Reviewerly/exHarmony huggingface dataset:
Channel Characteristics Based Adjustable Fingerprint for Identity Authentication in WDM-PON With Deep Neural Networks

Wavelength-division-multiplexed passive optical network (WDM-PON) has been widely deployed for the high-speed, reliable transmission and low-cost properties. The physical layer identity authentication in WDM-PON becomes increasingly prominent. Recently, many device fingerprint based identity authentication schemes are proposed. However, these schemes only realize constant fingerprint, which will be acquired by illegal ONU after optical spectrum analysis. Therefore, the higher-level security requirement cannot be satisfied. To solve the problem, we propose a physical layer identity authentication method in WDM-PON by exploiting the channel characteristics based adjustable fingerprint with deep neural networks (DNNs). By secretly negotiating with legal optical network units (ONUs) on the lengths of the local fibers applied by them, the optical line terminal (OLT) can acquire the unique channel characteristics fingerprint obtained by each legal ONU. It should be noted that the fingerprint can be adjusted by modifying the length of the local fiber. Moreover, the same number of DNNs as legal ONUs are trained for fingerprint identification. Simulation results show 100% identification accuracy for illegal ONU when the length deviation between two fibers applied by legal ONU and illegal ONU is greater than 1.5 km. Meanwhile, the identity of each legal ONU can be recognized with 100% accuracy.
Securing Healthcare Data Using Decentralized Approach

According to WHO, brain stroke seems to be the second most common cause overall, accounting for about eleven percent of all mortality. Data security and privacy are in great demand in the healthcare industry. Data is now kept in a centralized manner in present systems, with all data being stored in a single area. In such systems, there is a high possibility for an intruder or third party to access and change the data. In Healthcare, data is the most crucial factor, so if there are any small changes made by the intruder in the data, it may lead to provide false outcome. In this proposed system, we secure the data in a decentralized approach using IPFS protocol and Block chain. We can reduce the risk of data failures and outages while improving security, performance, and privacy using this strategy. The required data will be collected and be trained with the ANN algorithm to get the final model.
Exploring Privileged Features for Relation Extraction with Contrastive Student-Teacher Learning

Significant progress has been made by joint entity and relation extraction methods, which directly generate the relation triplets and mitigate the issue of overlapping relations. However, previous models generate the entity-relation triplets solely from input sentences. Such information is insufficient to support the modeling of interactive information between entities and relations. In this paper, we define the features that provide mutual supports for entity and relation detection but can only be accessed at training time as privileged features for relation extraction, and devise two teacher models to exploit privileged entity and relation features, respectively. Meanwhile, we propose a novel contrastive student-teacher learning framework for joint extraction of entities and relations (STER), where a student network is encouraged to amalgamate privileged knowledge from two expert teacher networks that additionally utilize the privileged features, based on contrastive learning. Experiment results on three benchmark datasets (i.e., ADE, SciERC and CoNLL04) demonstrate that STER has robust superiority over competitors and sets state-of-the-art. For reproducibility, we will release the data and source code once the paper is accepted.
Error correction of the continuous-variable quantum hybrid computation on two-node cluster states: Limit of squeezing

In this paper, we investigate the error correction of universal Gaussian transformations obtained in the process of continuous-variable quantum computations. We have tried to bring our theoretical studies closer to the actual picture in the experiment. When investigating the error correction procedure, we have considered that both the resource GKP state itself and the entanglement transformation are imperfect. In reality, the GKP state has a finite width associated with the finite degree of squeezing, and the entanglement transformation is performed with error. We have considered a hybrid scheme to implement the universal Gaussian transformations. In this scheme, the transformations are realized through computations on the cluster state, supplemented by linear optical operation. This scheme gives the smallest error in the implementation of universal Gaussian transformations. The use of such a scheme made it possible to reduce the oscillator squeezing threshold required for the implementing of fault-tolerant quantum computation schemes close to reality to -19.25 dB.
Recommendation System with Exploratory Data Analytics using Machine Learning

