Reviewerly/exHarmony huggingface dataset:
A Framework to Restrict COVID-19 like Epidemic Converting to Pandemic Using Blockchain

With the spread of COVID-19, it becomes necessary for the whole research community to think about solutions that can help prevent such spread in the future. Blockchain technology can provide one of the methods to reduce the spread of any future pandemic, and such a spread can be restricted to an epidemic level. Epidemic spread as there is no easy way to check infected people moving from one country to another. For this, we propose a framework in which passports are digitized, and all the travel history is stored in the blockchain. Records are maintained in the distributed ledger, not just by one country but all countries worldwide. Whenever a person travels from one country to another, his/her record can be fetched from the blockchain, and based on history, he/she may be permitted or denied further movement. Even in the initial stages of spread, the system will be designed in a way that anyone having travel history to the centre of the epidemic can be set on red alert in just one click so that governments can track the potentially infected people and can quarantine them or take necessary measures to minimize the spread. In the proposed framework, only travel history will be stored in the blockchain that is accessible by anyone, but an individual’s privacy will be maintained by generating different addresses from the same key.
Research on data processing method of railway safety information based on NLP technology

Aiming at the text data of railway transportation safety, designed the platform of the unstructured text data converte into the structured data, and assist the security text data further mined. Summarize the data characteristics, and design the overall framework and business process of the platform from the perspective of data security and intelligent data operation. The named entity recognition and text classification models that can realize text structural transformation are studied. Some models are built into the platform according to the characteristics of data. The interaction mode between model learning and platform is designed. The platform provides a structured transformation tool for text data analysis in the field of railway transportation security.
Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies

Natural Evolution Strategies (NES) is a promising framework for black-box continuous optimization problems. NES optimizes the parameters of a probability distribution based on the estimated natural gradient, and one of the key parameters affecting the performance is the learning rate. We argue that from the viewpoint of the natural gradient method, the learning rate should be determined according to the estimation accuracy of the natural gradient. To do so, we propose a new learning rate adaptation mechanism for NES. The proposed mechanism makes it possible to set a high learning rate for problems that are relatively easy to optimize, which results in speeding up the search. On the other hand, in problems that are difficult to optimize (e.g., multimodal functions), the proposed mechanism makes it possible to set a conservative learning rate when the estimation accuracy of the natural gradient seems to be low, which results in the robust and stable search. The experimental evaluations on unimodal and multimodal functions demonstrate that the proposed mechanism works properly depending on a search situation and is effective over the existing method, i.e., using the fixed learning rate.
A Rudimentary Progression Model for Artificial Intelligence in Education Competencies and Skills

Artificial Intelligence in Education is progressing with accelerated pace, and this impacts the role of the teacher in becoming catalyst in designing and orchestrating teaching and learning with the use of AI-based systems. This study proposes a rudimentary competency framework with an associated progression model that aims to help teachers to plan, develop, self-assess, and reflect on existing and new competencies for teaching and learning through AI-based systems. The framework encompasses six competency themes along with six relational and inclusive descriptors of competency progression levels that may be used either as standalone or as a concerted set of competencies for enabling complete and holistic conceptualisations, and utilizations of competencies and capabilities that teachers may envisage to develop for making the leap into adaptive and automated AI-based teaching and learning. The AIED Comp framework may inform computational representation models and algorithms that focus on teacher-facing AIED applications and tools for gaining insights on skills and capabilities teachers may employ for innovating in their classroom.
Utility Optimization for Multi-user Task Offloading in Mobile Ad Hoc Cloud: A Stochastic Game Approach

Offloading tasks to the mobile ad hoc cloud (MAHC) can improve the utility of mobile devices in scenarios without infrastructure. However, when multiple resource demanders (RDs) offload tasks to multiple resource providers (RPs) in the MAHC with task random generation, how to optimize the utility of RDs and the service quality of the MAHC is an issue worth researching. To this end, we investigate this issue from the stochastic game perspective. Firstly, we combine the obtained time, energy, and monetary revenue as the utility of each RD and construct the utility improvement of each RD as a static non-cooperative game. Secondly, according to the randomness of RD&#x2019;s task generation, we transform the single-shot non-cooperative game into a stochastic game in an infinite time horizon to obtain the long-term utility. Thirdly, to reduce the computational complexity for achieving Nash equilibrium, we propose an action reduction alternate backward iteration algorithm according to the games characteristics and prove its convergence. Finally, we compare the utility obtained from the static game with the stochastic game, the numerical results indicating that the utility of each RD can be effectively improved by utilizing the stochastic game.
Homomorphic Proxy Re-Signcryption Scheme and Its Application in Edge Computing-Enhanced IoT

