Reviewerly/exHarmony huggingface dataset:
Improving table detection for document images using boundary

Abstract Locating tables in document images is the first step to extracting table information, and high location precision is required. The dominant approach of table detection is based on an object detection algorithm, and the detector defines the prediction task as a regression problem, which inevitably leads to positioning errors. To address this issue, this paper presents an approach called Border Line Correction (BLC) to refine the rough prediction results of the original detector through the table boundary lines extracted from the document image. Our approach transforms the regression task into a classification problem, thus avoiding the inherent regression error of the object detection algorithm. Traditional annotation methods are inadequate for table detection tasks as they fail to capture the completeness and purity of the detection results. Therefore, this study treats the correct position of a table as a tolerance region. Additionally, to overcome the limitations of existing datasets in the materials domain, we collected 1183 samples from scientific literature in the materials field and created the MatTab dataset, annotating the tables with tolerance regions. This paper use Cascade RCNN with Swin Transformer as baseline models, and BLC is utilized to optimize the detection results. Experimental results demonstrate significant improvements with BLC at an IOU of 0.95 on the MatTab, ICDAR2019, and ICDAR2017 datasets. In MatTab, the percentage of correctly detected complete and pure tables increased from 72.3% to 82.1%.
Performance comparison of Flutter platform GUI in web and native environments

This article compares the performance of the Flutter framework's graphical user interface (GUI) in a Windows platform native environment and in a web environment on the same device. The purpose of the article is to make a comparative analysis of screen building and rebuilding times in both environments. For the purpose of the paper, a test application has been created in which screens of different types of complexity are included. The stated theses: "The Flutter platform is less efficient in the web environment compared to the native environment in terms of view loading times." and "The Flutter platform is less efficient in a web environment compared to a native environment in terms of view rebuilding time" have been proven.
Measuring and classifying students' cognitive load in pen‐based mobile learning using handwriting, touch gestural and eye‐tracking data

Abstract Although the utilization of mobile technologies has recently emerged in various educational settings, limited research has focused on cognitive load detection in the pen‐based learning process. This research conducted two experimental studies to investigate what and how multimodal data can be used to measure and classify learners' real‐time cognitive load. The results found that it was a promising method to predict learners' cognitive load by analysing their handwriting, touch gestural and eye‐tracking data individually and conjunctively. The machine learning approach used in this research achieved a prediction accuracy of 0.86 area under the receiver operating characteristic curve (AUC) and 0.85/0.86 sensitivity/specificity by only using handwriting data, 0.93 AUC and 0.93/0.94 sensitivity/specificity by only using touch gestural data, and 0.94 AUC and 0.94/0.95 sensitivity/specificity by using both the touch gestural and eye‐tracking data. The results can contribute to the optimization of cognitive load and the development of adaptive learning systems for pen‐based mobile learning. Practitioner notes What is already known about this topic Pen‐based mobile learning systems allow natural ways of handwriting and gestural touching, which can facilitate learners' cognitive processes in mobile learning. Behavioural and physiological multimodal data are helpful in detecting learners' real‐time cognitive load in mobile learning. The effectiveness of behavioural and physiological multimodal data for measuring cognitive load in pen‐based mobile learning is limited investigated. What this paper adds This paper confirms the effectiveness of handwriting and touch gestural multimodal data for measuring pen‐based learning cognitive load, in terms of their stroke‐, path‐ and time‐based features. This paper explores the potential of eye‐tracking data in measuring pen‐based learning cognitive load. A combination of behavioural and physiological multimodal data is reported to increase the prediction accuracy for cognitive load measurement. Implications for practice and/or policy Practitioners are suggested to use behavioural and physiological multimodal data individually or conjunctively for measuring cognitive load in pen‐based learning. The results provide guides for developing adaptive pen‐based learning systems by optimizing the real‐time cognitive load.
Analysis of how universal design principles impact on the perception of virtual museum interfaces

The research presented in this paper aims to analysis of the impact of using universal design in implementation of virtual museum interfaces to user perception. Experiment uses collaboration with Museum of the History of the City of Lublin to create the web application which presents virtual exhibits. The application was based on the React JavaScript framework, which enabled the creation of a basic web interface and the React 360 to generate the three-dimensional view. During research, implemented application was compared with the muzeumpuck.wkraj.pl website, which does not conform the universal design principles. The main research method consisted of Eye-tracking technology and LUT survey. The analysis of the results shows that interface which follow principles of universal design was easier to navigate. The time of searching specifical elements on that interface was shorter than in other application, as well.
Postdigital Neoliberalism

