Reviewerly/exHarmony huggingface dataset:
A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews

Authors:
raw_author_name: Róbert Lakatos
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary; Doctoral School of Informatics, University of Debrecen, H-4032 Debrecen, Hungary
raw_author_name: Gergő Bogacsovics
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary; Doctoral School of Informatics, University of Debrecen, H-4032 Debrecen, Hungary
raw_author_name: Balázs Harangi
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary
raw_author_name: Attila Tiba
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary
raw_author_name: János Tóth
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary
raw_author_name: András Hajdu
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary

Abstract: The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task.
AGCN-Domain: Detecting Malicious Domains with Graph Convolutional Network and Attention Mechanism

Authors:
raw_author_name: Yixin Li
raw_affiliation_string: Big Data Center of State Grid Corporation of China, Xicheng District, Beijing 100052, China
raw_author_name: Lihua Yin
raw_affiliation_string: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China

Abstract: Domain Name System (DNS) plays an infrastructure role in providing the directory service for mapping domains to IPs on the Internet. Considering the foundation and openness of DNS, it is not surprising that adversaries register massive domains to enable multiple malicious activities, such as spam, command and control (C&amp;C), malware distribution, click fraud, etc. Therefore, detecting malicious domains is a significant topic in security research. Although a substantial quantity of research has been conducted, previous work has failed to fuse multiple relationship features to uncover the deep underlying relationships between domains, thus largely limiting their level of performance. In this paper, we proposed AGCN-Domain to detect malicious domains by combining various relations. The core concept behind our work is to analyze relations between domains according to their behaviors in multiple perspectives and fuse them intelligently. The AGCN-Domain model utilizes three relationships (client relation, resolution relation, and cname relation) to construct three relationship feature graphs to extract features and intelligently fuse the features extracted from the graphs through an attention mechanism. After the relationship features are extracted from the domain names, they are put into the trained classifier to be processed. Through our experiments, we have demonstrated the performance of our proposed AGCN-Domain model. With 10% initialized labels in the dataset, our AGCN-Domain model achieved an accuracy of 94.27% and the F1 score of 87.93%, significantly outperforming other methods in the comparative experiments.
Cyberbullying Text Identification based on Deep Learning and Transformer-based Language Models

Authors:
raw_author_name: Khalid Saifullah
raw_affiliation_string: Chittagong University of Engineering & Technology
raw_author_name: Muhammad Ibrahim Khan
raw_affiliation_string: Chittagong University of Engineering & Technology
raw_author_name: Suhaima Jamal
raw_affiliation_string: Georgia Southern University
raw_author_name: Iqbal H. Sarker
raw_affiliation_string: Edith Cowan University

Abstract: In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The intelligent BullyFilterNeT system devised overcomes Out-of-Vocabulary (OOV) challenges associated with non-contextual embeddings and addresses the limitations of context-aware feature representations. To facilitate a comprehensive understanding, three non-contextual embedding models GloVe, FastText, and Word2Vec are developed for feature extraction in Bengali. These embedding models are utilized in the classification models, employing three statistical models (SVM, SGD, Libsvm), and four deep learning models (CNN, VDCNN, LSTM, GRU). Additionally, the study employs six transformer-based language models: mBERT, bELECTRA, IndicBERT, XML-RoBERTa, DistilBERT, and BanglaBERT, respectively to overcome the limitations of earlier models. Remarkably, BanglaBERT-based BullyFilterNeT achieves the highest accuracy of 88.04% in our test set, underscoring its effectiveness in cyberbullying text identification in the Bengali language.
Automatic pipeline fault detection using one-dimensional convolutional bidirectional long short-term memory networks with wide first-layer kernels

Authors:
raw_author_name: Longguang Peng
raw_affiliation_string: School of Urban Construction, Yangtze University, Jingzhou, China
raw_author_name: Guofeng Du
raw_affiliation_string: School of Urban Construction, Yangtze University, Jingzhou, China
raw_author_name: Yuanqi Li
raw_affiliation_string: College of Civil Engineering, Tongji University, Shanghai, China
raw_author_name: Kai Zhou
raw_affiliation_string: School of Urban Construction, Yangtze University, Jingzhou, China
raw_author_name: Jicheng Zhang
raw_affiliation_string: School of Urban Construction, Yangtze University, Jingzhou, China

