Reviewerly/exHarmony huggingface dataset:
Data-driven dynamic treatment planning for chronic diseases

In order to deliver effective care, health management must consider the distinctive trajectories of chronic diseases. These diseases recurrently undergo acute, unstable, and stable phases, each of which requires a different treatment regimen. However, the correct identification of trajectory phases, and thus treatment regimens, is challenging. In this paper, we propose a data-driven, dynamic approach for identifying trajectory phases of chronic diseases and thus suggesting treatment regimens. Specifically, we develop a novel variable-duration copula hidden Markov model (VDC-HMMX). In our VDC-HMMX, the trajectory is modeled as a series of latent states with acute, stable, and unstable phases, which are eventually recovered. We demonstrate the effectiveness of our VDC-HMMX model on the basis of a longitudinal study with 928 patients suffering from low back pain. A myopic classifier identifies correct treatment regimens with a balanced accuracy of slightly above 70%. In comparison, our VDC-HMMX model is correct with a balanced accuracy of 83.65%. This thus highlights the value of longitudinal monitoring for chronic disease management.
SEPSI: A Secure and Efficient Privacy-Preserving Set Intersection with Identity Authentication in IoT

The rapid development of the Internet of Things (IoT), big data and artificial intelligence (AI) technology has brought extensive IoT services to entities. However, most IoT services carry the risk of leaking privacy. Privacy-preserving set intersection in IoT is used for a wide range of basic services, and its privacy protection issues have received widespread attention. The traditional candidate protocols to solve the privacy-preserving set intersection are classical encryption protocols based on computational difficulty. With the emergence of quantum computing, some advanced quantum algorithms may undermine the security and reliability of traditional protocols. Therefore, it is important to design more secure privacy-preserving set intersection protocols. In addition, identity information is also very important compared to data security. To this end, we propose a quantum privacy-preserving set intersection protocol for IoT scenarios, which has higher security and linear communication efficiency. This protocol can protect identity anonymity while protecting private data.
QR Code based Shopping System

Shopping malls are becoming larger and more diverse, with more goods and a wider range of merchandise. The objective of practical purchasing becomes evident over the long run. Huge retailers have made shopping centre format maps, contact Mall shopping frameworks, and different shopping guide mailings to lighten the intricacy of shopping. The role of a shopping guide has risen in popularity in recent years. A system is designed for the convenience of clients' shopping to improve their shopping experience and to surpass the existing system. The merchants and customers are both concerned about developing a simple, fast, and convenient shopping system. Moreover, various faults with the old system are rectified in this system. The faults are for instance, paying bills takes longer and customers must wait in a long queue. Second, customers must push the cart around the mall and take a load of items back to their homes. Another issue is that the standardized identification can unfortunately store only a limited amount of information. Since cell phones have become more common, a straightforward streamlining procedure is utilized to deliver shopping guide frameworks for cell phones that influence QR code creation and acknowledgment innovation, permitting to store more information. The proposed project could give definite and constant shopping objections, permitting shopping centres to mine client information all the more actually and logically. QR code generator is used in this system to generate QR codes for each product. The results of the experiments are turned into a Python programme that scans QR codes with a camera and generates a bill for purchases.
An Application of Normalizer Free Neural Networks on the SVHN Dataset

Batch normalization is a process in which it applies the normal distribution on the previous layer by subtracting its mean over that particular batch and then divides it by the standard deviation. During the training of the input data, the values of the weights of the input layers and the outputs from the activation function are constantly changing. The estimate changes from layers to layers. It has to be updated in order to reach the final stage and obtain results. This process is totally time consuming and also a lot of computations are needed to be done to achieve the output. When training the dataset in smaller batches it tends to ignore the importance of some parameters resulting in the underflow or overflow of weights and the model developed will not be stable and whatever the dataset applied it will either overfit or underfit the data resulting into a futile one. Hence adaptive gradient clipping technique is used along with the Normalizer free networks to prevent the problem of the Batch Normalization. The proposed approach uses normalizer free residual neural networks which are 8.7x times faster than the regular networks. Normalizer free networks are used to overcome the problems of batch normalization and the gradient clipping is used to prevent the exploding of the gradients. The Nfnets are sensitive to alpha value hence introduced a modified form called as adaptive gradient technique which clips the value at every step.
Use of sentiment analysis in social media campaign design and analysis

