Reviewerly/exHarmony huggingface dataset:
A Countermeasure for Double Spending Attacks on Blockchain Technology in Smart Grid

As a distributed technology, blockchain has been applied in many fields. Much research has been done on its inherent security issues. Among these security issues, double spending is one of the most pernicious. Current countermeasures are not systematic, they either focus on monitoring or detection with no effective strategy to prevent future double spending. These countermeasures also have serious drawbacks, such as high network traffic, high CPU utilization, and heavy management overhead. In this paper, we present a systematic approach to address double spending attack on smart grid. A reputable node is selected, which constantly compares all transactions in current time window with previously validated block and current block. Upon discovering conflicting transactions, a warning message with the conflicting transaction and two penalty transactions are broadcasted to the network to stop the current attack and to prevent future attacks. Our experiment has demonstrated our design is highly effective to detect double spending, with short detection time and low CPU utilizations.
Mechanisms for Digital Transformation in the Education and Healthcare Sectors Utilizing Decentralized Self-Sovereign Identity

This paper argues for the consideration of a decentralized, open, interoperable identity framework as a secure, scalable, user-centered meta-platform capable of leveraging many aggregate network advantages and delivery options for education and healthcare providers. An overview of the shortfalls and vulnerabilities of the current Internet and systems for identity management is first explained, followed by a summary of the status of development and primary proponents of decentralized, blockchain-enabled, selfsovereign identification (SSI). An examination of the Key Event Receipt Infrastructure (KERI) open-source decentralized key management infrastructure (DKMI) and its primary root-of-trust in self-certifying identifiers (SCID) is evaluated. This paper recommends KERI for consideration as a potential metaplatform overlay and solution for both the education and health industries as a means of attaining their primary goal of being more user versus institution-centric in their core interactions and processes. Finally, some pathways for future research are recommended.
AUTOMATED NOTE MAKER FROM AUDIO RECORDING

Today speech technologies are commonly available for a limited but interesting range of tasks.The technologies enable machines to respond correctly and reliably to human voices and provide useful and valuable services.As communicating with a computer is faster using voice rather than using a keyboard, people will prefer such a system.Communication among human beings is dominated by spoken language, therefore it is natural for people to expect voice interfaces with computers.can be accomplished by developing a voice recognition system -speech-to-text which allows the computer to translate voice requests and dictation into text.Voice recognition system -speech-to-text is the process of converting an acoustic signal which is captured using a microphone to a set of words.The recorded data can be used for document preparation.In the project is to able automated notes maker from audio recordings application based on AI, ML to make the computer understand speech commands and convert it into text and then PDF/WORD format.
An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts

This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.
Two algorithms for excited-states quantum solvers: Theory and application to EOM-UCCSD

Near-term quantum devices promise to revolutionize quantum chemistry, but simulations with the current noisy intermediate-scale quantum (NISQ) devices are not practical due to their high susceptibility to errors. This motivated the design of NISQ algorithms that leverage classical and quantum resources. While several developments have shown promising results for ground-state simulations, extending the algorithms to excited states remains challenging. This paper presents two cost-efficient excited-state algorithms inspired by the classic Davidson algorithm. We implemented the Davidson method into the quantum version of the equation-of-motion unitary coupled-cluster (qEOM-UCC) excited-state method adapted for quantum hardware. The circuit strategies to generate desired excited states are discussed, implemented, and tested. We demonstrate the performance and accuracy of the proposed algorithms (qEOM-UCC/Davidson and its variational variant) by simulations of H2, H4, LiH, and H2O molecules. Similarly to the classic Davidson scheme, the qEOM-UCC/Davidson algorithms are capable of targeting a small number of excited states of the desired character.
A Systems-Theoretic Perspective to the Translation of Company Profiles from Chinese into English

A translation system is made up of a source text subsystem and a target text subsystem. The elements in the system are interrelated and exert influence on each other. Translation studies from a systems-theoretic perspective focus primarily on literary texts, while business discourses are seldom explored. In order to assess the translation quality of company profiles, a three-level model from the systems theory perspective has been proposed. The three levels are the preliminary data level, the discourse level, and the contextual level. The results show that the translator should attach great importance to improving and restructuring the source text and reconstructing experiential meaning, interpersonal meaning, and textual meaning. The translation should be reader-oriented and adapt to the cultural context of the target language.
Unveiling the Art of Food Vlogging: A Multimodal Discourse Analysis of Food Review Vlogs

