Reviewerly/exHarmony huggingface dataset:
"Time" Spent in Youth's “Global Information Space” (Problems of Satisfaction of Reading or Information Need)

The rapid penetration of the Internet into our lives has a profound effect on the reading habits of young people, and the concept of access to information is gradually becoming synonymous with the concept of reading. Until the last twenty years, young people have traditionally met their information needs through reading. It was formed as a result of long historical traditions. There has always been a significant difference between "heard information" and "read information". The percentage of "read information" by young people with higher education should have been higher than the percentage of "listened to information". This percentage determined the intelligence of the young man and expressed his social and cultural qualities. Surveys show that young people spend "time reading" on the Internet and are content with getting "episodic" information instead of classical reading. Also, reading is not one of the main activities in the organization of leisure. Here, too, the main place is occupied by the Internet. Over the past twenty years, the “time” spent on the Internet has been growing dynamically and is crowding out various traditional activities for young people. Students of the Azerbaijan State University of Culture and Arts created a working group to study the classical reading skills of young people, conducted surveys and obtained interesting results. To study the classic reading habits of young people belonging to different categories, and to compare the results obtained, it is necessary to have certain standards and norms. Unfortunately, although the results are not comparable with other regions, in a country with a population of ten million and 54 universities, the attitude of young people towards classical reading reveals a certain pattern.
A study on improving turnover intention forecasting by solving imbalanced data problems: focusing on SMOTE and generative adversarial networks

Abstract This study aims to improve the accuracy of forecasting the turnover intention of new college graduates by solving the imbalance data problem. For this purpose, data from the Korea Employment Information Service's Job Mobility Survey (Graduates Occupations Mobility Survey: GOMS) for college graduates were used. This data includes various items such as turnover intention, personal characteristics, and job characteristics of new college graduates, and the class ratio of turnover intention is imbalanced. For solving the imbalance data problem, the synthetic minority over-sampling technique (SMOTE) and generative adversarial networks (GAN) were used to balance class variables to examine the improvement of turnover intention prediction accuracy. After deriving the factors affecting the turnover intention by referring to previous studies, a turnover intention prediction model was constructed, and the model's prediction accuracy was analyzed by reflecting each data. As a result of the analysis, the highest predictive accuracy was found in class balanced data through generative adversarial networks rather than class imbalanced original data and class balanced data through SMOTE. The academic implication of this study is that first, the diversity of data sampling methods was presented by expanding and applying GAN, which are widely used in unstructured data sampling fields such as images and images, to structured data in business administration fields such as this study. Second, two refining processes were performed on data generated using generative adversarial networks to suggest a method for refining only data corresponding to a more minority class. The practical implication of this study is that it suggested a plan to predict the turnover intention of new college graduates early through the establishment of a predictive model using public data and machine learning.
Monitoring industrial control systems via spatio-temporal graph neural networks

Massive amounts of industrial data, which are often gathered by industrial control systems (ICS), have been generated by the fast growth of industrial intelligence. One of the hottest topics in ICS is how to extract the most useful information from industrial "Big Data" and provide a more comprehensive service for monitoring the condition of industrial production processes. As the industrial environment gets increasingly complicated, production tasks change often and malicious attacks are on the rise. It remains a grand challenge to perform fine-grained anomaly detection in high-dimensional, noisy industrial data. In response to this problem, we propose a spatio-temporal graph neural network-based anomaly detection framework for fine-grained state monitoring of ICSs. First, based on prior knowledge, we propose a method for feature dimensionality reduction and dynamic graph modeling. After that, the variational mode decomposition (VMD) module is then utilized to remove noise from industrial data. Finally, we propose a spatio-temporal feature extraction module for fine-grained anomaly detection. Numerical experiments are conducted on a real-world ICS dataset called HAI. The results demonstrate that the proposed framework can effectively deal with high-dimensional, high-noise, and imbalanced industrial data. The framework's concepts are interconnected and extensible to various industrial scenarios, including metallurgy, smart shop floors, etc. In terms of recall, precision, and F1-Score, a comparison between the proposed framework and eight representative methods reveals the merits of the proposed framework.
Artificial intelligence and data analytics: Ethical implications for accounting