Sentiment Analysis is the process by which opinions are defined and categorized as positive, neutral, or negative in a given piece of text. It is a vital part of every strategy for tracking social media. It helps to understand what someone is thinking behind a social media post. Knowing the emotion will provide valuable background for the businesses to move further and react. Over the last decade, numerous studies have been carried out on how businesses can use Sentiment Analysis to understand and quantify the feelings of their consumers about their brand, products, and services. An in-depth analysis of the existing empirical literature on SA regarding social media campaigns is provided using a systematic review of the literature. This paper presents the findings according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA). The aim is to create a resource of the methods and techniques used in the studies to assist both researchers and academia. We subsequently identified the corpus trends, challenges, and implications. We suggested avenues for further research in the application of SA in social media campaign design and analysis.
Software Engineering for Augmented Reality - A Research Agenda

Augmented reality changes the way we perceive reality and how we interact with computers. However, we argue that to create augmented reality solutions, we need to rethink the way we develop software. In this paper, we review the state of the art in software engineering for augmented reality applications, derive open questions, and define a research agenda. For this purpose, we consider different engineering phases and evaluate conventional techniques regarding their applicability for AR development. In requirements engineering, we found the integration of AR experts and the associated collaboration between actors to be of key aspect in the development process. Additionally, requirements about the physical world must be considered, which in turn has a huge impact on UI design. The relevance of the physical environment is not yet sufficiently addressed in applicable techniques, which also applies to current implementation frameworks and tools, complicating the AR development process. When evaluating AR software iterations, we found interaction testing and test automation to have great potential, although they have not yet been sufficiently researched. Our paper contributes to AR research by revealing current core challenges within the AR development process and formulating explicit research questions that should be considered by future research.
A Novel Framework for Secure Blockchain Transactions

Owing to the corona virus disease globally scientists designated as “COVID -19 Pandemic”, insurers have seen a rise in digital-virtual transactions as innovative and rejuvenation policy disbursements are made electronically in real-time, i.e., online. The lockdown and shutdown of schools, colleges and Academic institutions have shown chosen an advantage for education technology (EdTech) firms and enterprises, in conjunction with an expansion in need for their services and service sectors enabled by online payments. Many of the institutions are applying computational cloud mechanism to accumulate- build-up, store and share the data information related to the online transactions. Simultaneously, the rise of attacks and assaults is immensely amplified in the cloud mechanism. Keeping in view, scientists have developed model known as blockchain has been designed in a diverse and effective manner to overcome these raids and confrontations. But recent attacks on blockchain show that they can influence the blockchain and capture the half of the information from original transaction which can affect the integrity and privacy measure of the blockchain. The existing models are somewhat good but fail to offer continuous security because of the large data size. Therefore, in this paper, a novel blockchain-based cryptography algorithm is developed to secure blockchain transactions while avoiding security threats and preserving privacy. Moreover, the proposed blockchain algorithm is executed with the help of the Python platform. The performance of the proposed algorithm is compared with conventional techniques and attained the finest outcomes with high accuracy and less time duration.
Automatic hypocenter determination with the IPFx method for the 2018 Hualien earthquake sequence

Abstract The extended integrated particle filter (IPFx) method is an automatic source determination algorithm designed for the Japanese earthquake early warning (EEW) system. The method improved earthquake source determination during active seismicity by incorporating the smart phase association scheme. We applied this method to the 2018 Hualien earthquake sequence and evaluated its performance by comparing it to the manual catalog. We used 1-month continuous waveforms from February 2018 at 170 stations. Owing to the higher noise level, we improved the phase association algorithm to avoid noise contamination. Out of 127 earthquakes with a seismic intensity ≥ 4, 105 were successfully detected in one month, of which 103 had good accuracy with a location error of &lt; 30 km. The detectability of earthquakes decreased immediately following large events. The IPFx method showed good performance in detecting earthquakes with seismic intensity ≥ 4 during the 2018 Hualien earthquake sequence. The method was also applied to the 1-day continuous data on April 18, 2021, and detected 14 earthquakes with a magnitude ~ 2 that were not on the manual catalog. Currently, the Central Weather Bureau in Taiwan uses the effective epicenter method to locate earthquakes for the EEW system. However, source determination for offshore events is difficult as most of the stations are on land. We expect the IPFx method to provide better location estimates for offshore earthquakes and during the period of active seismicity. It also provides an earlier warning as it sends the first message when three stations are triggered. This new method can potentially improve the speed and accuracy of the Taiwanese EEW system.
Optimized Convolutional Neural Network for Tamil Handwritten Character Recognition