Digital data is generated on a vast scale nowadays and has been for the past two decades. This phenomenon implies shift in how data is managed, and conclusions drawn from it. Furthermore, artificial intelligence approaches and procedures for new ideas of analyzing Big Data, Sentiment Analysis (SA), also known as Opinion Mining (OM), has been a hot topic in recent, because of its ability to extract value from data, it has been around for a long time. It is, however, a topic that has gotten more attention in the fields of engineering and linguistics. As a result, the goal of this research is to provide insights into the field of exploratory data analysis, as well as an orientation toward the study of a recommendation system employing big data in the context of an e-commerce system, through the use of machine learning. probable machine learning based model. Initially the research contribution briefly summarizes discussion of the strategies and processes currently used in Sentiment Analysis and further we work upon the analysis of a recommendation system with e- commerce data using LSTM Model.
Handwritten digit recognition based on classical machine learning methods

Nowadays, handwritten digit recognition using deep learning and machine learning has become a popular field, and many approaches are based on CNN to achieve this. However, a recent study shows that perhaps MLP have better image classification performance compared to CNN. To investigate the performance of classical machine learning algorithms for image classification and to provide possible conclusions for subsequent research, this paper uses five classical machine learning algorithms including logistic regression, MLP, random forest, KNN and SVM to analyze, train and test experiments on handwritten digital images. The results show that MLP has better image classification performance compared to other algorithms.
Explainable AI in Neural Networks Using Shapley Values

Deep neural networks (DNNs) often outperform other machine learning models in terms of accuracy. However, due to the complex structure of deep neural networks, it is difficult to comprehend and trust the decisions made by them. Even though DNNs are highly accurate, they cannot be adopted easily in domains such as medical and finance, where trust and transparency are non-negotiable. Explainable Artificial Intelligence (XAI) is the field of study in AI to understand the machine learning models and to interpret their decisions. In this paper, we propose feature attribution-based explainable methods to interpret deep neural networks using a game theory concept known as Shapley values. We also discuss how to introduce interpretability in existing deep learning model systems non-intrusively, making the transition from "black box" to interpretable deep neural networks.
Scope and Challenges of Blockchain Technology

After the distribution of the white paper on Bitcoin, published by Satoshi Nakamoto in 2008, blockchain (BC) has become one of the most much of time-examined techniques for making sure about information stockpiling and moves through decentralized, trustless, shared frameworks. Blockchain Technology got its limelight after the success of a cryptocurrency named Bitcoin. Blockchain technology is completely behind working of Bitcoin. Blockchain is a tamper-proof technology with applications in a widespread area. The key behind BC is its tamper-proof nature, which provides integrity to data authentication. This paper focuses on the fundamentals of blockchain, along with its role in various other applications and technologies. The paper will also enlighten the readers about issues related to blockchain technology in integration with other technologies. The paper will expose the impact of blockchain technology in providing solutions to issues concerned with advanced technologies. This examination identifies peer-checked on writing that tries to use blockchain for cybersecurity purposes and presents a methodical investigation of the most ever now and again embraced blockchain security applications. The Internet of Things (IoT) credits itself well to novel blockchain applications, as do frameworks and machine recognition, open key cryptography, web applications, certification plans, and the ensured amassing of Personally Identifiable Information (PII). This helpful systematic review moreover uncovers understanding into the future heading of assessment, preparing, and rehearses in the blockchain and cybersecurity space, for instance, the security of blockchain in IoT, the security of blockchain for AI data, and sidechain security.
Power data publishing method based on differential privacy considering spatiotemporal correlation

With the continuous construction of smart grid, more and more power data can be easily collected through smart meters. These collected power data can be used for analysis and research, so as to improve the level of Saudi power grid construction. However, if these power data are directly released, it may reveal the privacy of power users. Therefore, this paper presents a new power data publishing method to protect the user’s privacy. The attacker can use the background knowledge to obtain the user’s privacy by analyzing the published power data. According to the attack model, this paper designs the probability density function of the corresponding disturbance data, and randomly disturbs the power data according to the probability density function of the disturbance data. Secondly, when designing the noise density function, we consider that the power data between the two dimensions may have spatiotemporal correlation. In order to avoid destroying the correlation when adding random disturbances, we design the probability density function directly from the perspective of two dimensions. Finally, in order to ensure the availability of data, the range of random disturbance is given. Experiments show that our method can not only ensure the privacy of users, but also ensure the reliability of data.
Learning and Fusing Multiple User Interest Representations for Sequential Recommendation

