Reviewerly/exHarmony huggingface dataset:
Research on the Influence of Artificial Intelligence Technology with web 3.0 on Accounting Education and Its Countermeasures

With the continuous development of artificial intelligence (AI) technology, AI technology has been widely used in various fields of social and economic life, bringing great impact to various industries. AI is a section of computer science, a new science technology that studies and develops theories, methods, and application systems used. The development of artificial intelligence technology first has a profound impact on the development of the accounting industry, which leads to the change of the demand for accounting talents. This paper takes the accounting industry as an example, discusses the application of artificial intelligence technology in accounting practice, analyzes its impact on the reform of accounting education, and finally proposes countermeasures and suggestions for accounting education to deal with technological challenges with the blended learning for increasing student retention and engagement in an E-learning environment. Blended learning gives greater flexibility which means being able to complete assignments any place and any time. It successfully improves students’ experience and enhances their engagement. It uses apps, games, or measurable programs and helps to balance the classroom. Practice shows that the development of artificial intelligence technology not only has a profound and extensive impact on the accounting profession and accounting education, but also has a profound impact on engineering education and other fields. An in-depth study of the challenges of artificial intelligence technology to accounting education is also of reference and guiding significance for the reform and development of engineering education and other fields.
Imagined Futures

This chapter offers a glimpse into the future through the lens of science fiction authors who have served as modern-day prophets. In that sci-fi books, short stories, films, games, popular music, and TV shows have informed global culture, the news media, too, has been influenced by these transformative narratives. From the turn of the 20th century, what was once considered preposterous or even blasphemous to contemplate became everyday reality in just one lifetime. For example, it would take just 67 years after French silent film director Georges Melies' “Le Voyage dans la Lune” (1902) depicted a rocket ship poking the “Man on the Moon” in the eye before CBS news anchor Walter Cronkite would take off his trademark black-framed glasses, wipe his tears, and exhale, “Phew . . . boy,” as the Apollo 11 lunar lander softly touched down.
FlowFormers: Transformer-based Models for Real-time Network Flow Classification

Internet Service Providers (ISPs) often perform network traffic classification (NTC) to dimension network bandwidth, forecast future demand, assure the quality of experience to users, and protect against network attacks. With the rapid growth in data rates and traffic encryption, classification has to increasingly rely on stochastic behavioral patterns inferred using deep learning (DL) techniques. The two key challenges arising pertain to (a) high-speed and fine-grained feature extraction, and (b) efficient learning of behavioural traffic patterns by DL models. To overcome these challenges, we propose a novel network behaviour representation called FlowPrint that extracts per-flow time-series byte and packet-length patterns, agnostic to packet content. FlowPrint extraction is real-time, fine-grained, and amenable for implementation at Terabit speeds in modern P4-programmable switches. We then develop FlowFormers, which use attention-based Transformer encoders to enhance FlowPrint representation and thereby outperform conventional DL models on NTC tasks such as application type and provider classification. Lastly, we implement and evaluate FlowPrint and FlowFormers on live university network traffic, and achieve a 95% f1-score to classify popular application types within the first 10 seconds, going up to 97% within the first 30 seconds and achieve a 95+% f1-score to identify providers within video and conferencing traffic flows.
Use of AI to Help Researchers Improve their Research Funding Capacities, Relevance, and Performance

Researchers and scientists face globally, and parallel to their core research activities, increased pressure to successfully lead or participate in fundraising activities. The field has been experiencing fierce competition with success rates of proposals falling dramatically down, while the complexity of the funding instruments and the need for acquiring a wide understanding of issues related to impacts, research priorities in connection to wider national and transnational (e.g. EU-wide) policy aspects, increase discomfort levels for the individual researchers and scientists. In this paper, we suggest the use of transdisciplinary AI tools to support (semi-)- automation of several steps of the application and proposal preparation processes.
Introduction

Since Planck’s ‘quantum’ hypothesis was first proposed in the early 20th century, quantum mechanics has developed rapidly for more than a century, making people have a new understanding of the micro quantum world, and it has been applied to chemistry, biology, engineering electronics and other fields. Many new disciplines based on quantum science have also emerged from the developments. Over the past few decades, the combination of quantum mechanics and information sciences has induced a new research field: quantum information science [1–3]. Several countries have proposed detailed roadmap [4] to develop quantum science and technology, especially on quantum communication, quantum computing, quantum precision measurement, and so on [5–12].
Pedestrian Detection Algorithm Based on ViBe and YOLO