Digital recognitions are playing a vital function in the current era of technological advancements. Hence, they offer more possible ways of performing handwritten character recognition (HCR). Generally, recognizing the Tamil handwritten texts is highly complicated, in comparison to the Western scripts. Nevertheless, many researchers have presented several real-time approaches to achieve Tamil character recognition (TCR) in offline mode. This paper introduces a new handwritten TCR (HTCR) approach with two phases: (1) pre-processing and (2) classification. Primarily, the scanned document in the Tamil language is pre-processed via the steps like RGB to grayscale conversion, binarization with thresholding, image complementation, application of morphological operations and linearization. The pre-processed images are then classified using an optimized convolutional neural network (CNN) model. Further, the fully connected layer (FCL) and the weights are tuned optimally via a new sea lion with self-adaptiveness (SL-SA) algorithm. Lastly, the adopted model is evaluated using various measures to prove its supremacy over the existing schemes.
Reduced Calibration Strategy Using a Basketball for RGB-D Cameras

RGB-D cameras produce depth and color information commonly used in the 3D reconstruction and vision computer areas. Different cameras with the same model usually produce images with different calibration errors. The color and depth layer usually requires calibration to minimize alignment errors, adjust precision, and improve data quality in general. Standard calibration protocols for RGB-D cameras require a controlled environment to allow operators to take many RGB and depth pair images as an input for calibration frameworks making the calibration protocol challenging to implement without ideal conditions and the operator experience. In this work, we proposed a novel strategy that simplifies the calibration protocol by requiring fewer images than other methods. Our strategy uses an ordinary object, a know-size basketball, as a ground truth sphere geometry during the calibration. Our experiments show comparable results requiring fewer images and non-ideal scene conditions than a reference method to align color and depth image layers.
Functional narrative animation as visual feedback for interactions in 3D visualization

Abstract Visual feedback can help users understand the function, state, and outcome of a system during the pre‐, mid‐, and post‐interaction phases. Current visual feedback in 3D visualization scenarios takes less account of information transfer in terms of appearance design and dynamic behavior, which results in visual feedback being presented in a more engineered form and conveying simple information. For these issues, we proposed the concept and method of functional narrative animation as visual feedback for interactions in 3D visualization. We also provided a set of Unity‐based animation library and a plugin tool for configuring the animations. Finally, through user experiments and interviews, we analyzed the role of functional narrative animation as in the interactive visual feedback for 3D visualizations and make corresponding design recommendations.
Does accuracy matter? Methodological considerations when using automated speech-to-text for social science research

The analysis of spoken language has been integral to a breadth of research in social science and beyond. However, for analyses to occur with efficiency, language must be in the form of computer-readable text. Historically, the speech-to-text process has occurred manually using human transcriptionists. Automated speech recognition (ASR) is advertised as an efficient and inexpensive alternative, but research shows this method of speech-to-text is prone to error. This paper investigates the viability of using error prone ASR transcriptions as part of the methodological process of language analysis. Results show that at the individual feature level, analysis of ASR transcriptions differ dramatically from human transcriptions. However, when the same features are used for classification, a common machine learning task, performance results between ASR and human transcriptions are similar. We present these findings and conclude with a discussion on the methodological considerations for researchers who opt to use automated speech recognition for social science research.
Disaster Tweet Classification using ML

Social media platforms such as Twitter bring about a lot of details amid disaster. Today data is not only seen in the form of texts but also in the form of special characters, sound, symbols, etc on Twitter. Time is invaluable when it comes to sharing emergency information and giving the response accordingly, seen by studies. It was seen by research that twitter gets utilized effectively when it comes to disseminate useful information regarding various types of disasters like volcano eruption, flood, devastation due to geopolitical war so that concerning authority(like NDMA) could use such details to response the disasters in a very quick time and effectively. The main of the research work is achieved by using the supervised machine learning and natural language processing method system could be able to classify the tweets as disaster related tweet or non disaster related tweet. Proposed approach is based upon the use of Word2Vec & the Machine Learning Algorithm, in which feature vector is provided by Word2Vec and to classify the tweets Classification Algorithms is used.
Machine Learning and 5G Network in an Online Education WSN using AI Technology

