Reviewerly/exHarmony huggingface dataset:
Decentralized E-Voting System Using Ethereum Blockchain Technology

Due to the increase in the use of internet web applications in today's world, multiple new ways have been brought in to change existing methods to make them hassle-free and efficient.E-Voting systems are one new change that has been brought over and is also being used in multiple countries as it is both cost and time-efficient when compared to the traditional voting systems. For an E-voting system the user, who in this case is the voter, requires a web browser and server for the user's verification and authentication. Usually, the voter can access a centralized database where he casts his vote and the result for the vote that has been cast is also not shown. Other security issues are also encountered when one uses a centralized database as data manipulation remains a possibility.The main focus of this paper is to develop an E-voting system that is made on a blockchain with a decentralized model to ensure that the whole process is reliable, efficient, flexible, and most importantly transparent.
Simultaneous quantum estimation of phase and indistinguishability in a two-photon interferometer

With the rapid development of quantum technologies in recent years, the need for high sensitivity measuring techniques has become a key issue. In particular, optical sensors based on quantum states of light have proven to be optimal resources for high precision interferometry. Nevertheless, their performance may be severely affected by the presence of noise or imperfections. In this work we derive the quantum Fisher information matrix associated to the simultaneous estimation of an interferometric phase and the indistinguishability characterizing the probe state consisting of an even number of photons. We find the optimal measurement attaining the ultimate precision for both parameters in a single setup and perform an experiment based on a pair of photons with an unknown degree of indistinguishability entering a two-port interferometer.
Study on the trajectory measurement filter algorithm for an adaptive IMM aerial vehicle

A motion state model set for maneuvering target has been established to solve the problem of the increased trajectory measurement error in the maneuvering process of aerial vehicle. The low accuracy of filter caused by the incomplete description of motion state for the single model has been solved by utilizing the interacting multiple model (IMM) algorithm. The adaptive factor has been introduced to the Unscented Kalman Filter (UFK) to resolve the inaccurate observation error prior by adjusting the process noise covariance matrix in real time. The adaptive probability conversion factor based on compression ratio has been added to the IMM algorithm, enhancing its convergence rate. At last, a new AIMM-AUKF trajectory measurement filter algorithm has been formed so that the high accuracy measurement for the trajectory of random maneuvering target has been achieved and the validity the proposed algorithm has been verified through simulation.
Learning Category Distribution for Text Classification

Label smoothing has a wide range of applications in the machine learning field. Nonetheless, label smoothing only softens the targets by adding a uniform distribution into a one-hot vector, which cannot truthfully reflect the underlying relations among categories. However, learning category relations is of vital importance in many fields such as emotion taxonomy and open set recognition. In this work, we propose a method to obtain the label distribution for each category (category distribution) to reveal category relations. Furthermore, based on the learned category distribution, we calculate new soft targets to improve the performance of model classification. Compared with existing methods, our algorithm can improve neural network models without any side information or additional neural network module by considering category relations. Extensive experiments have been conducted on four original datasets and 10 constructed noisy datasets with three basic neural network models to validate our algorithm. The results demonstrate the effectiveness of our algorithm on the classification task. In addition, three experiments (arrangement, clustering, and similarity) are also conducted to validate the intrinsic quality of the learned category distribution. The results indicate that the learned category distribution can well express underlying relations among categories.
Compliance Checking of Cloud Providers: Design and Implementation

The recognition of capabilities supplied by cloud systems is presently growing. Collecting or sharing healthcare data and sensitive information especially during the Covid-19 pandemic has motivated organizations and enterprises to leverage the upsides coming from cloud-based applications. However, the privacy of electronic data in such applications remains a significant challenge for cloud vendors to adapt their solutions with existing privacy legislation standards such as general data protection regulation (GDPR). This article first proposes a formal model and verification for data usage requests of providers in a cloud composite service using a model checking tool. A cloud pharmacy scenario is presented to illustrate the connectivity of providers in the composite service and the stream of their requests for both collection and movement of patient data. A set of verifications is then undertaken over the pharmacy service in accordance with three significant GDPR obligations, namely user consent, data access, and data transfer. Following that, the article designs and implements a cloud container virtualization based on the verified formal model realizing GDPR requirements. The container makes use of some enforcement smart contracts to only proceed with the providers’ requests that are compliant with GDPR. Finally, several experiments are provided to investigate the performance of our approach in terms of time, memory, and cost.
Data Block Matrix and Hyperledger Implementation: Extending Distributed Ledger Technology for Privacy Requirements