This entry begins with a definition of neoliberalism and follows with an abbreviated history that produced its economic and political rationalities. This history is then directed toward the postdigital condition, to describe how our everyday lives are governed through engagement with digital technologies. Postdigital neoliberalism continues to economize our everyday lives, now abetted and intensified through digitality. Yet liberal democratic traditions can be relearned, with the aid of digital technologies rather than at their expense. The entry discusses how postdigital neoliberalism is also a site for re-politicizing technology and neoliberalism. In particular, postdigital neoliberalism provides a critical reproach of weaponized forms of technology and neoliberalism, and the deleterious relationships between them. As such, the contemporary postdigital moment rests upon unlearning neoliberal practices that have been at work since the middle of the twentieth century. Today, neoliberal procedures appear normal and commonsensical, and many scholars argue that it is time to re-learn liberal and democratic practices, and with the assistance of digital technologies and not at their expense.
Analyze the effectiveness of ETL processes implemented using SQL and Apache HiveQL languages

In the era of digitization, where data is collected in ever-increasing quantities, efficient processing is required. The article analyzes the performance of SQL and HiveQL, for scenarios of varying complexity, focusing on the execution time of individual queries. The tools used in the study are also discussed. The results of the study for each language are summarized and compared, highlighting their strengths and weaknesses, as well as identifying their possible areas of application.
Comparative analysis of selected databases on the example of a proprietary web application

Database performance is one of the most important factors affecting the usability of the system. Therefore, the authors&nbsp;of the article decided to examine 3 popular database systems: MySQL, MS SQL and PostgreSQL, analyzing their&nbsp;performance. For this purpose, a test application was prepared and Docker software was used to simulate different&nbsp;hardware parameters. Depending on the selected settings and the number of records, different results were obtained. For&nbsp;small data sets, the differences were almost imperceptible. They have drastically increased for large data sets. In this&nbsp;case, MySQL fared poorly, and MS SQL was the best. This means that the choice of the database is very important, and&nbsp;it is worth considering the available hardware, the amount of data and the queries performed.
Negative Stances Detection from Multilingual Data Streams in Low-resource Languages on Social Media using BERT and CNN based Transfer Learning Model

Online social media allows users to connect with a large number of people across the globe and facilitate the exchange of information efficiently. These platforms cater to many of our day-to-day needs. However, at the same time, social media have been increasingly used to transmit negative stances like derogatory language, hate speech, and cyberbullying. The task of identifying the negative stances from social media posts or comments, or tweets is termed negative stance detection. One of the major challenges associated with negative stance detection is that most of the content published on social media is often in a multi-lingual format. This work aims to identify negative stances from multilingual data streams in low-resource languages on social media using a hybrid transfer learning and deep convolutional neural network approach. The proposed work strats by preprocessing the multi-lingual datasets by removing irrelevant information like special characters, hyperlinks, etc. The processed dataset is then passed through a pre-trained BERT (bidirectional encoder representations from transformers) model to generate embeddings by fine-tuning the model as per the dataset under consideration. The generated word embeddings are then passed to a deep convolutional neural network for extracting the latent features from the texts and removing the unessential information. This helps our model to achieve robustness and effectiveness for efficient learning on the given dataset and make appropriate predictions on zero-shot data. The paper utilizes several optimization strategies for examining the impact of fine-tuning different BERT layers on the model’s performance. ntensive experiments on a variety of languages, namely, English, French, Italian, Danish, Arabic, Spanish, Indonesian, German, and Portuguese are performed. The experimental results demonstrate the effectiveness and efficiency of the proposed framework.
PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST

Cluster analysis is a technique for grouping objects in a database based on their similar characteristics. The grouping results are said to be good if each cluster is homogeneous, and can be validated using the silhouette coefficient test. However, the presence of outliers in the data can affect the grouping results, so methods that are robust to outliers are used, such as K-Medoids and Density-Based Spatial Clustering of Applications with Noise. The purpose of this study is to compare the results and performance of the two methods using the silhouette coefficient test on data on human development indicators in South Sulawesi Province in 2021. The results of the analysis show that K-Medoids produced 2 groups, namely the districts/cities group which has indicators of human development that consist of 21 districts/cities, and the high group, which consists of 3 districts/cities, while Density-Based Spatial Clustering of Application with Noise produces 1 group that has the same characteristics, which consists of 19 districts/cities, and the remaining 5 districts/cities are identified as noise. Based on the silhouette coefficient test, K-Medoids have a greater value than Density-Based Spatial Clustering of Application with Noise, namely 0,635 and 0,544, respectively, so that K-Medoids have better performance.
Obstacle Detection Method of Underground Electric Locomotive Rail Based on Instance Segmentation