We explore the intersection of ethics and accounting information systems (AIS) with advanced technologies, and the impact on accountants. Ethical considerations evolve among individuals, organizations, and society, and rapid changes in technology influence ethical judgments, ethical decisions, and actions. Some question the ability of the accounting profession to deal with emerging, even disruptive, technologies, which are being seen as both opportunity and threat. Accountants are asked to use ethical judgment and professional skepticism when dealing with technology. Our research question is: What ethical challenges do Big Data analytics and Artificial Intelligence raise for professional accountants, and how can these challenges be overcome? We conduct a review of the academic and practitioner literature on ethics and AIS. We offer a discussion on the impact of disruptive technologies - Big Data and artificial intelligence - on professional accountants, suggesting that, instead of competing, the human and the machine would benefit by working in sync.
Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images

Earlier detection and classification of squamous cell carcinoma (OSCC) is a widespread issue for efficient treatment, enhancing survival rate, and reducing the death rate. Thus, it becomes necessary to design effective diagnosis models for assisting pathologists in the OSCC examination process. In recent times, deep learning (DL) models have exhibited considerable improvement in the design of effective computer-aided diagnosis models for OSCC using histopathological images. In this view, this paper develops a novel duck pack optimization with deep transfer learning enabled oral squamous cell carcinoma classification (DPODTL-OSC3) model using histopathological images. The goal of the DPODTL-OSC3 model is to improve the classifier outcomes of OSCC using histopathological images into normal and cancerous class labels. Finally, the variational autoencoder (VAE) model is utilized for the detection and classification of OSCC. The performance validation and comparative result analysis for the DPODTL-OSC3 model are tested using a histopathological imaging database.
Feature library-assisted surrogate model for evolutionary wrapper-based feature selection and classification

In recent years, wrapper-based feature selection (FS) using evolutionary algorithms has been widely studied due to its ability to search for and evaluate subsets of features based on populations. However, these methods often suffer from a high computational cost and a long computation time, mainly due to the process of evaluating the feature subsets according to the classification performance. In order to tackle this problem, this paper presents a feature library-assisted surrogate model (FL-SM), which aims to reduce the computational cost but maintain a good prediction accuracy. Unlike the existing surrogate models used in FS, the proposed method focuses on the feature level instead of the sample level: an FL is built by collecting the scores of all the features during the evolutionary search. Specifically, each solution (subset candidate) is pre-evaluated based on the FL using only simple operations to decide whether or not it deserves to be evaluated by the classifier, improving the efficiency of the FS algorithm. Meanwhile, because not evaluating a certain number of solutions may lead to inaccurate solution selection during the evolutionary search, dynamic individual selection criteria are proposed. In addition, an adaptive FL update operator is proposed to handle the dynamics of the evolved population; it ensures the real-time validity of the FL. Furthermore, we incorporate the proposed FL-SM into some state-of-the-art single- and multi-objective evolutionary FS methods. The experimental results on benchmark datasets show that with good flexibility and extendibility, FL-SM can effectively reduce the computational cost of wrapper-based FS and still obtain high-quality feature subsets. Among the five algorithms tested, the average computation time reduction was 34.87%; at the same time, there was no significant difference in the classification accuracy for 80% of the tests, and our method even improved the classification accuracy for 6% of the tests.
Depth map continuous super-resolution with local implicit guidance function

Depth super-resolution (SR) is an effective approach to compensate the resolution gap between the relative low resolution of current depth sensing devices and the increasing demands of high-resolution content in many nowadays applications and future scenarios. However, most convolutional neural networks (CNNs) for depth SR are designed for particular integer SR ratios ( e.g., 2×, 4×, or 8×) or their combinations, which limits its practical applications that usually demands fractional SR ratios. This paper proposes a depth map continuous SR (DCSR) framework that is able to achieve resolution adaptation at arbitrary SR ratios. Specifically, we proposed an encoder with a dual-stream architecture. Using depth map and the guided color image as input, each stream interacts with the other by a series of adaptive resize blocks (ARBs) to retain essential details and resize stabilization across features of various resolutions. In the decoder, depth information at arbitrary missing locations is fitted as the weighted prediction of neural implicit functions (NIFs) from latent codes containing relative coordinate, target pixel size, and fused depth-color features. Extensive experiments demonstrate that the proposed DCSR method provides high quality depth SR results at arbitrary ratios, which is a promising merit for practical applications. Code will be released upon the publication of this work.
FDTrans: Frequency Domain Transformer Model for predicting subtypes of lung cancer using multimodal data

