Reviewerly/exHarmony huggingface dataset:
Pipe Attitude Recognition and Control System Based on Machine Vision

This paper studies the device for pipe angle recognition and attitude control based on machine vision in pipe punching machines. The system uses the improved image matching algorithm to calculate the deviation angle of the pipe profile in the reference image and the test image, which are controlled by the upper computer logic control unit (PLC). The feeding device adjusts the attitude of the pipe according to the deviation angle, so that the pipe is in the correct attitude, and finally, the punching machine punches the pipe. The system reduces the production cost of the enterprise and improves the production efficiency, which has practical significance for the informatization and intelligent development of the manufacturing industry.
Region Guide Point for Depth Estimation

In this paper, aiming at the difficulty of depth estimation requiring the information of the whole image to be estimated, we propose a global information extraction method based on region features, namely the Global Multi-scale Feature Extraction Network. We convert the features of the image into a sequence of region features so that we can use a powerful but computationally intensive network to extract deeper features of each region. Specifically, we use the transformer encoder to aggregate global information and then use the Large Kernel Pyramid Convolution to extract multi-scale features to obtain enhanced region features. Finally, this feature is used as a priori information to guide the point feature, thereby improving the performance of depth estimation. We conducted extensive experiments on two of the most popular datasets for depth estimation, NYUD-V2 and KITTI, demonstrating the effectiveness of our method and having highly competitive performance on these two datasets.
A Novel Algorithm for Video Frame Prediction Based on Convolutional Neural Network

The fundamental support of the deep learning algorithm model comes from a large number of labeled data sets, but manually labeling a large number of data sets is Cumbersome, and it is easy to produce huge errors, which has also become a bottleneck in the development of computer vision. In this paper, video prediction has become a secret medicine to solve the bottleneck of the development of computer vision technology. The essence of video prediction is to predict and generate video frame by frame. The traditional LSTM algorithm has a large amount of calculation due to the internal full connection. ConvLSTM greatly improves the prediction performance. However, conventional ConvLSTM cannot guarantee the clarity of the predicted output image. This paper presents a novel ConvLSTM prediction model. Long short-term memory network is applied in the field of video prediction. ConvLSTM no longer simply extracts temporal features, but also captures spatiotemporal features. Compared with convolutional neural network, the feedforward neural network operations of ‘input-to-state’ and ‘state-to-state’ in traditional LSTM are replaced by convolution operations, and the row and column information of samples can be extracted through convolution structure. In addition, ConvLSTM transmits information from the previous layer of neurons to the three gates at the same time before the information is transmitted to the three gates, so that the neurons can capture the information retained for more time. The FitConvLSTM model proposed in this paper has a score of 95% on the SSIM evaluation function, and has achieved good results.
Short-Term Forecast Based on Generalized Maximum Correntropy Criterion and Kernel Extreme Learning Machine

How to realize the short-term forecast efficiently and accurately is of great significance to the stable economic operation of electric power system and the operation management of electric power company. Traditional short-term forecast method usually takes a long time to process data and is easy to occur errors. In this paper, a short-term forecast method based on generalized maximum correntropy criterion and kernel extreme learning machine is proposed. More precisely, multidimensional features representing the data characteristics are selected, date features and k-means clustering are carried out to obtain a power data cluster center. Then, generalized maximum correlation entropy is used to filtrate different users with similar power characteristics, and construct the advanced training data set. Finally, a novel kernel extreme learning machine model is used to simulate and output the short-term load prediction. Experimental results and comparison on real data show that our method can effectively improve the accuracy of prediction and has strong robustness.
Data Augmentation Based on Topic Relevance to Enhance Text Classification in Scarcity of Training Data

Acquiring data in some fields is formidable and faces challenges such as imbalanced datasets and data scarcity problems during data collection. Made text classification models become more prone to overfitting and bias toward a particular category. Thus, generating an extensive and effective dataset to improve the model performance becomes one of the important research topics. One of the fastest and most effective methods is data augmentation techniques. This study proposes a novel data augmentation method based on topic relevance for text classification. First, the BERT model is applied to generate its semantic vector of the text data, and text similarity analysis is performed in each category to determine the semantic similarity between text contents of the already limited and scarce datasets. Text data with a high correlation with other text data in the same category will then be extracted. This is because text data that are highly correlated with each other imply that the topic of these texts is most likely to be relevant. Thus, by performing keyword extraction on the most relevant text data to obtain the keywords from these highly correlated text data, these keywords are then shuffled and rejoined to generate massive and new high-quality augmented data. By calibrating the amount of newly generated augmented data according to the degree of balances in each category, the augmented text data may alter the category balance representation. From the experiment, the overall results indicate that with some computational effort, a significant increase in augmented data can not only alleviate the effect caused by imbalanced datasets but also increases the accuracy in text classification when data scarcity is considered.
A Performance Study of Applications of AI in Toxic Comments Classification