As more and more monitoring devices are deployed in various cities around the world, the technology of intelligent analysis and processing of video image data based on the computer is becoming more and more mature. This paper adopts an algorithm based on the combination of traditional ViBe and YOLO algorithm to realize the pedestrian detection of internal personnel in the surveillance video. Firstly, ViBe algorithm is used to detect pedestrians once, and some pedestrian frames are selected. Then the pedestrian frames are sent to YOLO network for secondary detection. The second pedestrian detection based on deep learning uses K-means algorithm to complete the clustering of prior frames, and then uses the CSPDarkNet53 network to extract pedestrian features. In order to improve the ability of YOLO small target detection, SPP-Net structure is added to the YOLO model to improve the accuracy of small target detection. The self-built pedestrian dataset used to train and test on the constructed network. The experimental results show that the detection algorithm based on the combination of ViBe and YOLO optimizes the regression of pedestrian boundary frame improves the positioning accuracy of pedestrians.
Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence

Recently it was shown, using the typical mutation mechanism that is used in evolutionary algorithms, that monotone conjunctions are provably evolvable under a specific set of Bernoulli $$(p)^n$$ distributions. A natural question is whether this mutation mechanism allows convergence under other distributions as well. Our experiments indicate that the answer to this question is affirmative and, at the very least, this mechanism converges under Bernoulli $$(p)^n$$ distributions outside of the known proved regime.
Combating Password Vulnerability with Keystroke Dynamics Featured by WiFi Sensing

The fast evolving credential attacks have been a great security challenge to current password-based information systems. Recently, biometrics factors like facial, iris, or fingerprint that are difficult to forge rise as key elements for designing passwordless authentication. However, capturing and analyzing such factors usually require special devices, hindering their feasibility and practicality. To this end, we present WiASK, a device-free WiFi sensing enabled Authentication System exploring Keystroke dynamics. More specifically, WiASK captures keystrokes of a user typing a pre-defined easy-to-remember string leveraging the existing WiFi infrastructure. But instead of focusing on the string itself which are vulnerable to password attacks, WiASK interprets the way it is typed, i.e., keystroke dynamics, into user identity, based on the biologically validated correlation between them. We prototype WiASK on the low-cost off-the-shelf WiFi devices and verify its performance in three real environments. Empirical results show that WiASK achieves on average 93.7% authentication accuracy, 2.5% false accept rate, and 5.1% false reject rate.
Distribution Consistency Penalty in the Quadratic Kappa Loss for Ordinal Regression of Imbalanced Datasets

Ordinal regression is a typical deep learning problem, which involves inherently ordered labels that are common in practical applications, especially in medical diagnosis tasks. To overcome the neglect of ordered or non-stationary property by merely exploiting classification or regression, quadratic weighted kappa (QWK) is proposed to be employed in the QWK loss function design as an efficient evaluation metric for ordinal regression. However, the paradox that kappa will be higher with an asymmetrical marginal histogram leads the QWK loss function to get the local optimal solution with all-zero-column in the confusion matrices during training. In practice, the all-zero column problem will result in a certain category not being detected at all, which can have serious consequences for the exclusion of pathology. To address this limitation, a new form of penalty term is proposed for the QWK loss function by penalizing the distance of marginal histogram to effectively avoid all-zero-column of the models. The experiments on the category-imbalanced datasets demonstrate that our penalty terms solve all-zero-column problem. On Adience dataset our penalty terms achieve 0.915 QWK, 0.446 MAE and 0.612 accuracy, while on DR dataset our penalty terms achieve 0.744 QWK, 0.281 MAE and 0.810 accuracy. Besides, experiments on the category-balanced datasets HCI show that our penalty terms achieve 0.810 QWK, 0.499 MAE and 0.610 accuracy.
Safeguarding GeoLocation for Social Media with Local Differential Privacy and L-Diversity

People are very active on social media nowadays. They share content, media and location information tied to the place they visit. This leads to disclosure of trends, business, political situation of a particular location, etc., which if exposed can harm the privacy of the user. In this work, we want to secure the privacy of the Geolocation data of a user with the help of Local Differential privacy and l-diversity. Local Differential Privacy (Local DP) is more secure than Global Differential Privacy (Global DP) because it adds noise at the device level only. So data, while leaving the device, is already perturbed making it safe to travel in network. Also, there is no need for trusted curator in Local DP, unlike Global DP where a trusted curator is needed. We have analysed the effectiveness of Local DP on location data and framed the results on the basis of the Hellinger Distance and Privacy Score. Our experiment results show that the Hellinger Distance of Local DP implemented data is 299, which is lower and better than sanitised data, which is 324.52. The privacy score of Local DP implemented data is also greater, i.e., 5.27% than 4.79% of sanitised data.
Finding Novel Links in COVID-19 Knowledge Graph Using Graph Embedding Techniques

