Reviewerly/exHarmony huggingface dataset:
Discussion about the article by Busygin A.E. «Russian state educational policy: in what degree it corresponds to the formation of new social reality?»

The article presents a discussion about the article by Doctor of Economics, professor Andrei Evgenievich Busygin on the problem of the evolution of higher education in Russia since Soviet times, organized by the editorial board of the journal. In connection with the possible turn of the Russian higher education system from the Bologna process to the more familiar Soviet system, it is important to think over and evaluate the possible risks, pros and cons with a historical digression into this problem. Now the topic of the development of the higher education system is relevant and in demand, it is especially acute for the younger generation and representatives of higher education
Research on Curriculum Design Method of Teaching Resource Library based on Deep Learning Technology

In order to improve the ideological understanding of computer majors and establish a correct world outlook, outlook on life and values, the ideological and political goals of computer majors are designed; (1) A new deep learning face recognition algorithm is proposed. Aiming at the limitations of many current methods of artificially designing image description features, this algorithm proposes an unsupervised big data-driven deep image feature learning and representation mechanism. (2) A new idea of single-target tracking algorithm DPL is proposed. Single target tracking is a hot research topic in the field of computer anxiety. Our proposed DPL algorithm is still very competitive with the best algorithms in the field of object tracking. Several key technology research and model design schemes for computer major courses are proposed, which enriches the relevant research content of deep learning in the field of computer vision, and has certain theoretical and practical significance.
The Role of Virtual Libraries in Enhancing Research Efficiency in the Post-COVID-19 Era

The COVID-19 pandemic has caused significant disruptions in the way research is conducted, with many researchers experiencing difficulties accessing physical libraries due to lockdowns and social distancing measures. Virtual libraries have emerged as an innovative solution to address this challenge, providing researchers with access to vast digital resources remotely. This study explores the role of virtual libraries in enhancing research efficiency in the post-COVID-19 era. The study adopts a qualitative approach, using interviews and surveys to collect data from researchers and librarians. The findings suggest that virtual libraries have significantly enhanced research efficiency by providing easy and convenient access to resources, facilitating collaboration, and supporting data management. The study recommends that institutions invest in virtual library infrastructure to enhance research productivity and efficiency.
Improved Method for Pedestrian Recognition Based on Generative Adversarial Networks

Traditional supervisory person re-id technology learning methods mainly relies on pre-marked image data, but there are a lot of unlabeled data in actual security scene, which seriously limits the application of person re-id technology in security monitoring field.Therefore, it is very important to study the semi-supervised learning of unlabeled data generated by antagonistic network.In the process of using GAN to generate data, in this paper we use global and local information of pedestrian image to generate realistic pedestrian image conditionally, and trains robust feature representations for different intraclass changes of cameras, so as to improve the accuracy of person re-id.The experimental results show that this method is more effective than the benchmark method.The performance of dataset Market1501 and Duke MTMC-reID improved by 4% and 3% respectively.
A Semi-Federated Active Learning Framework for Unlabeled Online Network Data

Federated Learning (FL) is a newly emerged federated optimization technique for distributed data in a federated network. The participants in FL that train the model locally are classified into client nodes. The server node assumes the responsibility to aggregate local models from client nodes without data moving. In this regard, FL is an ideal solution to protect data privacy at each node of the network. However, the raw data generated on each node are unlabeled, making it impossible for FL to apply these data directly to train a model. The large volume of data annotating work prevents FL from being widely applied in the real world, especially for online scenarios, where the data are generated continuously. Meanwhile, the data generated on different nodes tend to be differently distributed. It has been proved theoretically and experimentally that non-independent and identically distributed (non-IID) data harm the performance of FL. In this article, we design a semi-federated active learning (semi-FAL) framework to tackle the annotation and non-IID problems jointly. More specifically, the server node can provide (i) a pre-trained model to help each client node annotate the local data uniformly and (ii) an estimation of the global gradient to help correct the local gradient. The evaluation results demonstrate our semi-FAL framework can efficiently handle unlabeled online network data and achieves high accuracy and fast convergence.
Authoring tools to build AIED systems