Abstract: Pipeline networks are crucial components of modern infrastructure, and ensuring their reliable operation is essential for sustainable development. The percussion-based methods are considered promising for detecting pipeline faults due to their avoidance of constant-contact sensors and ease of implementation. However, the majority of existing percussion-based methods suffer from limitations such as the requirement for manual feature extraction, as well as subpar noise resilience and adaptability. This paper introduces a one-dimensional convolutional bidirectional long short-term memory network with wide first-layer kernels for the classification of percussion-induced acoustic signals, thus achieving automatic identification of pipeline leakage and water deposit conditions. This approach directly extracts features from audio signals using wide first-layer convolutional kernels, eliminating the need for manual feature extraction. Additionally, it employs bidirectional long short-term memory to effectively capture long-term signal dependencies from both past and future contexts. To validate the effectiveness of the method, two case studies were conducted on three groups of pipes. The results show that the proposed method demonstrates superior noise resistance and adaptability compared to other methods, and it also exhibits strong applicability to other percussion signal datasets. Additionally, the impact of different first convolutional kernel sizes on the noise resistance and adaptive performance of the model was investigated, which provides robust guidance for the effective processing of percussion-induced acoustic signals.
Sensorimotor adaptation in virtual reality: Do instructions and body representation influence aftereffects?

Authors:
raw_author_name: Ulrike Schäfer
raw_affiliation_string: Human-Centered Computing Research Group, Freie Universität Berlin, Berlin, Germany

Abstract: Abstract Perturbations in virtual reality (VR) lead to sensorimotor adaptation during exposure, but also to aftereffects once the perturbation is no longer present. An experiment was conducted to investigate the impact of different task instructions and body representation on the magnitude and the persistence of these aftereffects. Participants completed the paradigm of sensorimotor adaptation in VR. They were assigned to one of three groups: control group, misinformation group or arrow group. The misinformation group and the arrow group were each compared to the control group to examine the effects of instruction and body representation. The misinformation group was given the incorrect instruction that in addition to the perturbation, a random error component was also built into the movement. The arrow group was presented a virtual arrow instead of a virtual hand. It was hypothesised that both would lead to a lower magnitude and persistence of the aftereffect because the object identity between hand and virtual representation would be reduced, and errors would be more strongly attributed to external causes. Misinformation led to lower persistence, while the arrow group showed no significant differences compared to the control group. The results suggest that information about the accuracy of the VR system can influence the aftereffects, which should be considered when developing VR instructions. No effects of body representation were found. One possible explanation is that the manipulated difference between abstract and realistic body representation was too small in terms of object identity.
AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Authors:
raw_author_name: Abdulaziz Aldoseri
raw_affiliation_string: Engineering Management Program, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar
raw_author_name: Khalifa N. Al-Khalifa
raw_affiliation_string: Engineering Management Program, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar
raw_author_name: Abdel Magid Hamouda
raw_affiliation_string: Engineering Management Program, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar

Abstract: Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.
Disrupting Central Banking or Shadow Central Banking

Authors:
raw_author_name: Marco Dell'Erba

Abstract: Abstract Private entities have recently attempted to invade the realm of central banking. This chapter identifies such activity as ‘shadow central banking’, and draws analogies to the previous emergence of ‘shadow banking’ more generally. The similarities between shadow banking and shadow central banking are clear when analysing the circumstances of their emergence, their pathologies, and the regulatory response. This chapter focuses on the evolving roles of central banks and private initiatives, as their activities have led to the rethinking of central banking activities and the form of money in modern capitalism. In doing this, it proposes that this disruption of traditional central banking activity stems from a new interpretation of the concept of money, one that defies the classic dichotomy of ‘central bank money’ versus ‘commercial bank money’. This chapter then analyses the main characteristics of shadow central banking and identifies specific commonalities not only with the shadow banking system, but also with historical systems that pre-existed the modern central banking framework. Finally, it considers the systemic risks, systemic conflicts of interest and opaque corporate governance mechanisms associated with shadow central banking and posits some policy considerations to address such risks.
Starting from the Basics

Authors:
raw_author_name: Marco Dell'Erba

Abstract: Abstract This chapter explores the problem of systematization entailed in smart contracts. From a commercial law perspective, it discusses whether the rules provided in the commercial context for the conclusion of contracts between two ‘human’ parties may be safely extended to blockchain smart contracts. After analysing smart contracts and their main properties, this chapter surveys the relevant North American and European regulations. The chapter suggests that applying the existing regulatory framework to the new emerging commercial practices is the best solution and emphasizes the need for uniform principles as a precondition for commercial practices to prosper. It takes into consideration important historical precedents, such as the Law of Merchants (lex mercatoria), and its new form, the so-called lex cryptographia (or cryptographica), as the correct paradigms to be applied to the globalized cryptographic-based context. Finally, this chapter emphasizes the need for both public regulation and self-regulation as two complementary sources in commercial law, and the role that self-regulatory organizations and industry representatives should play in the shift towards a new technologic area.
Enhancing Disruption