Distributed ledger technology (DLT) , including blockchain, has a number of properties that make it useful for distributed systems. However, the immutability of blockchain and most forms of DLT make it impossible to delete data, as is required for compliance with many privacy rules regarding personally identifiable information. Thus, there is a need for DLT that can provide the integrity-preserving property of DLT while also allowing support for privacy rules. The data block matrix (DBM) is a variant of distributed ledger technology. It provides the integrity assurance of blockchain but allows for controlled revision or deletion of data. This property is essential for using DLT in applications that must guarantee privacy requirements by the deleting of a user's private data at their request. The DBM design solves the blockchain privacy conflict thus expanding the range of blockchain applications by also allowing exception management. It has been implemented and is available ( https://csrc.nist.gov/projects/redactable-distributed-ledger ) as a configurable option for Hyperledger Fabric (HF) , with a proof-of-concept application for data sharing in a health care environment. Other potential applications include logistics management and digital currency. This paper will cover the DBM properties and data structure, the DBM implementation in HF, and a use case and application design of the DBM implementation using the pharmaceutical industry supply chain.
Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data

Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared. A focus has been placed on the analysis of a transition to dimensionless input quantities. The methods used in this paper are the support vector machine, two-tiered support vector machine, random forest, gradient-boosted trees and long-short term memory. The performance between different models is remarkably similar, with the support vector machine attaining a slightly better accuracy score. The similarity could indicate issues with the dataset, but further study is required to confirm this. Both the two-tiered model and long-short term memory performed below expectations. The former could be attributed to an implementation which did not allow error propagation between tiers. The latter could be attributed to high noise and low frequency of the input signals. Dimensionless models experienced an expected decrease in performance, caused by a loss of information in the conversion. However, random forest and gradient boosted trees experienced a significantly lower decrease, making them more suitable for dimensionless predictors. From the disruption detection times, it was concluded that several disruptions could be predicted at more than 600 ms in advance. A feature importance study using the random forest indicated the negative impact of high noise and missing data in the database, suggesting improvements in data preparation for future work and the potential reevaluation of some of the selected portable features due to poor performance.
The Facilitation of Digital Literacy in EFL Classes

Digital literacy has become an essential form of literacy for the students of the 21st century. This development had been significantly accelerated by the Covid- 19 pandemic, when all students in Austria had to switch to distance learning at some point between the years 2020 and 2022. Based on a web-based survey conducted among Austrian EFL students, this paper aims to investigate the current status of digital literacy among 5th and 6th grade EFL students from their perspective. A special focus is placed on students who have received a digital device for e-Learning purposes within the school year 2021/2022. The following key results were observed: Students have a generally positive attitude towards e-Learning that also shows a connection to their level of digital skills. Furthermore, there are significant differences in experiences and attitudes toward e- Learning based on grade level. Further research into manifold areas of digital literacy could be beneficial in order to gain a deeper understanding of the topic and practical assistance for educators and students.
SENTIMENT ANALYSIS ON COVID-19 RELATED TWEETS

1. Introduction COVID-19[1] is a pandemic going on during the current time. COVID-19 poses a new challenge to humankind. Meanwhile, It is essential to know what the majority of people thinking about this pandemic. Sentiment analysis[2] is a widespread technique applied in field of marketing, customer feedback and consumer research. This project performs sentiment analysis on COVID-19 related tweets[3] and getting some higher idea about people's sentiment toward pandemic. After performing sentimental analysis on COVID-19 related tweets, this project also draws some interesting findings and conclusions in the last section. Performing sentiment analysis on social media data like tweets, Facebook comments are challenging task for some reasons .1) Due to high volume, data may or may not be in csv, excel ,or other text-rich formats. Performing operations on data needs 2) To draw human sentiment from raw text itself complex task for a machine. However, recently there are advanced deep learning based methods available for performing such a complex task. For example, Answering the question by chatbot or auto-compilation of a statement in email.[4]. This project takes NLP(Natural Language Processing) approach with the help of Google's pre-trained deep neural network called BERT[5] for identifying sentiment from raw text data. The layers of deep neural network implemented in PyTorch[6].
Application of Survey, Question, Read, Reflect, Recite, and Review (SQ4R) Reading Method in Learning to Read Argumentation Text by Class X Students