Easy-to-use authoring tools allow for more efficient and less costly creation of Intelligent Tutoring Systems (ITSs). Such ITSs are shown to be educationally effective within the Artificial Intelligence in Education (AIED) literature, but authoring costs are a barrier to broader use. ITSs are more adaptive and flexible than other interactive systems, and authoring their intelligence requires more complexity than other instructional approaches. Development of ITS authoring tools has been an ongoing process for many years by many researchers. In this chapter, we discuss the considerations that go into creating these tools, such as the breadth of domains with which ITS authoring tools can engage and the role and capabilities of authors. We provide an overview of authoring tools across a variety of tutoring types, such as model-tracing, constraint-based, agent-based, and dialog-based tutors. We conclude with an assessment of the current state of authoring tools for ITSs and some discussion of their future.
Human resources as the main vector of communication: the case of Mobexpert Group

"Owners and employees represent those stakeholders who substantially shape the organisational culture. In fact, their values, their expectations, significantly influence the company’s performance. Moreover, customers see a brand through its employees, through the owner. This paper has the following objectives: to explore the expectations/possibilities ‒ as basic pillars of an effective collaboration ‒ both from the point of view of the employees and from the point of view of the owner of the Mobexpert Group, and to identify those values that are appreciated by the employees of the Mobexpert brand, which also define the owner of the Mobexpert Group. In order to achieve the proposed objectives, content analysis was conducted based on the show ‘Empire of Lions’, Season 3, i.e. regarding the experiences and opinions of employees posted on YouTube (Mobexpert People section). Following the analysis, it was found that: (1) the success of the brand is summed up in the set of expectations, outlined values, (2) trust, self-confidence, perseverance and creativity representing the foundation/stone of the employee-brand relationship, and of possible collaboration between the owner of the Mobexpert Group as investor and potential business partners. Key words: Mobexpert Group, communication, brand essence, employees, owner JEL classification: M31, O15"
Evaluating Learning Outcomes of Virtual Reality Applications in Education: A Proposal for Digital Cultural Heritage

The surge of Mobile Virtual Reality (VR) applications is getting growing attention among researchers and practitioners. The recent literature demonstrates its benefits when used for education purposes, since virtual immersion yields promising results for learning. Leveraging this trend, within the so called “digital didactics”, the need to gauge VR’s effectiveness in the didactic field has become paramount; so far, a method to connect traditional evaluation strategies to novel VR-based learning is still broadly missing. This paper investigates the problem of quantifying the learning outcomes and proposes a new didactic evaluation method for the Digital Cultural Heritage (DCH) learning. This research, conducted in a higher education institute, proposes three new Key Performance Indicators, referring to Revised Bloom’s Taxonomy (RBT) : Mnemonic (M) , Transversal (T) , and Disciplinary (D) . A questionnaire was administered by the same teacher who holds the course, to evaluate how well the application communicated information. The participants have been subdivided into two groups with the same knowledge base. The first group (1ACAT) that represents the “VR group” used the app at home to deepen their subject studies; while the second group (1AGR) that represents the “control group” consulted and studied the app only before the test. The results have demonstrated that the “control group” has a greater ability to support purely mnemonic topics (1ACAT 46.9%, 1AGR 53.1%), such as dates and simple definitions. The skills reached by the “VR group” attest to both transveral (1ACAT 52.9%, 1AGR 47.1%) and disciplinary (1ACAT 52.5%, 1AGR 47.5%) knowledge. These results validate the use of VR in teaching, demonstrating both experiential value and student involvement, but even confirming the compensatory function of VR if compared with the irreplaceable role of teachers in guiding learners to learn.
Real Time Satellite Imagery Dissemination Website

This project consists of a combination of APIs by which the user is able to visualize the data by using various tools like excel, python and R. The user can also get the data to include image file, image data, and image metadata for a particular day or date.From that date the user has the data in the form of image and detail about the image.
Generative Modeling for Time Series Via Schrödinger Bridge

We propose a novel generative model for time series based on Schrödinger bridge (SB) approach. This consists in the entropic interpolation via optimal transport be- tween a reference probability measure on path space and a target measure consistent with the joint data distribution of the time series. The solution is characterized by a stochastic differential equation on finite horizon with a path-dependent drift function, hence respecting the temporal dynamics of the time series distribution. We can esti- mate the drift function from data samples either by kernel regression methods or with LSTM neural networks, and the simulation of the SB diffusion yields new synthetic data samples of the time series.The performance of our generative model is evaluated through a series of numerical experiments. First, we test with a toy autoregressive model, a GARCH Model, and the example of fractional Brownian motion, and measure the accuracy of our algorithm with marginal and temporal dependencies metrics. Next, we use our SB generated synthetic samples for the application to deep hedging on real-data sets. Finally, we illustrate the SB approach for generating sequence of images.
Transactive Energy Control of Electric Vehicles for Grid Peak Load Shaving Based on Dynamic Electricity Price