Authors:
raw_author_name: Marco Dell'Erba

Abstract: Abstract This chapter provides an analysis of initial coin offerings, their development in the market, and their role in shaping the development of the cryptoeconomy. It identifies the initial ICO ‘structural’ pattern and its subsequent evolutions. It then characterizes ICOs in three major jurisdictions—the United States, Europe, and Switzerland. Furthermore, this chapter considers specific unsolved issues as they emerged in the markets and legal systems. In doing so, it focuses on the lack of a definitive approach for regulating ICOs, as well as on specific promises that ICOs breached, in particular with respect to financial inclusion, disintermediation, and infrastructural transformations. Finally, this chapter identifies the main consequences brought by ICOs, with an emphasis on capital market structures and digital securities, which triggered multiple transformations at multiple levels, including corporate governance, development of digital assets, and non-fungible tokens.
Video deepfake detection using Particle Swarm Optimization improved deep neural networks

Authors:
raw_author_name: Leandro Cunha
raw_affiliation_string: Department of Computer Science, Royal Holloway, University of London, Surrey, TW20 0EX, UK
raw_author_name: Li Zhang
raw_affiliation_string: Department of Computer Science, Royal Holloway, University of London, Surrey, TW20 0EX, UK
raw_author_name: Bilal Sowan
raw_affiliation_string: Department of Business Intelligence and Data Analytics, University of Petra, Amman, 11196, Jordan
raw_author_name: Chee Peng Lim
raw_affiliation_string: Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, 3216, Australia
raw_author_name: Yinghui Kong
raw_affiliation_string: Department of Electronics and Communication Engineering, North China Electric Power University, Beijing, 102206, Hebei, China

Abstract: Abstract As complexity and capabilities of Artificial Intelligence technologies increase, so does its potential for misuse. Deepfake videos are an example. They are created with generative models which produce media that replicates the voices and faces of real people. Deepfake videos may be entertaining, but they may also put privacy and security at risk. A criminal may forge a video of a politician or another notable person in order to affect public opinions or deceive others. Approaches for detecting and protecting against these types of forgery must evolve as well as the methods of generation to ensure that proper information is supplied and to mitigate the risks associated with the fast evolution of deepfakes. This research exploits the effectiveness of deepfake detection algorithms with the application of a Particle Swarm Optimization (PSO) variant for hyperparameter selection. Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer learning for video forgery classification. A new PSO algorithm is proposed for hyperparameter search, which incorporates composite leaders and reinforcement learning-based search strategy allocation to mitigate premature convergence. To assess whether an image or a video is manipulated, both models are trained on datasets containing deepfake and genuine photographs and videos. The empirical results indicate that the proposed PSO-based EfficientNet-GRU and EfficientNet-B0 networks outperform the counterparts with manual and optimal learning configurations yielded by other search methods for several deepfake datasets.
Determination of the bilingualism coefficient among future subject teachers by the method of associative experiment

Authors:
raw_author_name: Leila Leonardovna Salekhova
raw_affiliation_string: Kazan Federal University

Abstract: The paper describes the essence of the phenomenon of bilingualism, types of bilingualism, approaches to its quantitative assessment, as well as the results of an associative experiment aimed at determining the level of bilingualism of future teachers studying at Kazan Federal University on a bilingual basis using Russian and Tatar languages. The experiment involved 40 students – future mathematics teachers in the 2nd and 3rd years of study. The scientific novelty of the research lies in determining that the level of the academic component of bilingual communicative competence among future bilingual teachers is higher than the level of their general communicative competence. As a result, it was found that at this stage of the implementation of the multilingual education model at Kazan Federal University, most future subject teachers have formed the coordinate and the compound type of Tatar-Russian bilingualism both in the field of general communication and academic language in the sphere of mathematics on the topic “Function”.
The significance of an authentic author’s fairy tale in developing the emotive competence of middle school students in foreign language classes in the additional education system

Authors:
raw_author_name: Nataliya Markovna Shlenskaya
raw_affiliation_string: Moscow State Humanitarian and Economic University
raw_author_name: Natalia Sergeevna Pletneva
raw_affiliation_string: Russian Biotechnological University (ROSBIOTECH)

Abstract: The purpose of the study is to identify didactic possibilities and provide theoretical justification for the feasibility of using an authentic literary fairy tale in the formation of emotive competence when teaching schoolchildren a foreign language in the system of additional education. The article analyzes the various didactic possibilities of literary fairy tales when teaching a foreign language to middle-level students. The scientific novelty lies in the development and testing of a set of exercises based on the literary fairy tale “A Bear Called Paddington” by Michael Bond, involving students in the process of foreign language communication and ensuring the formation of elements of emotive competence. As a result of working with fiction, children develop not only language skills, but also emotional intelligence. Students learn how to determine the emotional nature of a text and later use emotionally charged vocabulary in monologue speech, which contributes to the formation of emotive competence.
Research on dual robot collaboration method based on improved double ant colony algorithm

Authors:
raw_author_name: Xiaohui Jia
raw_author_name: Chunrui Tang
raw_author_name: Xiangbo Zhang
raw_author_name: Jinyue Liu

Abstract: Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.