Proxy re-signcryption combines the functionalities of re-encryption and re-signature in a single logical step, outperforming a simple combination of re-encryption and re-signature schemes. Proxy re-signcryption ensures the confidentiality of data transmission as well as the anonymity of the sender's identity in the public network, so it can be applied in smart grid, machine learning and other fields. However, only a few proxy re-signcryption schemes exist, and none of them support the homomorphic operations of ciphertext data. In this paper, we offer a homomorphic proxy re-signcryption scheme that leverages batch verification and achieves the conversion between ciphertext and signature. Based on the proposed proxy re-signcryption scheme, we also give a privacy-preserving data aggregation scheme for edge computing-enhanced IoT, which allows the control center to perform statistical analysis and processing on the data collected by IoT devices.
A Cryptocurrency Price Prediction Model Based on Twitter Sentiment Indicators

The cryptocurrency becoming increasingly expensive, price prediction methods have also been widely studied. As an application of big data in finance, the sentiment tendency of related topics on social platforms is an important indicator of cryptocurrency price prediction methods and has attracted broad attention. However, the accuracy of the existing macro-sentiment indicator calculation methods should be further improved. Aiming at the problem that the accuracy of price prediction is not significantly improved by applying the existing macro-sentiment indicators, this paper proposes three new public sentiment indicators based on small granularity. Correlational analysis between the indicators and price data is conducted in the paper as well. By analyzing the degree of sentiment tendency of each comment, the accuracy of the three public sentiment indicators is improved. Specifically, this paper quantifies public sentiment indicators by taking into account the degree of emotional bias of each tweet, which makes sentiment indicators with small granularity. Compared with previous methods, the value prediction accuracies of cryptocurrencies have been improved under three deep learning frameworks LSTM, CNN, and GRU with the use of small granularity sentiment indicators.
Multi-Sensor Measurements Fusion for Passive Target Tracking in Underwater

The pivot goal of estimation algorithms is to generate information with a quality higher than that of raw measurements available and to extract the information that can be obtained indirectly from the available measurements. To fulfill this aim, the measurements from multiple sensors are combined to get a more accurate and reliable estimate of the target. In this research work, measurements from five multiple sensors are combined to get different motion parameters of the desired target. The sub-optimal filter, Unscented Kalman filter is considered for the performance analysis of the algorithm. The simulation is carried in MATLAB software using the Monte-Carlo simulation method for better analysis of the results.
Anomaly Detection System of Controller Area Network (CAN) Bus Based on Time Series Prediction

With the development of intelligent networked vehicles, the research on the safety of in-vehicle networks has gradually become a hot spot. CAN (controller area network) is the most widely used in-vehicle network bus, and its safety problem has become the most critical problem to be solved in the development process of intelligent networked vehicles. This paper aims at the in-vehicle can be used in intelligent networked vehicles Bus network, its communication characteristics and security problems are analyzed and dissected. Meanwhile, with the increase in demand for in-vehicle network communication applications, the corresponding attacks have also increased year by year. Therefore, this paper only increases the anomaly detection of in-vehicle application log in the anomaly detection of CAN bus, aiming to detect the abnormal behavior of vehicles in an all-around way. To solve the field data lacking's problem, we collect a data set containing several types of data from multiple channels, including different types of attack can bus messages. Due to the different elements in the message having different effects on the classification results, the attention mechanism is introduced to give different weights to different messages and log data segments, which increases the effect of classification detection.
CArrency: An Energy-Efficient Proof-of-Work Scheme for Crypto-Currencies

