lowest rate of risk in investment is of particular importance. This paper aims to present a whale optimization-based multi-objective approach to optimize the portfolio in the financial markets. The proposed optimization approach, called PMP-WOA (periodic multi-population whale optimization approach), creates the Pareto fronts of portfolios at an appropriate convergence time and proper accuracy to achieve higher return and liquidity and lower risk as its main goals. The dynamic use of multiple sub-populations and the exchange of information between them have a great impact on increasing the accuracy of solutions and proximity to the real Pareto front. Moreover, controlling the parameters of the algorithm using learning automata leads to the selection of the appropriate search path and speeds up the convergence of the approach. Furthermore, PMP-WOA creates an archive of non-dominated solutions that increases the variety of solutions, which is one of the important issues in creating the Pareto front. In order to evaluate PMP-WOA, it is first used with four multi-objective meta-heuristic algorithms to optimize a number of ZDT mathematical test functions, and then with 6 existing meta-heuristic algorithms for portfolio optimization. The obtained results show the lower generational distance that means higher accuracy, higher number of Pareto solutions, and higher value of maximum spread, and lower value of spacing, which together indicate higher diversity of the proposed solution in the optimization of the ZDT test functions and also in the portfolio optimization compared to the other existing methods. The higher convergence speed of PMP-WOA approach is another evaluation parameter that shows its superiority compared to other existing methods.