Reviewerly/exHarmony huggingface dataset:
Data Augmentation Approach for Language Identification in Imbalanced Bilingual Code-Mixed Social Media Datasets

Addressing the problem of language identification in code-mixed datasets poses notable challenges due to data scarcity and high confusability in bilingual contexts. These challenges are further amplified by the associated imbalance and noise characteristic of social media data, complicating efforts to optimize performance. This paper introduces an augmentation approach designed to enhance language identification in bilingual code-mixed social media data. By incorporating reverse translation, semantic similarity, and sampling techniques alongside customized reprocessing strategies, our approach offers a comprehensive solution to these complex issues. To evaluate the effectiveness of the proposed approach, experiments were conducted on language identification at both the sentence and word levels. The results demonstrated the potential of the approach in optimizing language identification performance, offering a compelling combination of generation techniques for addressing the challenges of language identification in code-mixed data.
Spatial Feature Based Violence Detection Using Convolutional Neural Network

In the past decade, surveillance cameras have become a necessary integration for security measures in all types of localities. The omnipresence of these devices has substantially aided in tackling violent criminal activities. However, human error and biased judgment often result in delayed response and erroneous detection. In larger systems, continuous manual monitoring has become a cumbersome task. Therefore, automated recognition of aggressive activities in surveillance systems can enhance the remote monitoring experience and increase the preciseness of response. Previous experiments on various deep-learning techniques and Convolutional Neural Networks (CNN) tackled the challenge by identifying potential violent activities in real-time. The aim of this research is to benefit from reduced computational cost while maintaining optimality for practical implementation in real life. In this study, a lightweight yet highly effective CNN model has been proposed that can classify violent and non-violent behavior in surveillance footage solely based on spatial features. The model has undergone robust tuning and training and is capable of accurately extracting frame-level features. It was then evaluated conclusively on a combination of multiple benchmark datasets to see how well each of them performed. In conclusion, the proposed model has achieved an outstanding test accuracy of 99.6% and outperforms other popular CNN architectures by great margins.
Autonomous Path Optimization in Unfamiliar Map Through Deep Reinforcement Learning

The application of mobile robot's path planning and navigation in known map scenarios has been well-established. However, with the absence of a known map, the challenge lies in how a robot can effectively navigate through an unfamiliar environment and generate a sensible path towards its destination. This paper delves into the path planning quandary that mobile robot encountered in unknown environments. It employs a deep reinforcement learning (DRL) algorithm within a simulated environment, where the robot explores its objective. Additionally, an enhanced reward selection strategy is introduced through a performance-based comparison. This strategy tackles issues such as robot freezing and high collision rates during the exploration of uncharted territory. The outcome of this study is an enhancement in search performance and learning efficiency within path planning. The integration of this algorithm into the Robot Operating System (ROS) robot simulation platform, coupled with testing within the Gazebo simulation environment, demonstrates that the proposed strategy effectively diminishes obstacle collisions and boosts the success rate of reaching the intended destination.
Knowledge Base Processing Method Based on Text Classification Algorithm

The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. In practical application, the knowledge base processing method has been prove to have a good performance in text classification tasks. The utilization of knowledge base processing method in text classification has led to an average accuracy improvement of more than 17%. Furthermore, this method significantly reduces labeling costs by approximately 70% compared to traditional approaches. Recently, knowledge base processing methods have been widely used in supporting business applications, social media analysis and other fields. This paper proposes a knowledge base method to establish a feature model related to domain speciality and combine it with traditional text classification algorithm, so as to optimize the training and reasoning process of the classification model and improve the accuracy of classification effect. Lastly, we suggested strategies to overcome the shortcoming of the knowledge base method in improving the construction and training of the classification model.
Improved Q-Learning Algorithm for Path Planning of an Automated Guided Vehicle (AGV)

With the development of modern industry 4.0, intelligent path planning is an essential research direction of schedule systems for an automated guided vehicle (AGV), which has been widely used in logistics distribution centers of enterprises. This work implements the improved Q-learning algorithm to solve the typical obstacle avoidance problems in path planning. Specifically, the conventional Q-learning algorithm has shortcomings including low operational efficiency and slow learning speed. The improved Q-learning algorithm is successively proposed by adding a learning process based on the original Q-learning algorithm, which enables AGV to find obstacles and target locations within the shortest time, thus improving the efficiency of path planning. Finally, the simulation experiments are carried out in the grid environment with MATLAB. In comparison to the conventional Q-learning algorithm, the improved Q-learning algorithm has faster convergence and higher learning efficiency, improved by 20%.
Classification of graph topologies by machine learning methods