Proof-of-work (PoW) schemes have been an essential ingredient of crypto-currencies. These schemes enable crypto-currencies to operate as decentralized systems, and attract people to volunteer to host (mine) such services in the interest of incentives. Quite a few crypto-currencies use hash-based PoW. For example, the PoW of Bitcoin uses the SHA-256 hash function. Although SHA-256 provides 128-bit security normally considered sufficient in the domain of PoW schemes, it demands a huge amount of computational power, specifically in hardware. This paper proposes a new PoW scheme CArrency based on linear hybrid cellular automata (LHCA). The primary achievement of CArrency is a sizable reduction in the power requirements compared to SHA-256, while maintaining 128-bit security. CArrency does it by reducing the slice count required in its implementation on FPGAs. LHCAs serve this purpose effectively as any n-cell LHCA can be accommodated in a depth-two combinational circuit using $$\mathcal {O}(n)$$ slices in FPGAs. However, we stress that the low requirement of slices (so the power) does not dilute the practical hardness of CArrency as a PoW. Like all other PoWs, in reality the time required to solve any instance of CArrency can always be controlled by the parameter difficulty level. The hash-based PoWs rely on the one-wayness and collision resistance of hash functions in order to establish their security. On the contrary, CArrency is proven to be secure exploiting the inherent randomness of LHCAs. Further, we compare CArrency and SHA-256 in terms of their practical implementations on FPGA platforms.
A New Method of Specific Emitter Feature Extraction Based on IQ Imbalance

A new method for fingerprint feature extraction of specific emitter is proposed, the method used the individual differences of gain mismatch and phase mismatch which based on the I/Q imbalance. Received signal is denoised, demodulated and filtered, and the I/Q information of the original signal is decomposed in this method. Respectively calculate the ratios of autocorrelation functions to the cross-correlation function of I/Q signals at 0, and two specific values only related to gain and phase mismatch parameters. The values are used as two dimensional characteristic parameters and the parameters are used in specific emitter identification. Compared with the method based on bispectrum and Hilbert-huang transform method, the identification accuracy of new method is improved by more than 5% and 10% under the condition of simulation experiment. Compared with the SNR estimation method based on eigenvalue decomposition, the identification accuracy of the proposed method is improved by 3% and identification effect is better under the condition of low SNR.
An Opinion Mining of Text in COVID-19 Issues Along with Comparative Study in ML, BERT &amp; RNN

The global world is crossing a pandemic situation where this is a catastrophic outbreak of Respiratory Syndrome recognized as COVID-19. This is a global threat all over the 212 countries that people every day meet with mighty situations. On the contrary, thousands of infected people live rich in mountains. Mental health is also affected by this worldwide coronavirus situation. Due to this situation online sources made a communicative place that common people shares their opinion in any agenda. Such as affected news related positive and negative, financial issues, country and family crisis, lack of import and export earning system etc. different kinds of circumstances are recent trendy news in anywhere. Thus, vast amounts of text are produced within moments therefore, in subcontinent areas the same as situation in other countries and peoples opinion of text and situation also same but the language is different. This article has proposed some specific inputs along with Bangla text comments from individual sources which can assure the goal of illustration that machine learning outcome capable of building an assistive system. Opinion mining assistive system can be impactful in all language preferences possible. To the best of our knowledge, the article predicted the Bangla input text on COVID-19 issues proposed ML algorithms and deep learning models analysis also check the future reachability with a comparative analysis. Comparative analysis states a report on text prediction accuracy is 91% along with ML algorithms and 79% along with Deep Learning Models.
Multimedia security and privacy protection in the internet of things: research developments and challenges

With the rapid growth of the internet of things (IoT), huge amounts of multimedia data are being generated from and/or exchanged through various IoT devices, systems and applications. The security and privacy of multimedia data have, however, emerged as key challenges that have the potential to impact the successful deployment of IoT devices in some data-sensitive applications. In this paper, we conduct a comprehensive survey on multimedia data security and privacy protection in the IoT. First, we classify multimedia data into different types and security levels according to application areas. Then, we analyse and discuss the existing multimedia data protection schemes in the IoT, including traditional techniques (e.g., cryptography and watermarking) and emerging technologies (e.g., blockchain and federated learning). Based on the detailed analysis on the research development of IoT-related multimedia security and privacy protection, we point out some open challenges and provide future research directions, aiming to advance the study in the relevant fields and assist researchers in gaining a deeper understanding of the state of the art on multimedia data protection in the IoT.
Seismic Data Compression: A Survey