Social media websites and tweeting apps have seen a sharp rise in popularity in the recent years. One can express their opinions and sentiments about things, people, and events through these platforms. Arguments frequently start on social media platforms during discussions and debates and involve the usage of toxic comments, which are unpleasant, disrespectful, and hurtful statements. According to many, social networking sites must be able to identify these harmful comments. This research analyses several deep learning and machine learning methods like Convolutional Neural Network, Long Short -Term Memory, Support Vector Machine, Random Forest, and Naive Bayes for toxic comments classification along with the study that examines the effects of many word embedding methods including Word2Vector, Bag of Words, Global Vectors, Bidirectional Encoder Representations from Transformers, and Embeddings from Language Model on the classification of toxic comments and also the future scope of the research.
A Procrastinating Control-Flow Integrity Framework for Periodic Real-Time Systems

Connected embedded systems and cyber-physical systems exhibit larger attack surface than isolated ones. Control-flow integrity (CFI) is a set of techniques to prevent attackers from redirecting program control-flow and performing arbitrary computation, by detecting and checking control-flow transfers. Currently CFI for real-time systems either operate in-line with code execution, often depending on hardware mechanisms for improved performance and/or security guarantees, or focus solely on budget management when performing CFI out-of-order. In this work, we exploit the predictable release pattern of periodic real-time systems to create a novel CFI framework. This framework (1) consists of a novel real-time task model, which explicitly considers CFI related execution along with the regular portion of the tasks, and (2) presents a novel hardware-assisted trusted scheduler to enable a unique combination of out-of-order and in-line control flow enforcement on forward edge and backwards edge, respectively, to minimize performance overhead while ensuring real-time deadlines. Our framework provides the flexibility to model arbitrary forward-edge CFI as security tasks, so that we may strategically schedule them, and provide schedulability and correctness analysis to explicitly ensure that CFI verification is always performed on time without affecting the timeliness of the real-time tasks. Simulations show that our new task model outperforms existing work in terms of resource usage, thus allowing for more complex and sophisticated CFI to be implemented. We implement our approach on real hardware and microbenchmarks confirm that our approach has comparable in-line overhead as existing work.
Impact of IoT Applications Using Digital Marketing

Internet of things (IoT) is one of the new technologies that is expanding exponentially and is presenting new opportunities in a variety of industries, including business, academics, healthcare, transportation, industry, environment, and making our home and cities smart, among others. The IoT system's smart gadgets will be able to perceive, gather, and transport data via the internet without the need for human interaction. Large businesses strive to include technology into their marketing or digital marketing plan that reshapes consumers' expectations, attitudes, and requirements toward businesses in addition to communication style and product promotion. Due to the utilization of several smart devices, networks, and software that are weak and susceptible to hacking, it has a number of security vulnerabilities. The study focuses on the impression of IoT in digital marketing, IoT surface attack and security flaws, technological and security issues in digital marketing, and tries to present some preventive solutions for using IoT's in digital marketing.
ARGAEL: ARGument Annotation and Evaluation tooL

Argument mining aims to automatically extract structured argumentative information existing in natural language text, and it is commonly performed by machine and deep learning models that require accurately and meaningfully labeled corpora. In this paper, we present ARGAEL, an open-source desktop tool designed to provide flexibility, effectiveness and efficiency on the manual annotation of related arguments in text documents. ARGAEL supports the use of rich, configurable argument models, the labeling of argument components and relations, and the assessment of argument annotations from multiple people, being suitable for large-scale, collaborative argument annotation processes.
Applications of the Internet of Things and Machine Learning Using Python in Digital Marketing