Deep learning is known to be effective at automating the generation of representations, which has achieved great success by learning efficient representations from data, especially for user and item representations in sequential recommendation. However, most existing methods usually represent a user's interest by one independent representation vector, which is inadequate to capture user's diverse interests. We aim to fully characterize the diversity of user interests in this study to improve the recommendation performance. We propose a $${\boldsymbol{M}}ultiple$$ $${\boldsymbol{U}}ser$$ $${\boldsymbol{I}}nterest$$ $${\boldsymbol{R}}epresentations$$ (MUIR) model that learns and fuses user's interests from different aspects. To learn different levels of user interests, we specifically leverage two self-attention-based modules that better capture user's local and global interests respectively. By considering the information of the recent interacted items, we further design a gating module to balance the interests, which is capable of modeling how user interests evolve and interact in recent behavior sequence. Extensive experiments conducted on the real-world datasets demonstrate that our model, MUIR, outperforms existing state of-the-art methods significantly.
Development and Application of an Omni-Directional Robot for the Detection of Combustible and Toxic Gases

This study presents the development of an omnidirectional robot for the detection of combustible and toxic gases. The purpose of this study is to fabricate a robot that can detect combustible and toxic gases that are harmful to the human body. The design of the robot aims to limit the chances of exposure of humans from hazardous gases. The omnidirectional robot is designed to have the mobility to traverse on confined and cramped spaces such as ducts and pipes where gas leakages can occur. This study consists of designing, programming, GUI development, fabrication, approximation of gas concentrations, preliminary tests, sensor tests, and actual tests. The robot is based on existing designs and mecanum wheels are used for the omnidirectional function. The robot is programmed using Arduino microcontroller for motor control and sensor readings. For gas detection, gas sensors such as MQ3, MQ7 and MQ9 were used. The robot was tested based on its mobility and capability in determining specific gas concentrations. There are six gases that the robot can detect namely alcohol, benzene, carbon monoxide, hydrogen, methane, and LPG. Results showed that the omnidirectional feature of the robot allows it to traverse obstacles with ease. In addition, the developed robot shown its capability in detecting gas concentrations using preliminary and actual tests. The actual tests were conducted on a commercial building, basement parking, and printing company.
Processing Spanish gender in a usage‑based model with special reference to dual‑gendered nouns

Abstract In an experiment, Spanish speakers assigned gender to nouns. Some nouns had biological referents. Others had a mismatch between their gender and their final phones (e.g. problema ). Nouns with biological referents and nouns with matching gender and phonology were responded to faster suggesting that gender does not depend solely on a noun’s gender. Gender was also assigned to dual-gendered nouns, which are feminine nouns that take the masculine article el (e.g. agua ). Most participants assigned them masculine gender. Dual-gendered nouns are often preceded by masculine modifiers which is due to analogy to el . The idea is explored that the gender of el , along with all modifiers a noun has been experienced with, explains gender assignment. Computational simulations were carried out to test this using exemplar, naive Bayes, and decision tree algorithms. They made accurate predictions without referencing the noun’s gender. In dual-gendered nouns, a shift towards preposed masculine modifiers was observed. A simulation predicted the gender of bare dual-gendered nouns which mirrored the masculine gender the experimental participants provided. These results suggest a usage-based model in which a noun’s gender is determined by the modifiers it has been experienced with.
Perceptual Quality Evaluation of Corrupted Industrial Images

In recent years, computer vision applied in industrial application scenarios has been attracting attention. A lot of work has been made to present approaches based on visual perception, ensuring the safety during the processes of industrial production. However, much less effort has been done to assess the perceptual quality of corrupted industrial images. In this paper, we construct an Industrial Scene Image Database (ISID), which contains 3000 distorted images generated through applying different levels of distortion types to each of the 50 source images. Then, the subjective experiment is carried out to gather the subjective scores in a well-controlled laboratory environment. Finally, we perform comparison experiments on ISID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types.
De-Speckling of SAR Images with Fuzzy Filters along with Altered Preserved Edge Values