The technology effect is growing by the day. Artificial Intelligence, Machine Learning, Deep Learning, and Big Data are employed to acquire enough application feasibility to alleviate human labour strain. The manner in which the information society is developing in this globe. The impact will cause certain modifications in the educational system's evolution. This study focuses on how to improve remote music education in the presence of a 5G network. In this scenario, network speed is regarded as having one of the most significant effects on students taking online classes. As a result, a 5G network may be the greatest option for expanding network speed and providing a strong impetus. To train the intelligent system to deliver remote music education via wireless 5G networks, the suggested system employs the Convolutional Neural Network (CNN) technique. Compline, uncertainty, unverified, and out-of-place are the characteristics used to compare the proposed system to the existing system. The suggested method outperformed the old system in terms of total accuracy, achieving 99.13 percent.
Adrestus: Secure, scalable blockchain technology in a decentralized ledger via zones

Nowadays, an increasing number of blockchain architectures provide well-promising protocols for pseudonymous online payments via proposed cryptocurrencies. Most of them suffer from a number of extensibility and scalability issues, as their capacity regarding the number of transactions they are capable of processing per second is limited. Security is also a challenge for this kind of architectures. This paper presents the design and implementation of the Adrestus system, a blockchain-based transaction system with a novel consensus mechanism that is able to tolerate Byzantine faults and is designed to scale without compromising system security. One of the main components of the Adrestus design is a consistent hashing mechanism for the efficient assignment of transactions on parallel regions, called zones, and for solving load balancing problems. We claim that the Adrestus blockchain system scales linearly without compromising system security and achieves its goals without introducing the unnecessary overhead and by eliminating energy and computational waste. Preliminary theoretical simulations and results reflect that Adrestus exceeds the average throughput of the most well-known cryptocurrencies like Bitcoin, and thus, it achieves a higher performance. In this paper, we present this proposed approach along with simulation results and examine the conditions for the proposed fault-tolerant system to meet safety and liveness.
Radio Frequency Fingerprint Feature Extraction Based on I/Q Data Distribution Features

With the development of cognitive radio, more and more radio frequency devices can dynamically switch radio frequency parameters. The reliability of the traditional method of marking the radio signal source in the communication band is greatly reduced. This paper proposes a radio frequency fingerprint feature extraction method based on the accumulated distance of I/Q data components. The RF raw I/Q sample data is collected through the distributed electromagnetic spectrum detection network. The cumulative I/Q distance is extracted from the I/Q sample data collected by multiple detection nodes. The weight distribution of the I/Q components is calculated. Based on I/Q sample data estimation cumulative I/Q distance and reception signal strength parameters, the received signal strength (RSS)-I/Q distance characteristic curve and the corresponding dataset are constructed. The radio frequency fingerprint characteristic curve of the target radio device is established. The optimized Sigmod mathematical model is used as a target function. The RSS-I/Q distance data is set as a sample. The normalized dataset is fitted according to the target function. The parameter values of the optimized sigmod mathematical model are obtained by estimating the fitted curve. The estimated value is taken as the eigenvalue matrix, which is the radio frequency fingerprint characteristic parameter of the target radio equipment. The RF fingerprint feature extraction method proposed in this paper can comprehensively analyze the subtle features of radio equipment from the perspective of signal source. The experiments show that the RF fingerprint feature parameters extracted from the same signal source in different frequency bands, different environments (cement ground, asphalt road, grass, riverside, indoor, etc.), and different temperature and humidity parameters are invariant within a certain error range. The radio frequency fingerprint feature parameters extracted in different signal sources have good distinction in the same frequency band, the same environment, and the same temperature and humidity.
A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images

Aiming at the deficiency that blockchain technology is too sensitive to the binary-level changes of high resolution remote sensing (HRRS) images, we propose a new subject-sensitive hashing algorithm specially for HRRS image blockchains. To implement this subject-sensitive hashing algorithm, we designed and implemented a deep neural network model MultiRes-RCF (richer convolutional features) for extracting features from HRRS images. A MultiRes-RCF network is an improved RCF network that borrows the MultiRes mechanism of MultiResU-Net. The subject-sensitive hashing algorithm based on MultiRes-RCF can detect the subtle tampering of HRRS images while maintaining robustness to operations that do not change the content of the HRRS images. Experimental results show that our MultiRes-RCF-based subject-sensitive hashing algorithm has better tamper sensitivity than the existing deep learning models such as RCF, AAU-net, and Attention U-net, meeting the needs of HRRS image blockchains.