Accurate identification of lung cancer subtypes in medical images is of great significance for the diagnosis and treatment of lung cancer. Despite substantial progress in existing methods, they remain challenging due to limited annotated datasets, large intra-class differences, and high inter-class similarities.To address these challenges, we propose a Frequency Domain Transformer Model (FDTrans) to identify patients' lung cancer subtypes using the TCGA lung cancer dataset. We add a pre-processing process to transfer histopathological images to the frequency domain using a block-based discrete cosine transform and design a coordinate Coordinate-Spatial Attention Module (CSAM) to obtain critical detail information by reassigning weights to the location information and channel information of different frequency vectors. Then, a Cross-Domain Transformer Block (CDTB) is designed for Y, Cb, and Cr channel features, capturing the long-term dependencies and global contextual connections between different component features. At the same time, feature extraction is performed on the genomic data to obtain specific features. Finally, the image branch and the gene branch are fused, and the classification result is output through the fully connected layer.In 10-fold cross-validation, the method achieves an AUC of 93.16% and overall accuracy of 92.33%, which is better than similar current lung cancer subtypes classification detection methods.This method can help physicians diagnose the subtypes classification of lung cancer in patients and can benefit from both spatial and frequency domain information.
Image dehazing algorithm based on optimized dark channel and haze-line priors of adaptive sky segmentation

When dealing with outdoor hazy images, traditional image dehazing algorithms are often affected by the sky regions, resulting in appearing color distortions and detail loss in the restored image. Therefore, we proposed an optimized dark channel and haze-line priors method based on adaptive sky segmentation to improve the quality of dehazed images including sky areas. The proposed algorithm segmented the sky region of a hazy image by using the Gaussian fitting curve and prior information of sky color rules to calculate the adaptive threshold. Then, an optimized dark channel prior method was used to obtain the light distribution image of the sky region, and the haze-line prior method was utilized to calculate the transmission of the foreground region. Finally, a minimization function was designed to optimize the transmission, and the dehazed images were restored with the atmospheric scattering model. Experimental results demonstrated that the presented dehazing framework could preserve more details of the sky area as well as restore the color constancy of the image with better visual effects. Compared with other algorithms, the results of the proposed algorithm could achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation values and provide the restored image with subjective visual effects closer to the real scene.
VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized is hence, of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper, we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to an intelligent cognitive agent that implements the logic behind displacing objects in the virtual environment. In the complete VEsNA vision, for which this paper provides the building blocks, the intelligent agent will be able to reason on this displacement and on its compliance with legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; further autonomous agents, representing workers in the factory, will be added to make the virtual environment even more realistic; explanations and suggestions will be passed back to the user by the chatbot.
Between decentralization and reintermediation: Blockchain platforms and the governance of ‘commons-led’ and ‘business-led’ energy transitions

Digitalization will play a vital role in the achievement of successful transitions towards more flexible, reliable and sustainable energy systems. The progressive availability of data from meters and sensors deployed across end-users and energy supply-chains constitute a first step towards this direction. Leveraging on such cross-sectorial trend, business developers find in decentralized ledger technologies (DLTs) – such as the blockchain – opportunities for value creation across a broad range of participants in energy systems. Peer-to-peer energy trading platforms interconnect legacy and novel participants leveraging renewable and storage capacity (small and utility-scale) with grid operators and within local microgrids or markets. In this way, near real time (bi-directional) exchanges can be secured and validated via tokenization and smart contracting. The proposed configurations enable governance arrangements between legacy and novel participants at the platform and sectorial level. This paper focuses on investigating the decentralized governance characteristics proposed by peer-to-peer energy trading platforms and briefly discusses their implications on legacy systems from a broad ‘relational’ (i.e. actor network, sociotechnical transitions) and ‘normative’ (i.e. energy justice) set of theories. The paper draws upon prior surveys and publicly available whitepapers from business initiatives (start-ups). For the purposes of this study, whitepapers constitute official written texts and potential sources of information for the study of emergent ways of governance and collective action leveraging this technology. Results from ground theory method (qualitative) leveraging an inductive content analysis approach, built four (4) main categories that explain: (1) the digital features of these platforms, (2) the participants and incentives involved (3) the purposes and benefits of the solutions and, (4) the governance dynamics arising from trading tokenized energy via smart contracting. Results reveal complex multi-level socio-technical interrelations at different layers and points towards opportunities for private and community-based solutions, end-user empowerment, democratic energy systems and polycentric governance while considering fractal planning in policy design.
Robust Graph Neural-Network-Based Encoder for Node and Edge Deep Anomaly Detection on Attributed Networks