Under the background of opening the electricity retail market, in order to promote electric vehicles (EV) to participate in the ancillary services for the power system, an EV distributed transactive energy control (TEC) method for peak load shaving based on the alliance blockchain and dynamic electricity price is proposed. Firstly, the architecture of EV TEC system with distribution system operator (DSO), EV aggregators (EVA) and EVs as the main bodies at the distribution network level is constructed based on the alliance blockchain, which realizes the hierarchical control of EVs by DSO and EVAs. Secondly, aiming to reduce the charging cost of EVs, maximize EVA revenue and reduce the load peak-valley difference, an optimization model for the charging and discharging dynamic price and strategies of EVs participating in peak load shaving in power system is established. This multi-objective optimization problem is formed through Stackelberg game, and solved by the multi-objective genetic algorithm. The charging and discharging smart contracts of EVs are realized on the alliance blockchain according to the solution results. In real-time operation, EVs are automatically controlled to conduct energy transaction according to the smart contracts. Finally, based on MATLAB, 40000 EVs are taken as an example to test the proposed method, and the results show that the proposed method can effectively achieve peak load shaving and reduce the load fluctuation rate by 22.63%.
A Machine Learning Approach to Detecting Deepfake Videos: An Investigation of Feature Extraction Techniques

Deepfake videos are a growing concern today as they can be used to spread misinformation and manipulate public opinion. In this paper, we investigate the use of different feature extraction techniques for detecting deepfake videos using machine learning algorithms. We explore three feature extraction techniques, including facial landmarks detection, optical flow, and frequency analysis, and evaluate their effectiveness in detecting deepfake videos. We compare the performance of different machine learning algorithms and analyze their ability to detect deepfakes using the extracted features. Our experimental results show that the combination of facial landmarks detection and frequency analysis provides the best performance in detecting deepfake videos, with an accuracy of over 95%. Our findings suggest that machine learning algorithms can be a powerful tool in detecting deepfake videos, and feature extraction techniques play a crucial role in achieving high accuracy.
Quantum correlation enhanced bound of the information exclusion principle

We investigate the information exclusion principle for multiple measurements with assistance of multiple quantum memories that are well bounded by the upper and lower bounds. The lower bound depends on the observables’ complementarity and the complementarity of uncertainty whilst the upper bound includes the complementarity of the observables, quantum discord, and quantum condition entropy. In quantum measurement processing, there exists a relationship between the complementarity of uncertainty and the complementarity of information. In addition, based on the information exclusion principle the complementarity of uncertainty and the shareability of quantum discord can exist as an essential factor to enhance the bounds of each other in the presence of quantum memory.
A Novel Survey on ML based Vehicle Detection for Dynamic Traffic Control

In recent decades, object detection has played animportant role in real-time applications. The threat of accidents and criminality was mounting, and the population was growing quickly as a result, covering technology is improving on a daily basis classifying items is a simple activity for humans but it is a difficult assignment for robots owing to the variety of elements, where colourful models have been used to break up this sensitive process. There are several methods for depicting multiple things this study examines several models that have been preliminarily imposed to describe numerous items in real time. This becomes a challenging task as it takes a long time to reuse an accurate object recognition. Hence, developing a novel architecture to perform genuine object detection is highly required. This study provides a method for detecting numerous items and discusses about different machine learning models, including R-CNN, SSD, and YOLO. It also indicates that several designs were utilised to build A frame for holding generally realistic models for detection, recognition, and identification.
Performance optimization-based boosting algorithm for resolving class imbalance problems in finance

In this paper, we propose a GMBoost (Geometric Mean-based Boosting) with a direct performance optimization technique to resolve class imbalance problem in financial field, such as bankruptcy, card insolvency, and card fraud. The conventional boosting models including AdaBoost, GBM, and XGBoost are used as benchmarking models for performance comparison. The main findings of 30 rounds of cross validations are as follows. First, the conventional boosting models largely depend on the specificity of majority, but ignore the sensitivity of minority. On the contrary, GMBoost proceeds with balanced learning that simultaneously considers the specificity and the sensitivity at the same time. Second, GMBoost outperforms the conventional boosting algorithms in terms of GM and AUC, and the results of the t-test for GM and AUC also showed that the performance of GMBoost is significantly different from those of the benchmarking models.
OPTIMIZING THE PRODUCT DEVELOPMENT PROCESS FOR SPEED TO MARKET, QUALITY, AND CUSTOMER SATISFACTION: A COMPARATIVE STUDY OF AGILE AND WATERFALL METHODOLOGIES