The internet of things (IoT) is a network of physical objects or “things” embedded with electronics, software, sensors, and network connectivity, which enables them to collect and exchange data. These objects can include everyday devices such as smart thermostats, home security systems, wearable fitness trackers, and even vehicles. However, IoT also raises concerns about privacy, security, and reliability, as the increasing number of connected devices creates new opportunities for cyber attacks and data breaches. As such, the development and deployment of IoT technology must be done in a responsible and secure manner. Through the use of sensors, it is now possible to collect, transmit, and transform data into knowledge. The marketing and sales sectors are capable of using data gathered through IoT to interact with customers on an individual basis. Python is an object oriented, scripted and interpreted language for both learning and real-world programming. Work in this chapter first introduces Python as a language, and gives summary about data science, machine learning, and IOT.
Challenges in Prototyping a Cloud-Native Billing Application for 5G with Stream Processing

New enterprise systems must be able to keep pace with increasing data volumes through business innovations. To enable enterprise systems to handle current data volumes, they are typically distributed systems built to scale well and are operated in the cloud. Today, distributed online analytical processing systems (OLAP) are more appropriate to be based on stream processors such as Apache Flink than online transaction processing systems (OLTP). This application paper is a practical contribution to the research of using stream processors for cloud-operated general-purpose applications processing OLTP workloads. We present the challenges and lessons learned from prototyping a billing application for a 5G mobile network using Flink. To reveal these challenges, we describe the technical details of the prototype.
Causality extraction: A comprehensive survey and new perspective

Researchers in natural language processing are paying more attention to causality mining. Numerous applications of the growing need for efficient and accurate causality mining include question answering, future events predication, discourse comprehension, decision making, scenario generation, medical text mining, and textual entailment. Although causality has long been in the spotlight, but there are still issues that need to be addressed. This study provides a comprehensive review of casualty mining for various application domains available in the new-age literature from 1989 to 2022. We searched and rigorously examined numerous papers in the most reliable libraries for the review, and the terminologies that drive the context are described. Each paper underwent a thorough review process to extract the following meta-data: techniques, target domains, datasets, features, and limits of each approach. This meta-data will aid researchers in selecting the strategy that is most suited to their research needs. The literature is divided into three groups based on critical reviews including traditional, machine learning-based, and deep learning-based approaches. A concise taxonomy that can substantially help new scholars comprehend the field is developed. In order to make it simple for new researchers to start their research, various perspectives and suggestions are offered.
The virtual classroom in the ICT career within the Technological University of Xicotepec

The virtual classroom of the race has helped to facilitate the work of teachers in ICT Academy. Following on from its inception in the year 2007. It has been an evolution, although it is known that this development never ends, emerged as support but now is more than that, it's a tool where course content, tutorial information and advice are handled until stays, where distances are not a barrier for teachers and students, this allows a contact at all times, the virtual classroom has already served for generations to send reports, documentation, monitoring the content of the projects, in addition to not lose track with the students during the four months is the period of stay. Also great contubucion are the preparatory courses of the race from the previous year they have been in this virtual classroom.
A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition

The typical pretrained model’s feature extraction capabilities are insufficient for medical named entity identification, and it is challenging to express word polysemy, resulting in a low recognition accuracy for electronic medical records. In order to solve this problem, this paper proposes a new model that combines the BERT pretraining model and the BilSTM-CRF model. First, word embedding with semantic information is obtained by pretraining the corpus input to the BERT model. Then, the BiLSTM module is utilized to extract further features from the encoded outputs of BERT in order to account for context information and improve the accuracy of semantic coding. Then, CRF is used to modify the results of BiLSTM to screen out the annotation sequence with the largest score. Finally, extensive experimental results show that the performance of the proposed model is effectively improved compared with other models.
THE POLICY OF STUDENT PROFILING AND ITS IMPACT ON THE DEVELOPMENT OF THE SECONDARY VOCATIONAL EDUCATION SYSTEM