Real-time and accurate obstacle detection is a vital technology for electric locomotives, especially as driverless vehicles are introduced. A method of obstacle detection for underground electric locomotive rail based on instance segmentation is developed to solve the problems of misdetection and missing detection, low detection accuracy, and slow detection speed of rail obstacles. The method of locating the track mask, demarcating the effective driving boundary, expanding the track mask, and forming the effective driving area is adopted to verify whether the target is an obstacle based on whether the target is located in the effective driving area, to avoid the problem of misdetection and missing detection of the target obstacle. The YOLACT++ (You Only Look At CoefficienTs) model is improved, and path augmentation and target classification loss function replacement strategies are adopted to enhance the model’s ability to detect target details and increase the accuracy of target segmentation. Compared with traditional image processing, this method can detect both straight rail and turnout. The mean average precision of boundary box mAP 0.5 (box) and mask mAP 0.5 (mask) of the improved YOLACT++ model reaches 98.52% and 98.55%, which is higher than that of the YOLACT++ model, and the detection frame rate reaches 21.9 frames per second.
SDbQfSum: Query‐focused summarization framework based on diversity and text semantic analysis

Abstract Query‐focused multi‐document summarization (Qf‐MDS) is a sub‐task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. Unlike other summarization tasks, Qf‐MDS has specific research challenges including the differences and similarities across related document sets, the high degree of redundancy inherent in the summaries created from multiple related sources, relevance to the given query, topic diversity in the produced summary and the small source‐to‐summary compression ratio. In this work, we propose a semantic diversity feature based query‐focused extractive summarizer (SDbQfSum) built on powerful text semantic representation techniques underpinned with Wikipedia commonsense knowledge in order to address the query‐relevance, centrality, redundancy and diversity challenges. Specifically, a semantically parsed document text is combined with knowledge‐based vectorial representation to extract effective sentence importance and query‐relevance features. The proposed monolingual summarizer is evaluated on a standard English dataset for automatic query‐focused summarization tasks, that is, the DUC2006 dataset. The obtained results show that our summarizer outperforms most state‐of‐the‐art related approaches on one or more ROUGE measures achieving 0.418, 0.092 and 0.152 in ROUGE‐1, ROUGE‐2, and ROUGE‐SU4 respectively. It also attains competitive performance with the slightly outperforming system(s), for example, the difference between our system's result and best system in ROUGE‐1 is just 0.006. We also found through the conducted experiments that our proposed custom cluster merging algorithm significantly reduces information redundancy while maintaining topic diversity across documents.
Diffusion Convolution Neural Network-Based Multiview Gesture Recognition for Athletes in Dynamic Scenes

This paper focuses on deep vision sensing-assisted gesture recognition for athletes in dynamic scenes. Although many research attention had been devoted to this field in recent years, most of existing works failed to fully take characteristics of dynamic scenes into consideration. To deal with this challenge, this paper proposes a diffusion convolution neural network-based multiview gesture recognition approach in dynamic scenes. For one thing, the dynamic spatiotemporal slice position selection based on the body mask heatmap is adopted to calculate positions of horizontal and vertical slices. Thus, the dynamic selection of slice positions in two directions can be realized, and then the extraction of bi-directional spatiotemporal slice images can be completed. For another, action sequences through the 3D residual neural network are learned, and the spatiotemporal information among frames are mined through recurrent networks. Through their combination, a multi-view gesture recognition approach for athletes is constructed. In the experiments, two standard datasets UCF101 and HMDB51 are utilized to establish simulation environment. The proposed method can reach the accuracy beyond 95% on the two datasets. Compared with several typical recognition methods, the proposed method shows higher accuracy.
COMPARISON OF WEIGHTED MARKOV CHAIN AND FUZZY TIME SERIES-MARKOV CHAIN METHODS IN AIR TEMPERATURE PREDICTION IN BANDA ACEH CITY

Air temperature prediction is needed for various needs such as helping plan daily activities, agricultural planning, and disaster prevention. In this research, Weighted Markov Chain (WMC) method and Fuzzy Time Series-Markov Chain (FTS-MC) method are applied to predict the weekly air temperature in Banda Aceh city. The purpose of this study is to find out how the results of the application and comparison of the accuracy of the WMC method and the FTS-MC method on weekly air temperature prediction in Banda Aceh City. The prediction result of air temperature in Banda Aceh city using the WMC method for the next three weeks obtained an air temperature of 26,5℃. The prediction results of air temperature in Banda Aceh city using the FTS-MC method for the next three weeks obtained predicted values of 26,66℃ for the 105th week, 26,79℃ for the 106th week, and 26,83℃ for the 107th week. The MAPE accuracy level of the WMC method is 1,5% and the FTS-MC method is 1,7%. This shows that the MAPE of the WMC method is smaller than the FTS-MC method so it can be concluded that air temperature prediction using the WMC method is better than the FTS-MC method.
Bibliometric Analysis of the Influence of Academic Research on HRM Strategy, Employee Performance, and Organizational Success Globally