With the rapid proliferation of scientific literature, it has become increasingly impossible for researchers to keep up with all published papers, especially in the biomedical fields with thousands of citations indexed every day. This has created a demand for algorithms to assist in literature search and discovery. A particular case is the literature related to SARS-CoV-2 where a large volume of papers was generated in a short span. As part of the 2021 Smoky Mountains Data Challenge, a COVID-19 knowledge graph constructed using links between concepts and papers from PubMed, Semantic MEDLINE, and CORD-19, was provided for analysis and knowledge mining. In this paper, we analyze this COVID-19 knowledge graph and implement various algorithms to predict as-yet-undiscovered links between concepts, using methods of embedding concepts in Euclidean space followed by link prediction using machine learning algorithms. Three embedding techniques: the Large-scale Information Network Embedding (LINE), the High-Order Proximity-preserved Embedding (HOPE) and the Structural Deep Network Embedding (SDNE) are implemented in conjunction with three machine learning algorithms (logistic regression, random forests, and feed forward neural-networks). We also implement GraphSAGE, another framework for inductive representation on large graphs. Among the methods, we observed that SDNE in conjunction with feed-forward neural network performed the best with an F1 score of 88.0% followed by GraphSAGE with F1 score of 86.3%. The predicted links are ranked using PageRank product to assess the relative importance of predictions. Finally, we visualize the knowledge graphs and predictions to gain insight into the structure of the graph.
A Robust Method for Simultaneous Unsupervised Feature Selection and Clustering

In many real applications, the collected data is usually high dimensionality of feature space and no labels are given, which will result in the “curse of dimensionality” problem and bring the challenge for the feature selection. Although tremendous efforts have been made in unsupervised feature selection, the existing methods neglect the robustness of jointly feature selection and clustering. In this paper, we present a robust simultaneous feature selection and clustering method. By introducing a robust loss function, the unsupervised linear discriminant analysis methods with two different strategies (the ratio trace and the trace ratio) are incorporated into this loss function, which can guarantee the selected features with robustness. Then two novel simultaneous unsupervised feature selection and clustering objective functions are presented by the proposed loss function equipped with $\ell_{2,1}$-norm. Two alternative iterative optimization algorithms are developed to solve these two objective functions. The comparative experimental results conducted on eight UCI datasets show that the proposed methods are better than other competing methods.
Optimization of English Machine Translation Algorithm Based on Internet Information Technology

Machine translation English technology uses computers to translate one natural language into another, and quickly convert between different languages. The translation efficiency of English automatic translation technology is low. In order to meet the needs of real-time application rather than people's expectations, the process of generating correct translation in English translation system is inseparable from the participation of translators. In this case, researchers turn their attention from automatic English translation to computer-aided English translation. The system provides translators with useful translation and auxiliary tools to help them review the translation. The interaction between the machine translation English system and the translator can guide the computer to decode and improve the quality of the output translation, so as to realize the combination of the high efficiency of the English translation system and the high accuracy of the translator. The current basic IMT method takes Internet information technology as an example to expand the scope of candidate translation elements, and designs an interactive interface based on the translator's cognitive translation process to improve the post-translation process. This paper introduces the research situation and subject source of relevant researchers in the field of interactive machine translation English in detail. The research results show that this method is effective. More computer experts and scholars also study machine translation English system and devote themselves to the research of Internet machine translation. Compared with the traditional translation methods, it improves the work efficiency.
Smart Human–Computer Interaction Interactive Virtual Control with Color-Marked Fingers for Smart City

This study proposed to use a camera to understand the movement of color-marked finger through the Microsoft Visual Studio platform to build a gesture-based wearable technology. The main purpose of this study is to detect and understand human gesture by using a camera to show the interaction of human capabilities that connect with both real and virtual world. The most important thing is to show a bunch of functionality which is very useful in our daily life. In detail, the proposed study combined two major components, a camera and a projector. The development of this study includes the integration of an electronic circuit with software. When a human gesture was detected by the camera, the signal or information was sent to a computer or an Android phone for further processing to analyze the meaning of the movement. The process from signal capturing until the analysis of the information is performed in a real-time condition. This study supports two types of platform; they are Windows and Android, which depends on user demand. Furthermore, the camera consists of HD lens to capture the picture precisely with a 720p resolution for a more accurate result. It can be viewed on a wide range of screen which is good at capturing lots of gesture information. In a nutshell, users can use this kind of technology to trace their movement status in order to make the future much more interactive instead of using a smartphone screen. It is a concept to let people understand that gesture technology enables motion in a 3D environment in a wonderful and amazing way for smart city purposes. It may change people’s lives and yet, due to the acceptable cost through computational intelligence technique for green smart cities.
A Quantitative Analysis in CTP images for Ischemic Stroke Lesion Segmentation