Vlogs are among the videos with the biggest viewership on different social media sites, which led to academic interest and analyses. This study aims to identify the speech acts used by popular food vloggers in their food vlogs and investigate how the various modalities they used create a perlocutionary effect on the audience in the form of reactions. Furthermore, the study looked into how the modalities observed shape the discourse of food vlogging. This descriptive study explored the genre’s apparent unique discourse community through the food vlogs by Mikey Chen, Mark Wiens and Luke Martin in Binondo, Philippines. The corpora which were specific videos and vloggers were chosen through purposive sampling. The linguistic aspect of the data was analyzed by categorizing the utterances into different speech acts, whereas the types of multimodalities present in each of the vlogs were also examined. Results of the study revealed that food vlogs share a common goal of describing food and enticing curiosity of viewers. It was also found that food vlogs thrive in modes and languages that are exclusive to the food vlogging community. Food vloggers encourage interaction via the comment section. Moreover, food vlogging has a unique genre with specific lexis that includes food, culinary and complimentary terms. Most importantly, food vloggers and viewers in specific food vlogs contribute to the expanding food vlogging community that has its unique discourse that is shaped by its distinctive modalities and structure.
Influence of Social Media on Purchasing Decision in Chennai

The study's goals are to determine whether social media has an impact on the general public, to assess the degree to which reviews on social media affect people's purchase decisions, to look into the relationship between the influence of advertisements on consumers' purchase decisions and marital status, and to examine the relationship between social media product advertisements and respondent age. Making a purchase is a fundamental leadership activity that customers use to display their interactions both before and after making a purchase of a good or service. It is frequently seen as a particular kind of cost-benefit analysis given the potential of various alternatives. A few basic cases include shopping and choosing what to eat. This showcase of basic leadership is a mental process. The study used 1450 respondents for the purpose of understanding the buying decision. Chi-square, independent t test and correlation are the various tests used in this research. According to the study, men and women view social media advertising from different angles. Customers are less concerned about their purchase decisions as income levels rise. Teenagers only are targeted by the majority of social media product marketing. Relationship status has no bearing on how effective advertising is in influencing consumer decisions.
AMST2: Aggregated Multi-Level Spatial and Temporal Context-Based Transformer for Robust Aerial Tracking

Abstract Recently, many existing visual trackers have made significant progress by incorporating either spatial information from multi-level convolution layers or temporal information for tracking. However, the complementary advantages of both spatial and temporal information cannot be leveraged when these two types of information are used separately. In this paper, we present a new approach for robust visual tracking using a transformer-based model that incorporates both spatial and temporal context information at multiple levels. To integrate the refined similarity maps through multi-level spatial and temporal encoders, we propose an aggregation encoder. Consequently, the output of the proposed aggregation encoder contains useful features that integrate the global contexts of multi-level spatial and the temporal contexts. The contrasting yet complementary representation of multi-level spatial and temporal contexts provided by this feature can help prevent tracking failures in a range of complex aerial scenarios, including occlusion, motion blur, and scale variations. Additionally, the proposed architecture can achieve more robust object tracking against significant variations by updating the features of the latest object while retaining the initial template information. Extensive experiments on five challenging short-term and long-term aerial tracking benchmarks have demonstrated that the proposed tracker outperforms state-of-the-art tracking methods in terms of both real-time processing speed and performance.
Under the Radar: Analyzing Recent Twitter Information Operations to Improve Detection and Removal of Malicious Actors, Part 2

This report builds upon the work done in part one (DOI:10.2139/ssrn.4389821) of this series by examining the network structure of three information operations (IOs) that were removed from Twitter in 2021. The analysis that follows uses social network analysis (SNA) to explore the structure, key network statistics, and measures of centrality for network graphs created from Twitter mentions. Five data sets feature in this analysis, three IO networks and two control networks. Data for the three IO networks came from Twitterâ€™s Transparency Center and contained tweets from a Russian, Chinese, and Iranian IO, respectively. In addition, two COVID-19 tweet data sets from Kaggle served as the controls. This project seeks to determine if it is possible to make cross-network comparisons that could enhance the early detection of IOs on social media platforms like Twitter. The analysis found that while each network was structurally unique, the key SNA statistics failed statistical significance testing when checking for differences between the IO group and control group. This may be the result of a small network sample size composed of a mere five networks (n=5). However, this study also found that measures of centrality had statistically significant differences between the IO group and the control group. This suggests that measures of centrality, particularly eigenvector centrality and Pagerank, could be useful metrics for differentiating IOs from legitimate Twitter conversations.
Multi-Step Solar Power Forecasting using Deep Learning Methods