In this research, the de-noising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV).SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves).A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the de-noised image.The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE.The proposed method in this research shows better SSI (Spackle Suppression Index) value.Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise.This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.
Analysis of written interactions in open-source communities using RCNN

Open-source software has proved to be a key pillar in modern-day software development. The growing size of the open-source communities has significantly increased the throughput of these projects. However, larger communities tend to lead to difficulties in communication and openness for newer members. In this paper, we try to analyze the interactions on Github for some of the popular open-source projects. We have created a database of 2500 filtered comments classified into five classes of emotion. We have also proposed a novel RCNN based architecture to detect the sentiment of the comments and perform multiclass text classification. Furthermore, we have discussed possible model integrations with existing open-source platforms and the challenges associated with the implementation.
Fog image enhancement algorithm based on improved Retinex algorithm

In order to enhance the contrast, color and brightness information of the foggy image, an improved Retinex algorithm is proposed to enhance the foggy image. First, based on the Retinex algorithm, the Gaussian iterative algorithm is used to solve the total variational energy functional function, so as to effectively maintain the color constancy. Secondly, the method of combining relative gradient and absolute gradient is used to stretch the gradient of the brighter part of the foggy image. The enhanced fog image is reconstructed under the total variational Retinex theory, and the enhancement algorithm is applied to the RGB color image. Enhance the contrast and perception characteristics of the entire image, and restore the color information of the image. Use this to enhance the blurry image in foggy weather. Finally, the image is reconstructed by adding color factors to obtain a fused fog-free image. In addition, in the image preprocessing part, an image enhancement algorithm based on bilateral filtering and high-frequency lifting filtering is proposed. The simulation experiment with Matlab shows that the improved algorithm is better than the results processed when the two algorithms are used alone through the comparison of visual effects, running time and information entropy.
Strategies for Online Teaching of Chinese as a Second Language

The emergence of the COVID-19 is a challenge and an opportunity for the education industry. Under the environment of the pandemic, Teaching Chinese as a Second Language (TCSL) has responded to the call of the country and the needs of the times by developing online teaching, using modern information technology to enable foreign students to continue learning Chinese. And the online teaching mode can still be retained as a new teaching mode after the pandemic is controlled. However, in the process of online TCSL, some defects and shortcomings of online teaching are gradually revealed, which have a certain negative impact on students' Chinese learning. This article analyzes the impact of the pandemic on the TCSL and emphasizes the urgency of online teaching in the pandemic environment. At the same time, this article explores the challenges faced by online TCSL and proposes strategies, putting forward personal thoughts and suggestions for the progress and optimization of online TCSL.
Sentiment Analysis Based on User Opinions on Twitter Using Machine Learning

The growing influence of users on social networks has caused positive and negative content to spread across the Internet. This paper arises from the need to have new tools that allow an analysis of sentiments based on the opinions made by users on the social network Twitter. In particular, the results of identifying the positive and negative opinions issued to a user are presented in order to develop effective communication strategies through social networks. The results obtained allow decisions to be made to obtain a competitive advantage, evaluating positive opinions or, failing that, identifying negative comments to establish strategies to overcome user dissatisfaction. Sentiment analysis can be performed in several ways; however, to obtain greater precision, naïve bayes classifier was used as a machine learning technique, obtaining an accuracy value of 86.2% in the evaluation of the model.
Augmented Reality-Centered Position Navigation for Wearable Devices with Machine Learning Techniques

People have always relied on some form of instrument to assist them to get to their destination, from hand-drawn maps and compasses to technology-based navigation systems. Many individuals these days have a smartphone with them at all times, making it a common part of their routine. Using GPS technology, these cellphones offer applications such as Google Maps that let people find their way around the outside world. Indoor navigation, on the other hand, does not offer the same level of precision. The development of indoor navigation systems is continuously ongoing. Bluetooth, Wi-Fi, RFID, and computer vision are some of the existing technologies used for interior navigation in current systems. In this article, we discuss the shortcomings of current indoor navigation solutions and offer an alternative approach based on augmented reality and ARCore. Navigating an indoor environment is made easier with ARCore, which brings augmented reality to your smartphone or tablet.