Aim. To evaluate the effectiveness of the student profiling policy in the context of its impact on the development of secondary vocational education and the entire system of reproduction of human capital assets in Russia. Methodology. Based on the data of empirical (both qualitative and quantitative) studies conducted both in Russia and in other countries, the analysis of the student profiling policy impact on such indicators as equal access to education and the popularity of secondary vocational education was carried out. Results. It is shown that the system of secondary vocational education, being one of the mechanisms and at the same time the resultant effects of the educational policy of student profiling, serves as a system that contributes to the encapsulation of a number of social problems, in particular, inequality of opportunities. It is demonstrated that the policy of student profiling leads to the separation of the secondary vocational education system, which is perceived by the public opinion of Russia as an unattractive educational trajectory. The conclusion is made about the need to revise the basic imperatives of the student profiling policy. Research implications. The assumption is substantiated that in order to correct the negative effects of student profiling, educational policy can be reoriented to revise its ideological and organizational foundations. From the authors' point of view, changing the basic imperatives of the student profiling policy while maintaining its general orientation will contribute to the realization of Russia's national interests in the field of education.
Multimodal Question Generation using Multimodal Adaptation Gate (MAG) and BERT-based Model

Question Generation (QG) is a task to generate questions based on an input context. Question Generation can be solved in several ways, ranging from conventional rule-based systems to recently emerging sequence-to-sequence approaches. The limitation of most QG systems is its limitation on input form, which is mainly only on text data. On the other hand, Multimodal QG covers several different inputs such as: text, image, table, video, or even acoustics. In this paper, we present our proposed method to handle the Multimodal Question Generation task using an attachment to a BERT-based model called Multimodal Adaptation Gate (MAG). The results show that using the proposed method, this development succeeds to do a Multimodal Question Generation task. The generated questions give 16.05 BLEU 4 and 28.27 ROUGE-L scores, accompanied by the human evaluation to judge the generated questions from the model, resulting in 55% fluency and 53% relevance.
Application of Multilevel Models in Classification and Regression Problems

There is a constant need to create methods for improving the quality indicators of information processing. In most practical cases, the ranges of target variables and predictors are formed under the influence of external and internal factors. Phenomena such as concept drift cause the model to lose its completeness and accuracy over time. The purpose of the work is to improve the processing data samples quality based on multi-level models for classification and regression problems. A two-level data processing architecture is proposed. At the lower level, the analysis of incoming information flows and sequences takes place, and the classification or regression tasks are solved. At the upper level, the samples are divided into segments, the current data properties in the subsamples are determined, and the most suitable lower-level models are assigned according to the achieved qualitative indicators. A formal description of the two-level architecture is given. In order to improve the quality indicators for classification and regression solving problems, a data sample preliminary processing is carried out, the model’s qualitative indicators are calculated, and classifiers with the best results are determined. The proposed solution makes it possible to implement constantly learning data processing systems. It is aimed at reducing the time spent on retraining models in case of data properties transformation. Experimental studies were carried out on several datasets. Numerical experiments have shown that the proposed solution makes it possible to improve the quality processing indicators. The model can be considered as an improvement of ensemble methods for processing information flows. Training a single classifier, rather than a group of complex classification models, makes it possible to reduce computational costs.
Improving LZW Compression of Unicode Arabic Text Using Multi-Level Encoding and a Variable-Length Phrase Code

This paper introduces a novel approach to enhance the efficiency of compressing Arabic Unicode text using the Lempel-Ziv-Welch (LZW) technique. This method includes two stages: transformation and compression. During the first phase, a multi-level scheme that works according to the level of words, syllables, and characters replaces multi-byte symbols with single-byte symbols, resulting in a binary output of 51%-75% smaller than the actual size and effective for compression. In the second phase: the outputs of the previous phase are received as inputs to the adaptive LZW technique, attached to a value representing the length of the initial phrase (minimum code length). This value is automatically determined according to the size of the data source to enhance the performance of LZW. The original data size is included in the compressed file to be used during the decompression process to detect the length of the initial phrase. The compression ratio achieved by the proposed method was compared to that of the traditional LZW technology that uses multi-byte encoded characters and a fixed initial length phrase, as well as two recent technologies, DEFLATE and Gzip. Experimental results indicate that our method achieves an average compression rate of about 71% and outperforms other methods for all forms of Arabic texts, with improved LZW able to compress an additional 7% or more or less of the files it compresses. Variable-length dictionary LZW continuously displays a significant difference in compression ratios for small files compared to modern methods, whether it uses a variable-width- encoding scheme only or with multi-level encoding as a precompression step. Multi-level schema can be used as a preprocess to other compression techniques, especially those that work efficiently with binary data. Also, the original data volume can be used as a private key within data security and encryption applications.