For the effective use and integration of solar energy into the power system, precise forecasting of photovoltaic power generation is required. Currently, forecasting methods can accurately predict PV power with different step horizons, but they fail to maintain a good resolution of accurate forecasting when we increase the number of steps ahead. This paper addresses the forecasting of PV performance of a system with multi-step ahead by considering 1, 2 and 3 steps ahead of 15-minute scenarios using Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) which are kinds of Deep Learning method . The originality of this study is that the forecasted value of solar power is put against the real data issued from the PV system. All the obtained results are evaluated using different statistical indicators such as Mean Absolute Percentage Error (MAPE), Normalized Mean Square Error (NMSE), Coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and Forecast skill (FS). The low values of the different statistical indicators justify the goodness of the proposed approaches.
Effect of Academic Self-Efficacy on Students’ Thesis Writing Resilience

The most crucial inventory of literacy skills for students is completing a final project in the form of a thesis. However, many students experience high levels of anxiety during the guidance process, due to worries about conducting difficult research and demands from parents, as well as finding problems that can be used as titles, understanding how to write scientific papers, finding references, and meeting supervisors. This leads to stress among students, who feel pressured and lack confidence in their abilities, which is an important phenomenon studied here. Therefore, this study aims to determine the influence of student self-efficacy factors on their ability to complete their thesis. This research is a form of correlation between student self-efficacy and the ability to write a thesis, and it involved 117 seventh-semester students of the Islamic Religious Education study program at one of the Islamic universities in Jambi, Indonesia. The study used a Thesis Writing Resilience questionnaire consisting of 43 items and an Academic Self-Efficacy questionnaire consisting of 35 items. The study found a strong correlation between self-efficacy and the ability to complete a thesis with a correlation coefficient of -0.8. Therefore, there is a strong influence of student self-efficacy on their ability to write a thesis. Self-efficacy can reduce or decrease student anxiety in preparing a thesis. This level of academic self-efficacy is more influenced by the level of individual resilience to their beliefs and individual ability to master a task. By improving student self-efficacy, students can feel more confident and less anxious in the thesis writing process. Therefore, universities and educators should develop interventions to improve student self-efficacy, such as providing guidance and support to help students develop the necessary skills to complete their thesis successfully. Additionally, future research should explore other factors that may influence student self-efficacy, such as family support, cultural backgrounds, and personal motivation, to gain a more comprehensive understanding of the topic.
Authorship and Inventorship of AI-Generated Works/Inventions

Artificial Intelligence (AI) has advanced swiftly around us and has become an essential part of our daily lives. It helps us with e- payment, serves as a smart assistant, helps to adjust the room temperature automatically, conserves energy by automatically switching the lights on/off when it detects a human presence, etc.Artificial Intelligence has evolved over the past years and it seems to be extending its roots in almost every business sector. According to Tractica, revenue from the AI software market worldwide, AI is expected to reach 126 billion dollars in 2025.i The function and popularity of Artificial intelligence is on the rise because the work and energy required to perform daily task is increasing, so automating everyday tasks is a great idea and increasing output.Artificial Intelligence is revolutionizing industries with its applications and helping solve complex problems. With the continuous emergence of creative works and innovations by Artificial Intelligence systems, a lot of legal issues have become of urgent practical importance. Presently, there are legal cases in multiple jurisdictions involving AI-generated works, the role of AI in the creation of works of authorship, AI and fair use, training data, text and data mining exceptions to copyright infringement. However, this paper shall be considering the claims of authorship and inventorship of AI-generated works and inventions.
Supplementary material to "Measuring diameters and velocities of artificial raindrops with a neuromorphic dynamic vision sensor disdrometer"