Many objects of the real world, both living and man-made, may be described in the terms of graph theory and represented as graph data. One of the tasks of graph data analysis is to classify graph topologies. This work explored the possibilities of machine learning methods, and in particular graph neural networks (GNNs), to classify graphs with different topologies. The aim was to study the robustness of the classifiers based on graph convolutional networks depending on the samples they were trained on. The authors also compared solutions based on GNNs and non-graph classifiers, which were fed several graph characteristics. The non-graph methods, which included support vector machine, decision trees and linear discriminant analysis, demonstrated results comparable to GNNs with a single convolutional layer on the test sample, but overfitting was clearly visible on the validation sample. GNNs were less sensitive to the changes in the number of vertices. However, GNNs showed a large number of false alarms when classifying regular graphs.
The Development of Simultaneous Congkak Game with Intelligent Agents

Congkak is a two-player game of perfect information with two phases, a simultaneous phase and a sequential phase. In this work, several intelligent agents for each phase were implemented to determine that minimax is the best algorithm for the sequential phase of the game. From the agents implemented, the payoff matrix of the game can be determined. It is shown that there is no pure strategy Nash Equilibrium but there exists a mixed strategy Nash Equilibrium of (0, 0, 0.111, 0.111, 0.333, 0.111, 0.333). An agent using this mixed strategy was proven to be more effective than the simultaneous agents trained.
Leveraging Knowledge Graphs for Orphan Entity Allocation in Resume Processing

Significant challenges are posed in talent acquisition and recruitment by processing and analyzing unstructured data, particularly resumes. This research presents a novel approach for orphan entity allocation in resume processing using knowledge graphs. Techniques of association mining, concept extraction, external knowledge linking, named entity recognition, and knowl-edge graph construction are integrated into our pipeline. By leveraging these techniques, the aim is to automate and enhance the efficiency of the job screening process by successfully bucketing orphan entities within resumes. This allows for more effective matching between candidates and job positions, streamlining the resume screening process, and enhancing the accuracy of candidate-job matching. The approach's exceptional effectiveness and resilience are highlighted through extensive experimentation and evaluation, ensuring that alternative measures can be relied upon for seamless processing and orphan entity allocation in case of any component failure. The capabilities of knowledge graphs in generating valuable insights through intelligent information extraction and representation, specifically in the domain of categorizing orphan entities, are highlighted by the results of our research.
Unified information and educational environment as a basis for the formation of professional competencies

Introduction . The relevance of this work is related to the attention to the topic of digitalization of the educational and professional space. Digital tools have firmly entered the educational practice, the number of electronic resources filling the educational and professional space is filling. Nevertheless, there is a problem of misalignment between the educational and professional community on the content of training, the relevance of the skills being formed that do not have implementation in further work. The authors believe that an increase in the share of digital tools and digitalized ways of learning both in the space of an educational institution and in the professional development of specialists in the workplace should contribute to the creation of a unified information environment between an educational institution and graduates. Purpose setting . The purpose of the article is to present a structural and meaningful model of a unified information and educational environment on the example of achieving the goals of environmental training of military specialists (UIEE ET). Methodology and methods of the study . The methodological basis was scientific statements that the primary task of digital transformation of education lies in the plane of selection and organization of educational content, and not in the technical solution of the issue; the information and educational environment is a means of forming an individualized educational space for the implementation of semantic content not available before. In preparing the article, methods of generalization (pedagogical experience and experience of military specialists), modeling in the course of determining educational strategies were used, and a pedagogical experiment was also conducted, including checking the formation of cognitive, affective and behavioral components of the culture of rational use of resources. Results . The authors propose a model of a unified information and educational environment for environmental training of military personnel. The conducted empirical research included questioning and solving situational problems by students, teachers, and military personnel. The analysis of the results confirmed the opinion of the authors of the article about the need to create an UIEE ET, which is based on the principles of ecologization of the information and educational environment and integration within the educational and professional space. Conclusion . As a result of the subject's mastering the totality of environmental conditions, the environmental competence of a specialist is formed, which should be regarded as a corporate educational result. On the basis of theoretical analysis and experimental data, recommendations for the creation of UIEE are made, promising areas of research in this area are identified.
Social drug dealing: how peer-to-peer fintech platforms have transformed illicit drug markets

