Reviewerly/exHarmony huggingface dataset:
FEATURES OF SELF-ORGANIZATION OF PEDAGOGICAL UNIVERSITY STUDENTS

When studying the development of self-organization of students — future teachers, its importance is high in the current conditions of an unstable world, both for the student and for the teacher. The transition of world development from a complex and ambiguous world (VUCA-world) to a nonlinear and incomprehensible world (BANI-world) requires a change in the person himself. Complex types of activities, including educational and pedagogical, do not leave their subject a choice to be organized or not. Self-organization of the individual acts as the ability to independently form internal ordered stable structures in response to the challenges of the new BANI world. The concept of self-organization does not have a single, accepted definition, and is considered at various levels, from different sides, by various scientific directions. From the standpoint of a technical approach (scientific organization of labor, time management), the results of a survey of students of a pedagogical university are presented. The factors that complicate the educational activities of students are identified; factors that hinder the development of self-organization competence, as well as factors that help reduce stress in the learning process and perform educational activities at the required level. It is shown that there are significant differences between full-time and part-time students in some personal factors that prevent students from being organized. The ways of further research of the problem field of self-organization development in the educational process of the university through the definition of methodology, including axiological grounds, are proposed.
A Machine Learning-Based Mobile Chatbot for Crop Farmers

Agriculture remains the basis of the country’s economy, providing the main source of livelihood for most citizenry such as food, employment, income and foreign exchange as well as raw materials for the manufacturing sectors. Despite the great need for economic advancement in crop farming, agriculture seems to be limited in some parts of the country as many people go about in search of white-collar jobs due to lack of adequate information and knowledge on the use of modern farming technologies. The inability of farmers in rural and sub-urban areas to access agricultural knowledge and real-time information on latest farming practices to enhance informed decision making related to soil properties, seeds, fertilizers, pests, modern agricultural tools, and agro-best practices leads to poor crop productivity by farmers. This work is aimed at providing a mobile chatbot for crop farmers in Uyo and its environs. The dataset used was obtained from Akwa Ibom State Ministry of Agriculture and farmers using a combination of two classic research methods; questionnaires and interviews. An ontology-based representation of the obtained dataset is used for training the chatbot using a hybridized machine approach that consists of word shuffling and Jacquard Similarity algorithm. The resulting chatbot is a knowledge base that will provide the means of obtaining useful answers to questions, advice and recommendations on specific farming concerns. The use of the chatbot will give government a platform to reach out to famers in the state and obtain feedback on governance through agricultural services.
Penerapan Metode Waterfall Pada Aplikasi Reservasi Lapangan Futsal Berbasis Web

This study aims to make it easier to provide scheduling information, the leasing process, and making financial reports. The benefits of this research can find out booking transactions, financial reports, scheduling information, and services at the Bandar Lampung Sport Center. For data collection methods using several methods such as observation, interviews, and literature studies. While the method for developing the system uses the waterfall method. The results of this study are in the form of a web-based application that can make it easier for customers and managers. The results of the tests carried out using the Black Box method with a valid category with a result of 100%, then this test can be said to be successful,, it is concluded that the applications that have been made can run well.
Metaverse Interoperability with Composability in Hyper-Connected and Hyper-Personalized Virtual Environments

A metaverse platform is a simulated environment in which human users can interact with computer-generated objects and other users. Each metaverse platform operates multiple virtual worlds according to the purpose of the service. In order to connect the virtual worlds of heterogeneous metaverse platforms and allow users to freely navigate the metaverse, it is necessary to consider interoperability.
Research on the use of drones for logistics distribution network terminal system based on computer 5G technology

With the advent of the 5G era, informatization has become the mainstream development process in today's world. In the process of rapid technological development, due to the rapid progress of 5G communication technology combined with the rapid development of drone technology, the logistics and distribution industry has become With the great historical progress, this paper introduces the performance of the high-quality development of the UAV logistics industry driven by computer 5G. This article discusses the characteristics of the computer 5G network and analyzes its improved application scenarios in the UAV logistics industry system., the study found that the computer 5G network not only makes the UAV intelligent facilities and equipment popularized and applied, but also makes the UAV logistics service more high-quality. The key technology of machine control is analyzed and researched.
Detection of multiple anomalous instances in video surveillance systems