The task of identifying anomalous users on attributed social networks requires the detection of users whose profile attributes and network structure significantly differ from those of the majority of the reference profiles. GNN-based models are well-suited for addressing the challenge of integrating network structure and node attributes into the learning process because they can efficiently incorporate demographic data, activity patterns, and other relevant information. Aggregate operations, such as sum or mean pooling, are utilized by Graph Neural Networks (GNNs) to combine the representations of neighboring nodes within a graph. However, these aggregate operations can cause problems in detecting anomalous nodes. There are two main issues to consider when utilizing aggregate operations in GNNs. Firstly, the presence of anomalous neighboring nodes may affect the representation of normal nodes, leading to false positives. Secondly, anomalous nodes may be overlooked as their representation is flattened during the aggregate operation, leading to false negatives. The proposed approach, AnomEn, is a robust graph neural network developed for anomaly detection. It addresses the challenges of false positives and false negatives using a weighted aggregate mechanism. This mechanism is designed to differentiate between a node’s own features and the features of its neighbors by placing greater emphasis on a node’s own features and less emphasis on its neighbors’ features. The system can preserve the node’s original characteristics, whether the node is normal or anomalous. This work proposes not only a robust graph neural network, namely, AnomEn, but also specific anomaly detection structures for nodes and edges. The proposed AnomEn method serves as the encoder in the node and edge anomaly detection architectures and was tested on multiple datasets. Experiments were conducted to validate the effectiveness of the proposed method as a graph neural network encoder. The findings demonstrated the robustness of the proposed method in detecting anomalies. The proposed method outperforms other existing methods in node anomaly detection tasks by 5.63% and edge anomaly detection tasks by 7.87%.
Accuracy of the Sentence-BERT Semantic Search System for a Japanese Database of Closed Medical Malpractice Claims

In this study, we developed a similar text retrieval system using Sentence-BERT (SBERT) for our database of closed medical malpractice claims and investigated its retrieval accuracy. We assigned each case in the database a short Japanese summary of the accident as well as two labels: the category was classified as a hospital department mainly, and the process indicated a failed medical procedure. We evaluated the accuracies of a similar text retrieval system with the two labels using three different multilabel evaluation metrics. For the encoders of SBERT, we employed two pretrained BERT models, UTH-BERT and NICT-BERT, that were trained on huge Japanese corpora, and we performed iterative optimization to train the SBERTs. The accuracies of the similar text retrieval systems using the trained SBERTs were more than 15 points higher than those of the Okapi BM25 system and the pretrained SBERT system.
Multi-Conflict-Based Optimal Algorithm for Multi-UAV Cooperative Path Planning

Multi-UAV cooperative path planning can improve the efficiency of task completion. To deal with the space and time conflicts of multi-UAVs in complex environments, a multi-collision-based multi-UAV cooperative path planning algorithm, multi-conflict-based search (MCBS), is proposed. First, the flight and cooperative constraints of UAV are analyzed, and a three-dimensional environment model is established that incorporates geographical information. Then, hierarchical optimization is used to design collaborative algorithms. In the low-level path design, UAV flight constraints are combined with a sparse A* algorithm, and by improving the cost function, the search space is reduced, and the search time is shortened. In high-level cooperation, the priorities of different conflicts are set, heuristic information is introduced to guide the constraint tree to grow in the direction of satisfying the constraints, and the optimal path set is searched by the best priority search algorithm to reduce the convergence time. Finally, the planning results of the proposed algorithm, the traditional CBS algorithm, and the sparse A* algorithm for different UAV tasks are compared, and the influence of the optimization parameters on the calculation results is discussed. The simulation results show that the proposed algorithm can solve cooperative conflict between UAVs, improve the efficiency of path searches, and quickly find the optimal safe cooperative path that satisfies flight and cooperative constraints.