This bibliometric analysis explores the profound influence of academic research on Human Resource Management (HRM) strategy, employee performance, and global organizational success. Leveraging a comprehensive dataset from reputable academic databases, the study employs bibliometric techniques to analyze patterns of publication, citation, collaboration, and keyword trends within the HRM research domain. The findings reveal a multifaceted landscape of HRM scholarship. Co-authorship network analysis identifies prolific authors and research clusters, highlighting the collaborative nature of HRM research. Citation analysis uncovers highly influential publications, showcasing the impact of seminal works on HRM theory and practice. Keyword analysis unveils emerging research themes, reflecting the field's responsiveness to evolving challenges and trends. Notably, the analysis underscores the significance of evidence-based HRM practices and the global context in which HRM operates. It recognizes the role of academic research in shaping HRM strategies that enhance employee performance and contribute to global organizational success. This study provides valuable insights for HRM practitioners, researchers, and organizational leaders by offering a comprehensive overview of the evolving HRM landscape. It emphasizes the importance of collaboration, the influence of seminal works, and the dynamic nature of HRM research. Understanding these dynamics is pivotal for advancing HRM scholarship and driving organizational excellence in a globalized world.
On Semi-Reconstruction of Graphs of Connectivity 2

The deck of a graph G is the collection of subgraphs G − υ for all vertices υ of G. It is proved that at most two graphs of connectivity 2 and minimal degree at least 3 can have the same deck. Denote by $$\mathcal{D}\left(G\right)$$ the deck of a 2-connected graph G. An algorithm is described which given the deck $$\mathcal{D}\left(G\right)$$ of a graph G of connectivity 2 with minimal degree at least 3 constructs two graphs G1,G2 such that G ∈ {G1,G2}.
Modeling the dynamic safety management of buildings adjacent to karst shield construction: An improved cloud Bayesian network

Shield construction in karst tunnels inevitably causes settlement deformation and even damage to surrounding buildings. To accurately evaluate the security of neighboring buildings, a hybrid method combining an improved cloud model (CM) and a Bayesian network (BN) is proposed for dynamic security analysis and the evaluation of neighboring buildings. First, through engineering practice, simulation analysis and literature research, the safety risk factors and evaluation criteria for adjacent buildings influenced by shield construction in karst areas are determined, and a BN for safety evaluations of adjacent buildings is constructed. The improved CM is used to model the uncertainty of the risk state, realize the discretization of continuous data and obtain prior knowledge. By improving the cloud Bayesian network (CBN) to perform three kinds of comprehensive analyses, including forward prediction reasoning in advance, sensitivity analysis and reverse risk diagnosis for events, and appropriate risk management and control measures, the dynamic safety perception and control of buildings adjacent to karst shield construction is realized. The data for buildings under construction adjacent to Guiyang Rail Transit Line 3 are used to demonstrate the applicability and effectiveness of the proposed method. The contributions of the research results are as follows: (a) The key risk factors affecting the safety of buildings adjacent to shield construction areas in karst zones are extracted, and an evaluation index system and an evaluation standard are established; (b) An improved CM is used to describe the fuzziness and uncertainty in the process of node state discretization in a risk inference network, thus improving the prediction and diagnosis performance of BN security evaluation models; (c) The improved cloud Bayesian model is applied to construct a knowledge representation-based safety model of buildings adjacent to karst shield construction areas, and accurate perception and control diagnosis results are obtained for effective safety risk prediction and control, providing a reference for other similar projects.
Continuously non-malleable codes from block ciphers in split-state model

Abstract Non-malleable code is an encoding scheme that is useful in situations where traditional error correction or detection is impossible to achieve. It ensures with high probability that decoded message is either completely unrelated or the original one, when tampering has no effect. Usually, standard version of non-malleable codes provide security against one time tampering attack. Block ciphers are successfully employed in the construction of non-malleable codes. Such construction fails to provide security when an adversary tampers the codeword more than once. Continuously non-malleable codes further allow an attacker to tamper the message for polynomial number of times. In this work, we propose continuous version of non-malleable codes from block ciphers in split-state model. Our construction provides security against polynomial number of tampering attacks and it preserves non-malleability. When the tampering experiment triggers self-destruct, the security of continuously non-malleable code reduces to security of the underlying leakage resilient storage.