The diversity of reading methods does not mean that learning activities must use in various ways. Using a plan that suits the material and needs involves using multiple techniques. This research aimed to obtain the ability to read argumentation texts by students of SMA Negeri 1 Dolok Panribuan using the SQ4R method. The research method used is the experimental method. This research was conducted at SMA Negeri 1 Dolok Panribuan. In particular, the research was carried out in class X. The empirical research design was a group pre-test and post-test design. The research instrument used to obtain data is by using a test. The study results mean that the average value of student learning outcomes in the experimental class before being given less/low treatment and after being given treatment using the SQ4R method increased. This indicates an increase in student learning outcomes after being given treatment. This study aims to improve students' reading skills in understanding argumentation texts and applying the SQ4R method to other texts or discourses.
Graph-Augmentation-Free Self-Supervised Learning for Social Recommendation

Social recommendation systems can improve recommendation quality in cases of sparse user–item interaction data, which has attracted the industry’s attention. In reality, social recommendation systems mostly mine real user preferences from social networks. However, trust relationships in social networks are complex and it is difficult to extract valuable user preference information, which worsens recommendation performance. To address this problem, this paper proposes a social recommendation algorithm based on multi-graph contrastive learning. To ensure the reliability of user preferences, the algorithm builds multiple enhanced user relationship views of the user’s social network and encodes multi-view high-order relationship learning node representations using graph and hypergraph convolutional networks. Considering the effect of the long-tail phenomenon, graph-augmentation-free self-supervised learning is used as an auxiliary task to contrastively enhance node representations by adding uniform noise to each layer of encoder embeddings. Three open datasets were used to evaluate the algorithm, and it was compared to well-known recommendation systems. The experimental studies demonstrated the superiority of the algorithm.
Analysis of Social Acceptance for the Use of Digital Identities

According to a study by the German Federal Printing Office (2022), every European lives with 90 digital identities on average, and the trend is rising. The German government has launched the innovation competition “Digital Identities Showcase” to select and promote identity projects for data security and sovereignty. The funding amount is 50 million EUR to develop software, research practical use cases and implement them by 2024. Of course, this large sum presupposes acceptance for the use of such digital identities, especially against the backdrop of critical opinions from the media and society, as already outlined in a Canadian study. However, there is little academic research on blockchain technology, but almost no article on the use of digital identities based on blockchain technology. This paper conducts a quantitative study on the social acceptance of digital identities using a questionnaire-based survey with 324 German participants on the social acceptance of the use of digital identities. The result of the study is that social acceptance of the use of digital identities is significantly influenced by demographics, citizens’ experience with blockchain products, affinity with financial products and privacy concerns.
An efficient approach for traffic sign detection, classification, and localization applied for autonomous intelligent vehicles

In this paper, an efficient approach for traffic sign detection, classification, and localization is introduced. An integration of the tiling technique with the RetinaFace model and the MobileNetV1-SSD was proposed for traffic detection and classification. The combination of the extraction of the region of interest with a depth estimation model, namely the AANet+, was introduced for the traffic sign localization task. All models were developed based on the transfer learning technique and existing datasets, including the Zalo and ApolloScape datasets. The accuracy and the computational efficiency of the approach are evaluated. Experimental results show that the novel traffic sign detection and classification method outperform the existing ones with an average precision of 77.2%. Moreover, the computing performance achieved is 5 FPS on Jetson Nano and 50 FPS on Jetson Xavier. For the traffic sign localization, the relative error can be reduced to 3.78%, and the computing time is 1.965 s/pair on Jetson Nano and 0.128 s/pair on Jetson Xavier, while the existing method has lower accuracy and is more time-consuming.
An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning

Current research on knowledge graph construction is focused chiefly on general-purpose fields, whereas constructing knowledge graphs in vertically segmented professional fields faces numerous difficulties. To solve the problems of complex relation types of domain entities, the lack of a large amount of annotated corpus, and the difficulty of extraction, this study proposed a method for constructing domain-annotated datasets based on publicly available texts on the web, which integrates remote supervision and semi-supervision. For the relational triad extraction of a given core entity (an entity lexicon defined semi-automatically by experts), an inflated gate attention network structure for increasing the perceptual field of the model is proposed. In addition, a relational extraction model, Ro-DGANet, was designed based on this structure, incorporating the idea of a probability graph. The Ro-DGANet model was experimentally evaluated on the publicly available Chinese datasets LIC2019 and CHIP2020 and compared with the mainstream relation extraction models, achieving the best results with F1 values of 82.99% and 66.39%, respectively. Finally, the Ro-DGANet model was applied to the relation extraction task of equipment components in industrial scenarios and to the relation extraction task of core knowledge points of programming languages. The analysis results show that the proposed method is applicable to open relation extraction among core entities in different domains with reliable performance and portability.
Identifying Adversary Impact Using End User Verifiable Key with Permutation Framework

In the Internet of Things (IoT), security is a crucial aspect that ensures secure communication, transactions, and authentication for different applications. In IoT security, maintaining the user interface and platform security is a critical issue that needs to be addressed due to leaky security distribution. During communication, synchronisation and security are important problems. The security problems are caused by the adversary impact and vulnerable attacks, leading to service failure. Therefore, the Permutated Security Framework (PSF) is designed to manage security in the IoT by providing secure communication, transactions, and authentication for different applications. The PSF uses time intervals to manage transaction security. These intervals are secured using end-verifiable keys generated using the conventional Rivest–Shamir–Adleman (RSA) technique in IoT-based communication-related applications. In this approach, the key validity is first provided for the interval, and in the latter, the access permitted time modifies its validity. The security of transactions is managed by dividing time into smaller intervals and providing different levels of security for each interval. By using time intervals, the framework is adaptable and adjustable to changes in the system, such as user density and service allocation rate, adapting parallel transactions per support vector classifications’ recommendations. The proposed framework aims to synchronise interval security, service allocation, and user flexibility to mitigate adversary impact, service failures, and service delays while improving the access rate and transactions. This allows for more flexibility and better management of transaction security. The proposed framework reduces adversary impact (10.98%), service failure (11.82%), and service delay (10.19%) and improves the access rate by 7.73% for different transactions.
Sharing Experiences and Seeking Informal Justice Online: A Grounded Theory Analysis of Zoombombing Victimization on Reddit

During the COVID-19 pandemic, Zoombombing emerged as a new form of online disruption/harassment characterized by unintended and unsolicited virtual visits by both strangers and known individuals via Zoom. The current study utilizes a grounded theory-based qualitative analysis of over 1,000 posts on Reddit to explore discussions around Zoombombing victimization incidents. This paper reveals how Zoombombing victimization subreddits function as communities for sharing victimization stories, user perceptions, and support while further developing a space that promotes informal justice online. The implications include an enhanced understanding of how Zoombombing occurs and the role of online forums and cyberpolicing tools in preventing and discussing victimization.
Video Stream Recognition Using Bitstream Shape for Mobile Network QoE

Video streaming service delivery is a challenging task for mobile network operators. Knowing which services clients are using could help ensure a specific quality of service and manage the users' experience. Additionally, mobile network operators could apply throttle, traffic prioritization, or differentiated pricing. However, due to the growth of encrypted Internet traffic, it has become difficult for network operators to recognize the type of service used by their clients. In this article, we propose and evaluate a method for recognizing video streams solely based on the shape of the bitstream on a cellular network communication channel. To classify bitstreams, we used a convolutional neural network that was trained on a dataset of download and upload bitstreams collected by the authors. We demonstrate that our proposed method achieves an accuracy of over 90% in recognizing video streams from real-world mobile network traffic data.