Anomalous event recognition has a complicated definition in the complex background due to the sparse occurrence of anomalies. In this paper, we form a framework for classifying multiple anomalies present in video frames that happen in a context such as the sudden moment of people in various directions and anomalous vehicles in the pedestrian park. An attention U-net model on video frames is utilized to create a binary segmented anomalous image that classifies each anomalous object in the video. White pixels indicate the anomaly, and black pixels serve as the background image. For better segmentation, we have assigned a border to every anomalous object in a binary image. Further to distinguish each anomaly a watershed algorithm is utilized that develops multi-level gray image masks for every anomalous class. This forms a multi-class problem, where each anomalous instance is represented by a different gray color level. We use pixel values, Optical Intensity, entropy values, and Gaussian filter with sigma 5, and 7 to form a feature extraction module for training video images along with their multi-instance gray-level masks. Pixel-level localization and identification of unusual items are done using the feature vectors acquired from the feature extraction module and multi-class stack classifier model. The proposed methodology is evaluated on UCSD Ped1, Ped2 and UMN datasets that obtain pixel-level average accuracy results of 81.15%,87.26% and 82.67% respectively.
A General Recipe for Automated Machine Learning in Practice

Automated Machine Learning (AutoML) is an area of research that focuses on developing methods to generate machine learning models automatically. The idea of being able to build machine learning models with very little human intervention represents a great opportunity for the practice of applied machine learning. However, there is very little information on how to design an AutoML system in practice. Most of the research focuses on the problems facing optimization algorithms and leaves out the details of how that would be done in practice. In this paper, we propose a frame of reference for building general AutoML systems. Through a narrative review of the main approaches in the area, our main idea is to distill the fundamental concepts in order to support them in a single design. Finally, we discuss some open problems related to the application of AutoML for future research.
Phonetic Speech Segmentation of Audiobooks by Using Adapted LSTM-Based Acoustic Models

This paper describes experiments on phonetic speech segmentation of audiobooks by using LSTM neural networks. The segmentation procedure includes an iterative adaptation of an initial speaker-independent model. The experimental data involves 5 audiobooks recorded by various renowned Czech speakers. About 20 min long portions of each audiobook were precisely manually segmented by phonetic experts. We focused mainly on the optimal setting of the iterative segmentation procedure and explored the effect of the most relevant parameters on the resulting segmentation accuracy.
Minimally Invasive Cytopathology and Accurate Diagnosis: Technical Procedures and Ancillary Techniques

In recent years, the demand for cytopathological accurate diagnoses has increased as expanding minimally invasive procedures obtain materials from patients with advanced cancer for diagnostic, prognostic, and predictive purposes. However, inadequate knowledge of cytopathological technical procedures and ancillary techniques by clinicians remains the most common reason for the limited availability of cytopathology. The objectives of this review were to understand the technical procedures, ancillary techniques, and application and effectiveness of various types of tests in cytopathology. Each of the many ancillary technologies described in the literature has specific advantages and limitations and laboratories select one or more methods depending on their infrastructure and expertise to achieve the goal from initial screening of the disease to the final diagnosis of the cytopathology. This paper systematically reviews the development of cytopathology, summarizes the existing problems in cytopathology and the new progress of auxiliary examination, to provide a theoretical basis for the advanced development of cytopathological diagnostic technologies and to consolidate the minimally invasive and accurate diagnosis of cytopathologies for clinicians. Cytopathology offers many advantages over other clinical examinations, particularly for minimally invasive and accurate diagnosis.
GCNXSS: An Attack Detection Approach for Cross-Site Scripting Based on Graph Convolutional Networks

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods.However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR).Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers.In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN.Previously, other methods required training with the help of labeled data after embedding to complete data classification.With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload.First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships.Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results.Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms.Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.
Double-image encryption algorithm based on discrete fractional angular transform and fractional Fourier transform