With the advent of robust computing power, any sophisticated algorithm for data compression is becoming a trivial task. With more than petabytes of seismic data produced every day and the trend toward 4D survey, it is becoming essential to develop robust algorithms for the seismic data compression. This led to significant research efforts in data compression for field implementation, as shown by the research trends. The study of seismic data compression techniques is the main focus of this paper. In particular, we provide a short survey of different seismic data compression methods. This survey covers several categories of compression techniques, namely transformation, prediction, quantization, run length, and sampling. This paper can be deemed an initial attempt to provide an up-to-date overview of the research work carried in this all-important field of seismic data processing.
A Comparative Study of Machine Learning-based Approach for Network Traffic Classification

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to find the most suitable training model for network traffic classification tasks. Data pre-processing is first applied to normalize each feature type in the dataset. Different machine learning techniques, including k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), are applied based on the normalized features in the classification phase. An open-access dataset ISCXVPN2016 is applied for this research, which includes two types of encryption (VPN and Non-VPN) and seven classes of traffic categories classes. Experimental results on the open dataset have shown that the proposed models have reached a high classification rate – over 85% in some cases, in which the RF model obtains the most refined results among the three techniques.
X-ray Image Blind Denoising in Hybrid Noise Based on Convolutional Neural Networks

Low-dose X-ray imaging is a medical imaging method used for disease screening and diagnosis. However, the interpretation of such images is a challenging task because of machine noise. Although some deep learning-based denoising algorithms have made considerable progress, they do not perform well on real X-ray images. Because the actual noise of the X-ray image is more complicated. In this paper, we design a noise model according to the physical principle of X-ray imaging, which is used to simulate the real X-ray image. On this basis, we propose a blind denoising convolutional neural network (X-BDCNN) for low-dose X-ray image enhancement. X-BDCNN consists of two networks. One is used to estimate the noise level of the input noise X-ray image. The other is used to obtain the residual noise image by taking the noisy X-ray image and the estimated noise level as input. The final denoised X-ray image is obtained by subtracting the residual noise image from the input noise X-ray image. In addition, we add a structural similarity (SSIM) loss function to X-BDCNN to maintain the structural information. The experimental results show that the denoising performance of X-BDCNN is better than the existing denoising methods. Code is available online.
Tangles in IOTA to make crypto currency transactions free and secure

Blockchain introduction has made a revolutionary change in the crypto currency around the world but it has not delivered on its promises of free and faster transaction confirmation. Serguei Popov's proposal of using tangles, a directed acyclic graph, which essentially is considered to be the successor of blockchains and offers the required features like machine to machine micro payment and fee-less transaction. It requires the user to approve the previous two transactions in the web to participate in the network. This essentially eliminates the miners and the mining part form the currency exchange and provides the user or participants to do their transactions fee-less. Since the participant verifies the previous two transactions it also contributes to the security of the tangle. In this paper, features of internet of things application (IOTA) and all the improvements in it using tangles are discussed along with how it contributed to the security and how it enables the participants to have fee-less transactions is also discussed.
Designing Immersive Virtual Reality Environments for Supporting Patients at Home: Translating Input From Home Care Nurse Experts to Design Requirements

CIN: Computers, Informatics, Nursing 41(3):p 183-184, March 2023. | DOI: 10.1097/01.NCN.0000904400.19657.5d
A Systematic Mapping on Detection of Human Mouth Landmarks

Facial landmarks represent regions of interest whose detection and localization generate features supporting the identification of movements, feelings, and reactions. Most facial feature detection algorithms focus on entire semantic areas, such as the region of a mouth which allows grained manipulation that is essential for a wide domain variety. This paper describes a systematic mapping of the detection of landmarks in human faces and their application domains. The identification and selection methods of primary studies include automatic search on information sources, inclusion, and exclusion criteria over 344 scientific papers from 2015 and 2021, from which we analyzed and synthesized 115 primary studies. Our analysis considered the implementation of methods, types, and uses of data extracted from the mouth. The mapping brought exciting information as new methods, datasets, and domains researched through the time interval reviewed as well research gaps that can be explored.