Achieving high quality products quickly and meeting customer expectations is critical to the success of any product development process.Agile and waterfall methods are two widely used approaches in product development.In order to optimize the process in terms of time to market, quality and customer satisfaction, a comparative study was conducted to determine the effectiveness of these methods.The study followed a mixed methods approach and collected data from surveys, interviews and case studies.Agile methods are known for their flexibility, adaptability and continuous improvement.In Agile, product development occurs in short periods of time called sprints, with a specific set of activities that the team performs together.Feedback is taken regularly to ensure the product meets customer requirements.The flexibility of the Agile methodology allows development teams to respond quickly to changing market trends and customer needs, reducing time to market.Waterfall methods are based on a structured and sequential approach.The development process is divided into phases, each of which must be completed before proceeding to the next.This approach provides a clear understanding of the product development process and ensures that the end product meets all required requirements.However, it may not be as adaptable to change as agile methods, resulting in longer development times.A comparative study on agile methods that are more effective in optimizing the product development process in terms of time to market, quality and customer satisfaction.One reason for this is the agile flexibility that enables development teams to react quickly to changing customer requirements and market trends.In addition, the collaborative approach of the agile methodology encourages communication and teamwork, which leads to a better understanding of customer needs and improves the quality of the end product.
A Chinese Short Text Classification Method for Tax Audit Reports based on Word Importance and Syntactic Enhancement BERT

Tax audit is an important part of the tax collection and management system, which directly affects the economic interests of the country and taxpayers. Therefore, reducing the enforcement risk in tax audit is crucial to continuously improve the tax collection and management system. Recently, the research of using deep learning to classify Chinese tax audit data to achieve this goal has attracted much attention. Inspired by BERT, this paper proposes a syntactic enhancement BERT (SE-BERT). It can improve BERT’s text understanding ability by learning input features and grammatical structure of text from text content and location embeddings. In addition, we weight the word importance calculated by TF-IDF with SE-BERT to improve the ability of recognizing local salient features. Through comparative experiments on our Chinese tax audit dataset, our method achieves better performance.
Instance-Based Continual Learning: A Real-World Dataset and Baseline for Fresh Recognition

Real-time learning on real-world data streams with temporal relations is essential for intelligent agents. However, current online Continual Learning (CL) benchmarks adopt the mini-batch setting and are composed of temporally unrelated and disjoint tasks as well as pre-set class boundaries. In this paper, we delve into a real-world CL scenario for fresh recognition where algorithms are required to recognize a huge variety of products to facilitate the checkout speed. Products mainly consists of packaged cereals, seasonal fruits, and vegetables from local farms or shipped from overseas. Since algorithms process instance streams consisting of sequential images, we name this real-world CL problem as Instance-Based Continual Learning (IBCL) . Different from the current online CL setting, algorithms are required to perform instant testing and learning upon each incoming instance. Moreover, IBCL has no task boundaries or class boundaries and allows the evolution and the forgetting of old samples within each class. To promote the researches on real CL challenges, we propose the first real-world CL dataset coined the Continual Fresh Recognition (CFR) dataset, which consists of fresh recognition data streams (766 K labelled images in total) collected from 30 supermarkets. Based on the CFR dataset, we extensively evaluate the performance of current online CL methods under various settings and find that current prominent online CL methods operate at high latency and demand significant memory consumption to cache old samples for replaying. Therefore, we make the first attempt to design an efficient and effective Instant Training-Free Learning (ITFL) framework for IBCL. ITFL consists of feature extractors trained in the metric learning manner and reformulates CL as a temporal classification problem among several most similar classes. Unlike current online CL methods that cache image samples (150 KB per image) and rely on training to learn new knowledge, our framework only caches features (2 KB per image) and is free of training in deployment. Extensive evaluations across three datasets demonstrate that our method achieves comparable recognition accuracy to current methods with lower latency and less resource consumption. Our codes and datasets will be publicly available at https://github.com/detectRecog/IBCL .