Abstract Digital platforms have revolutionized the way illegal drug trafficking is taking place. Modern drug dealers use social network platforms, such as Instagram and TikTok, as direct-to-consumer marketing tools. But apart from the marketing side, drug dealers also use fintech payment apps to engage in financial transactions with their clients. In this work, we leverage a large dataset from Venmo to investigate the digital money trail of drug dealers and the social networks they create. Using text and social network analytics, we identify two types of illicit users: mixed-activity participants and heavy drug traffickers and build a random forest classifier that accurately predicts both types of illicit nodes. We then investigate the social network structure of drug dealers on Venmo and find that heavy drug traffickers share similar network characteristics with previous literature findings on drug trafficking networks. However, mixed-activity participants exhibit different patterns of network structure characteristics, including a higher clustering coefficient, suggesting that they may be accessing multiple networks and bridging those networks through their illicit activities. Our findings highlight the importance of distinguishing between these two types of illicit users and provide law enforcement agencies with valuable insights that can aid in combating illegal drug transactions in digital payment apps.
CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention

The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network-long short-term memory (CNN-LSTM) residual blocks attention (PCLRA) anomaly detection model with engine sensor data. To our knowledge, this is the first time that parallel CNN-LSTM-based networks have been used in the field of CHP engine anomaly detection. In PCLRA, spatiotemporal features are extracted via CNN-LSTM in parallel and the information loss is compensated using the residual blocks and attention mechanism. The performance of PCLRA is compared with various hybrid models for 15 cases. First, the performances of serial and parallel models are compared. In addition, we evaluated the contributions of the residual blocks and attention mechanism to the performance of the CNN-LSTM hybrid model. The results indicate that PCLRA achieves the best performance, with a macro f1 score (mean ± standard deviation) of 0.951 ± 0.033, an anomaly f1 score of 0.903 ± 0.064, and an accuracy of 0.999 ± 0.002. We expect that the energy efficiency and safety of CHP engines can be improved by applying the PCLRA anomaly detection model.
Research on public opinion analysis of COVID-19 based on machine learning

Social media is a platform for the participation of the whole people, and the "COVID-19" is a topic closely related to the whole people. Based on this, this paper conducts sentiment analysis on the hot topic of social media network public opinion "COVID-19", which can well present the public's emotional evolution process and results of this public opinion event, so as to provide certain guidance for relevant national departments to guide and control public opinion. This article chooses Twitter as the data source platform, collects tweets related to the "COVID-19" in January this year, and builds a simple public opinion analysis system using natural language processing models and different machine learning methods. The research results show that for the topic of "COVID-19", which is a long-lasting topic that is closely related to everyone, the proportion of positive emotions is relatively large, which also protects the security of the country from the level of public opinion. In other words, for a hot topic, when negative emotions prevail, relevant departments need to intervene in time to supervise, guide the direction of public opinion, and maintain social stability.
Correction: Hybrid predictive coding: Inferring, fast and slow

[This corrects the article DOI: 10.1371/journal.pcbi.1011280.].
SARW: Similarity-Aware Random Walk for GCN

Graph Convolutional Network (GCN) is an important method for learning graph representations of nodes. For large-scale graphs, the GCN could meet with the neighborhood expansion phenomenon, which makes the model complexity high and the training time long. An efficient solution is to adopt graph sampling techniques, such as node sampling and random walk sampling. However, the existing sampling methods still suffer from aggregating too many neighbor nodes and ignoring node feature information. Therefore, in this paper, we propose a new subgraph sampling method, namely, Similarity-Aware Random Walk (SARW), for GCN with large-scale graphs. A novel similarity index between two adjacent nodes is proposed, describing the relationship of nodes with their neighbors. Then, we design a sampling probability expression between adjacent nodes using node feature information, degree information, neighbor set information, etc. Moreover, we prove the unbiasedness of the SARW-based GCN model for node representations. The simplified version of SARW (SSARW) has a much smaller variance, which indicates the effectiveness of our subgraph sampling method in large-scale graphs for GCN learning. Experiments on six datasets show our method achieves superior performance over the state-of-the-art graph sampling approaches for the large-scale graph node classification task.
Heterogeneous information fusion based graph collaborative filtering recommendation