Raindrop measurement with event camera: Supporting information 1 S. Supporting information: Methods and materialsThe Matlab code used for data analysis, raw data, simulations, graphs, photos and videos of the experiments is available from the following website: Raindrop measurement with event camera.This supplementary material has the following sections:• S.1 provides details on the Dynamic Vision Sensor (DVS) event camera.• S.2 describes our Hard Disk Droplet Generator (HDDG) and Intravenous Dripper Droplet Generator (IVDG).-S.2.1 details our droplet needle.-S.2.2 details construction of the HDDG.-S.2.3 provides additional useful methodology for testing the stability of the HDDG -S.2.4 describes unsuccessful attempts to develop hydrophobic coatings to reduce the IVDG droplet size.• S.3 details our experimental setups.• S.4 describes our experiments and the methods we used to measure droplets and estimate uncertainty, in particular -S.4.2 details our error analysis.-S.4.3 explains how we optimized our droplet illumination.-Secs.S.4.7,S.4.4,and S.4.5 explain our calibration of the optical magnification of the camera M , angle ↵, and droplet fall height.-S.4.6 explains how we measured the droplet diameter and speed in the image plane -S.4.10 provides formulas to compute the physical diameter and speed from the image plane measurements.-S.4.11 describes how we measure droplet flow rate for ground-truth droplet size estimates.-S.4.12 further details our ground truth (GT) measurement of droplet mass.• S.5 explains our model of the droplet speed versus fall height and droplet diameter, which we used to obtain our GT speed from mass and to ensure that droplets fell at close to terminal speed.
THE RECONCEPTUALIZATION OF THE CRAFT WORKSHOP IN RELATION TO «ARTS AND SCIENCES»

The well-established formula of «sciences and arts» contained the main principle of the knowledge structure of the eighteenth century, i.e. the combination of science and practice, science and technology, which meant a special place for the craft workshop as an experimental production space. At the same time, craft workshops, i.e. art chambers at the Academy of Sciences, turned into experimental laboratories of their time, where experimental science took shape. It marked the beginning of the industrial revolution, where fundamental (theory) and applied (practice) science in the development of industry and the entire economy played a decisive role. Peter I started a trend that is clearly visible today, which aimed to promote scientific achievements for the sphere of industry, using the creative potential of scientists, “artists”, artisans, and those referred to as technologists and engineers today. The study employs the methods of social topology.
Forecasting of Solar Irradiance Using Multivariate Hybrid

Solar energy is the most important clean and environmentally friendly renewable energy source. However, before usage of this source and while using, it is of vital importance to predict the amount of possible radiation in future to plan and operate of a photovoltaic system accurately. For this aim in this study, an advanced prediction technique is presented, and the performance of the technique is tested on real data. In prediction, real-time, multivariate solar irradiation data from a specific region in Turkey is employed. The proposed technique combines Long Short-Term Memory and Gated Recurrent Units models to increase speed and accuracy in the estimation process. Considerably impressive results with low MSE, RMSE, MAE, and high R2 values in just 25.63 milliseconds are obtained, and the results are compared and discussed with similar previous studies in the literature.
BLOCKCHAIN TECHNOLOGY ADOPTION IN BANKING AND FINANCIAL SECTOR: A LITERATURE REVIEW

Blockchain is a novel technology that is being used in the banking and financial industries to provide transparency, security, cost savings, and speed. There is a research gap that provides opportunities to analyze the factors influencing blockchain adoption, theories and models employed, methods for collecting data, and techniques used for data analysis in banking and financial sectors globally. Now, the purpose of the study is to provide a literature review. It was discovered after evaluating 114 publications and 20 selected studies that the best factors for blockchain technology adoption include theories and models, data collection methods, and data analysis techniques. The new model was proposed on the factors influencing blockchain technology adoption based on a study of the literature, and it will define the agenda for future research. This will be beneficial to bankers, academics, and researchers. This study presents a review by evaluating the factors of blockchain technology adoption in the banking and finance industry to date from previous studies.
Improved Whale Optimization Algorithm by Multi-mechanism Fusion

To solve the problems of insufficient global exploration ability, low convergence accuracy and slow speed of traditional whale optimization algorithm, an improved whale optimization algorithm by multi-mechanism fusion is proposed. Firstly, the algorithm uses the nonlinear parameter to coordinate the exploration and exploitation ability of the whale optimization algorithm. Secondly, combine with the Harris hawks optimization algorithm, it improves the global exploration and local optimization ability of the whale optimization algorithm. Finally, consider the important role of the fitness of the algorithm in the optimization, the Gaussian detection mechanism is proposed. The improved algorithm and other algorithms are simulated and tested on the eight variable dimension benchmark functions and design problems of tension spring. The results show that the improved whale optimization algorithm by multi-mechanism fusion has better robustness and stability, while ensure convergence accuracy and speed.