Due to the limitations of MR imaging availability, CT perfusion (CTP) has been used to determine the extension of stroke in early stages. But CT perfusion maps alone are not enough to segment the infarct core or penumbra precisely. So, the combination of noncontrast CT scans with CT perfusion maps would be accurate and efficient for the identification of the boundary of stroke lesions. In this paper, we aim to study the effect of reducing the perfusion maps number on the segmentation result and compare the performance of different CNN architectures such as Unet, ResUnet, and ResUnet++ using ISLES 2018 dataset.
An Extended Scheduling of Mobile Cloud using MBFD and SVM

Mobile cloud refers to a cloud network which supports mobility. The mobility provides a lot of benefits including the fast execution of user request and less wastage of resources. To encompass the services, the cloudlet use virtual machines (VMs) which not only share the load of the cloudlet but also increases the processing speed. This paper presents a unique allocation and utilization policy of mobile cloud which uses load management and VM allocation and migration policy to provide the best services to the user. The VM selection is complete via support vector machine (SVM). The paper uses modified best fit decreasing (MBFD) algorithm to settle down the VMs in the mobile environment.
Online revisions in Swedish as a foreign language – a longitudinal study

Abstract Writing is a complex process in which different sub-components both follow each other and interact with each other. Tracking and revising the text is a natural behaviour that helps the writer to shape the text in a desirable way - both in terms of its form and content. Previous studies of self-corrections by second language learners, for example, have shown that they most often correct spelling or formal, linguistic errors or that conceptual changes increase as language skills in the foreign language develop. This paper presents an analysis of online revisions with respect to the orientation of the corrections, i.e. typographical, linguistic and conceptual revisions. The texts were collected at the end of each semester during the first three years of language learning. The analysis has shown that, regardless of language level, foreign language learners in the study group focused most on the surface of the text, which was reflected in corrections of typographical errors. In addition, two revision patterns were observed: one characterised by a simultaneous focus on different dimensions when revising the text, while the other was characterised by a greater focus on one aspect of the text (usually typography), while the others received less attention.
Operating Physical Security- and Technology-Centered Programs

The philosophical basis for achieving security control is Situational Crime Prevention which draws upon personnel and physical and electronic technology. Resources like monitors, cameras, biometrics, sensors, contraband detection, and fire and life safety are discussed.
Knowledge Application to Crossover Operators in Genetic Algorithm for Solving the Traveling Salesman Problem

Genetic Algorithm often bears with Premature Convergence for solving combinatorial optimization problems but can be improved by modification at different prospectives. In this research, an effective knowledge is applied in the procedure of Genetic Algorithm used for solving TSP. The key concept for the proposed GA is a modification in the crossover operators by applying knowledge of smallest distant cities (shortest edge) assuming that it would improve the process to find the shortest path, by optimizing the new generations. As TSP is represented as a graph, considering cities as nodes and paths as edges, in the proposed approach, crossover point selection for crossover operators is selected upon the basis of minimum edge in the given graph. To test the effect of the proposed method of knowledge application, Linear Order Crossover, Cycle Crossover Operator, and Sequential Constructive Crossover are modified and results are proved on the randomly generated data sets for TSP. This optimization not only improved the shortest path but also reduced the problem of Premature Convergence.
Research on Application of Data Compression in Signal Correlation Processing

With the rapid development of information technology, the digital signal has exponentially exploded, which brings tremendous pressure to data storage, transmission and seriously affects the efficiency of signal processing. This paper aims to the data compression in signal correlation processing. It is verified that Huffman coding, Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT) can effectively reduce the data volume while maintaining the accuracy of the signal correlation results. For better application in practice, the influence of signal parameters on data compression is further researched. The experimental results show that the lossless compression method can compress the signal more than 3 times without correlation distortion, while lossy compression can reach a compression ratio of at least 20 on the premise of ensuring the accuracy of correlation. In addition, DCT compression results demonstrate that the compression performance of phase-modulated, higher signal-to-noise ratio, longer duration, and lower carrier frequency signal is better.