Nowadays, with the application of 5G, graph-based recommendation algorithms have become a research hotspot. Graph neural networks encode the graph structure information in the node representation through an iterative neighbor aggregation method, which can effectively alleviate the problem of data sparsity. In addition, more and more information graph can be used in collaborative filtering recommendation, such as user social information graph, user or item attributed information graph, etc. In this paper, we propose a novel heterogeneous information fusion based graph collaborative filtering method, which models graph data from different heterogeneous graph, and combines them together to enhance presentation learning. Through information propagation and aggregation, our model can learn the latent embeddings effectively and enhance the performance of recommendation. Experimental results on different datasets validate the outperformance of the proposed framework.
Comparison and realization of digital circuit and PLC designed four-way responder

According to the requirement of four-way responder, two different methods of digital circuit and PLC are designed and compared in this paper. The first method is to use the integrated chip "4511" and "555" and the corresponding peripheral circuit to achieve, according to the four-way scrambler design block diagram, successfully designed the schematic diagram and the corresponding PCB diagram, and actually made the finished product; The other method is to use programmable logic controller PLC to realize, designed the ladder diagram and the corresponding statement table of the four-way responder PLC control, and actually made the finished product. Both of these methods have been successfully implemented, have good application value in practice, and can be based on this idea, expand into more and better multi-route responder.
Evaluation and decision of task aircraft usage quality based on wavelet neural network

The quality status of aircraft use involves multiple indicators and has nonlinear correlations. The commonly used traditional evaluation methods often rely heavily on expert knowledge and cannot depict the nonlinear relationship of indicators, making it difficult to accurately and effectively support mission aircraft usage decisions. This article constructs one mission aircraft usage quality status indicator system that includes annual usage plan execution rate, matching degree of aircraft lifespan usage, variability of the remaining lifespan echelon difference, rate of satisfaction with aviation materials supply, and aircraft failure free rate. Wavelet Neural Network and BP Neural Network models are respectively used to predict and evaluate the quality status. Case validation shows that Wavelet Neural Network have faster data learning speed and higher prediction accuracy compared to BP Neural Network, which are basically consistent with actual results and can provide more accurate quantitative basis for mission aircraft usage decision-making.
Comparative Analysis of Security Features in Banknotes Across Various Global Economies using VSC®-8000/HS

Background: Banknote counterfeiting is a serious issue. Every government adds various security elements to its banknotes to avoid forgery. The security measures inducted in the banknotes reduce the likelihood of counterfeiting. These are the unique elements that are incorporated during the production process. Paper quality, intaglio printing, watermarks, security threads, latent images, etc. are some of these unique properties. Every nation has a unique set of security characteristics. In the year 2016 India demonetized and recalled all of its highest denomination bank notes (Rs. 1000 and Rs. 500) in order to cease counterfeiting. Thereafter, there has been incorporation of various security features in the current Mahatma Gandhi series to make the currency standout with other international currencies, in order to reduce the chances of forgery as per the sections 489A-489E of Indian Penal Code, 1872. Methods: Banknote samples were analyzed using the non-destructive Video Spectral Comparator 8000 (VSC®-8000/HS) by foster+freeman®. Each sample was placed in the live-camera unit with high- resolution 12-megapixel camera forming the part of Super Resolution Imaging (SRI) system. Front and back pictures of the banknotes were taken in white light. The samples were then examined under various illumination settings (Spot Light, Oblique Light, Transmitted Light, UV-light, and Infrared light) for both sides. Security features like Microprinting, Micro texting, Guilloche Pattern, OVI, Watermarks, etc., were studied. Results: In the current research paper, the security characteristics of the banknotes of ten different countries are compared utilizing VSC®-8000/HS. Analysis reveals Thai Baht with the most security features, followed by Vietnamese Dong, Czech Koruna, Indian Rupees, and others. Notably, Indian banknotes stand out due to UV features, security thread, latent image, and strategic micro text placement. Conclusions: This research paper recognizes the importance of security features in various banknotes including the Indian banknotes and also provides some of the recommendations to enhance the quality and security features in the Indian banknotes.