By combining fractional Fourier transform with discrete fractional angular transform, a double-image encryption algorithm is designed. The discrete cosine transform is performed on two grayscale images to generate a spectrum image, and then the generated spectrum image is compressed into an image with Zigzag scanning. The compressed image is processed with the discrete fractional angular transform, and then fractional Fourier transform and double random phase coding are executed on the image. The DNA operation controlled by chaotic system is introduced to change the pixel values. Finally, the ciphertext image is obtained through bit-level permutation and pixel adaptive diffusion. The statistical information of the plaintext images is employed as the input of the SHA-256 to calculate the initial conditions of the chaotic map. Simulation experiments demonstrate that the double-image encryption algorithm can effectively reduce the correlation among adjacent pixels of the plaintext images.
Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image

Attention allocation reflects the way of humans filtering and organizing the information. On one hand, different task scenarios seriously affect human's rule of attention distribution, on the other hand, visual attention reflecting the cognitive and psychological process. Most of the previous studies on visual attention allocation are based on cognitive models, predicted models, or statistical analysis of eye movement data or visual images, however, these methods are inadequate to provide an inside view of gaze behavior to reveal the attention distribution pattern within scenario context. Moreover, they seldom study the association rules of these patterns. Therefore, we adopted the big data mining approach to discover the paradigm of visual attention distribution. We applied the data mining method to extract the gaze patterns to discover the regularities of attention distribution behavior within the scenario context. The proposed method consists of three components, tasks scenario segmented and clustered, gaze pattern mining, and association rule of frequent pattern mining. The proposed approach is tested on the operation platform. The complex operation task is simultaneously segmented and clustered with the TICC-based method and evaluated by the BCI index. The operator's eye movement frequent patterns and their association rule are discovered. The results demonstrate that our method can associate the eye-tracking data with the task-oriented scene data. The proposed method provides the benefits of being able to explicitly express and quantitatively analyze people's visual attention patterns. The proposed method can not only be applied in the field of aerospace medicine and aviation psychology, but also can likely be applied to computer-aided diagnosis and follow-up tool for neurological disease and cognitive impairment related disease, such as ADHD (Attention Deficit Hyperactivity Disorder), neglect syndrome, social attention differences in ASD (Autism spectrum disorder).
Multiplexing quantum tunneling diodes for random number generation

Random numbers are indispensable resources for application in modern science and technology. Therefore, a dedicated entropy source is essential, particularly for cryptographic tasks and modern applications. In this work, we experimentally demonstrated a scheme to generate random numbers by multiplexing eight tunnel diodes onto a single circuit. As a result, the data rate of random number generation was significantly enhanced eightfold. In comparison to the original scheme that employed one diode, this multiplexing scheme produced data with higher entropy. These data were then post-processed with the Toeplitz-hashing extractor, yielding final outputs that achieved almost full entropy and passed the U.S. National Institute of Standards and Technology (NIST) Special Publication 800-90B validation. These data also passed the NIST Special Publication 800-22 statistical randomness examination and had no sign of patterns detected from an autocorrelation analysis.
Why do Competition Authorities need Artificial Intelligence?

Recent technological developments are transforming the way antitrust is enforced as well as the way market players are infringing competition law. As a result, enforcers are starting to equip themselves with sophisticated digital investigation tools. This paper explores this interest in building an Artificial Intelligence (AI) arsenal for the fight against algorithmic infringements. What are the key factors motivating regulators to develop their own technological tools to enforce competition law? Building on interviews with a number of competition authorities, this paper finds that changes in digital markets, the need for enforcers to reverseengineer companies’ algorithms in order to better understand their implications for competition law, the need to enhance efficiency and keep pace with the fast evolution of the digital economy, and, finally, the decrease in leniency applications, are all reasons for which competition authorities should strive for more innovative and alternative means to boost their ex officio investigations.
Domain-Specific Heuristics in Answer Set Programming: A Declarative Non-Monotonic Approach

Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur in practice, for example, when picking an item that has not yet been placed in bin packing. Therefore, we present novel syntax and semantics for declarative specifications of domain-specific heuristics in ASP. Our approach supports heuristic statements that depend on the partial assignment maintained during solving, which has not been possible before. We provide an implementation in Alpha that makes Alpha the first lazy-grounding ASP system to support declaratively specified domain-specific heuristics. Two practical example domains are used to demonstrate the benefits of our proposal. Additionally, we use our approach to implement informed search with A*, which is tackled within ASP for the first time. A* is applied to two further search problems. The experiments confirm that combining lazy-grounding ASP solving and our novel heuristics can be vital for solving industrial-size problems.
Email spam detection using bagging and boosting of machine learning classifiers

The increase in the popularity, utility, and significance of electronic mails has also raised the exposure of spam emails. This paper endeavours to detect email spam by constructing an ensemble system using bagging and boosting of machine learning techniques. The dataset used for the experimentation is Ling-Spam Corpus. The system detects spam email by bagging the machine learning-based multinomial Naïve Bayes (MNB) and J48 decision tree classifiers followed by the boosting technique of converting weak classifiers into strong by implementing the Adaboost algorithm. The experimentation includes three different experiments and the results attained are compared with each other. Experiment 1 employs the individual classifiers, experiment 2 ensembles the classifiers with bagging approach, and experiment 3 ensembles the classifiers by implementing the boosting approach for the email spam detection. The effectiveness of the ensemble methods is manifested by comparing the evaluated results with individual classifiers in terms of evaluation metrics.
Autoencoder-based background reconstruction and foreground segmentation with background noise estimation

Even after decades of research, dynamic scene background reconstruction and foreground object segmentation are still considered as open problems due to various challenges such as illumination changes, camera movements, or background noise caused by air turbulence or moving trees. We propose in this paper to model the background of a frame sequence as a low dimensional manifold using an autoencoder and compare the reconstructed background provided by this autoencoder with the original image to compute the foreground/background segmentation masks. The main novelty of the proposed model is that the autoencoder is also trained to predict the background noise, which allows to compute for each frame a pixel-dependent threshold to perform the foreground segmentation. Although the proposed model does not use any temporal or motion information, it exceeds the state of the art for unsupervised background subtraction on the CDnet 2014 and LASIESTA datasets, with a significant improvement on videos where the camera is moving. It is also able to perform background reconstruction on some non-video image datasets.
The Necessity of Cloud-Based Technology in Educational System

Article Open Access by Latif Zeynalli1 and Aytan Zeynalli2 1 Azerbaijan State Oil and Industry University (Baku, Azerbaijan), The Academy of Public Administration under the President of the Republic of Azerbaijan (Baku, Azerbaijan) 2 Azerbaijan State Oil and Industry University (Baku, Azerbaijan) DOI: https://doi.org/10.29202/up/11/13 Received: 29 October 2022 / Accepted: 27 November 2022 / Published: … Читать далее The Necessity of Cloud-Based Technology in Educational System
Analysing the characteristics of precipitation variation in Hefei in recent years based on multiple linear regression

Based on the multiple linear regression model, the precipitation change characteristics of Hefei from 2009 to 2019 are analyzed, and the long-term precipitation data show that the annual precipitation in northwest China, Jianghuai and eastern coast of China is increasing, and the annual precipitation in North China and central China is decreasing, and the precipitation change between regions is obviously different.In recent decades, extreme precipitation events have shown a frequent trend in China, especially in the northwest and southeast coasts and the middle and lower reaches of the Yangtze River, which not only increases the frequency of extreme precipitation but also increases the intensity of precipitation.In this context, the characteristics of precipitation change in Hefei from 2009 to 2019 were studied, and the results showed that the change of precipitation in Hefei was related to the minimum temperature and year.The change of precipitation is obvious, there is an interannual and interdecadal variation law of fluctuation change, the fluctuation amplitude increases, the uncertainty of precipitation increases, and the lowest temperature has a negative correlation with the annual precipitation, that is, the lower the lowest temperature, the greater the precipitation, which is consistent with the change trend of precipitation in the entire Jianghuai River Basin.In order to provide reference for meteorological research and the prevention of meteorological disasters.