Reviewerly/exHarmony huggingface dataset:
Management Mechanism of College Students' Network Work Under the Internet of Things Environment

With the popularization of computer technology, people have more and more demands for network and informatization of school management, and more and more schools begin to pay attention to the popularization of network management of school students. School network management is more difficult. In order to solve the limitations of the existing research on the network work management mechanism of college students, this paper discusses the composition of the college network student work management mechanism and the concept of the functional equation of the routing protocol algorithm. The innovative network nodes and software and hardware settings of the student network work management mechanism are briefly introduced. In addition, it discusses the workflow design of the network work management mechanism architecture of college students in the Internet of Things environment, and finally tests the application of the network work management mechanism for college students in the Internet of Things environment. The response time to data query in the network work management mechanism of college students is in a relatively stable state. The minimum query response time is 198 and the maximum query response time is 405. Therefore, the application of technology based on information visualization in smart city application scenarios is verified.
Situation Awareness Method of Distribution Network Operation Based on Data Mining Technology

In recent years, the intelligent level of the distribution network has been rising. The structure and types of corresponding equipment have become more complex, resulting in a high probability of failure as well as greater economic losses and adverse social impact. A data mining technology, namely a convolution-gating cyclic network, is introduced to mine the characteristics of the operation situation of the distribution network. Combined with the principles of compatibility and logicality, the operation situation awareness index of the distribution network is selected. The weight value of the index is calculated by using the analytic hierarchy process. The grey prediction principle is integrated. The accurate perception of the operation situation of the distribution network is realized. The experimental data indicate that the minimum error value of the operational situation awareness results obtained by the proposed method is 0.5%, which fully confirms that the proposed method has better application performance.
Research on robot path planning algorithm in modern automatic storage environment based on improved A* algorithm

In order to improve the efficiency of robot path planning in the storage environment, this study improved the A* algorithm based on the warehouse environment. In this paper, the heuristic function of the A* algorithm is optimized based on the map of the simulated warehouse environment, and the robot path planning simulations of single target point and multiple target points are carried out respectively. To evaluate the improvement of algorithm we improved on the path planning speed of the mobile robot, this study compared the performance of the improved algorithm on the path planning ability of the mobile robot with the Dijkstra path planning algorithm, the A* algorithm and the improved A* algorithm based on dynamically weighted. The results show that the improved A* algorithm based on Manhattan distance achieves optimal results in terms of computational speed, with 99.8% improvement over Dijkstra's algorithm, 98.8% improvement over the traditional A* algorithm, and 94.87% improvement over the A* algorithm based on dynamically weighted heuristic functions. The algorithm in this paper can provide support for the study of path planning of robots in automatic storage environment.
Square Table Lookup Multiparty Computation Protocol

A secure interval test protocol is a powerful and useful tool to check whether a shared secret value is within a particular interval without revealing any information about the secret value, and therefore it has important applications in secure multi-party computation, machine learning, privacy-preserving location services, and many other areas. The secure inerval test protocol can be used as a building block in larger or more complex systems. A secure table lookup protocol based on the secure interval test can be used to implement approximate calculations of certain functions. In this work, we propose a square table lookup protocol based on the secure interval test protocol, which improves the efficiency in a certain setting compared to the state-of-the-art secure table lookup protocol proposed by Morita el al. (CANS'19). We reduce the computational complexity of the table lookup protocol from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(N)$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\sqrt{N})$</tex> where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> is the number of elements in the lookup table by changing the dimension of the lookup table from a one-dimensional array to a two-dimensional array, arranging it as a square matrix, and redesigning the procedure of secure table lookup protocol.
A Packet Routing using Lightweight Reinforcement Learning Based on Online Sequential Learning

Existing simple routing protocols (e.g., OSPF, RIP) have some disadvantages of being inflexible and prone to congestion due to the concentration of packets on particular routers. To address these issues, packet routing methods using machine learning have been proposed recently. Compared to these algorithms, machine learning based methods can choose a routing path intelligently by learning efficient routes. However, machine learning based methods have a disadvantage of requiring training time. Therefore, we use a lightweight machine learning algorithm, OS-ELM (Online Sequential Extreme Learning Machine), to reduce the training time in this paper. There is a previous work about reinforcement learning method using OS-ELM, though it has a problem of low learning accuracy. Hence, we propose OS-ELM QN (Q-Network) with a prioritized experience replay buffer and multi-agent learning function to improve the learning performance. It is compared to a deep reinforcement learning based packet routing method using a network simulator. Experimental results show that the introduction of the experience replay buffer improves the learning performance. In terms of learning speed, OS-ELM QN achieves approximately 2 times speedup than a DQN (Deep Q-Network). The multi-agent learning further improves the learning speed of OS-ELM QN.
Application of Database Technology in Video Surveillance System

With the development of technology, the technological informationization of the security network video surveillance service industry has become the demand of the times. How to improve the functions of the video surveillance system and build an open security integrated monitoring management platform has become the research point of this article. This article intends to build a video surveillance system based on database technology to meet the multi-functional requirements of the surveillance system. This article mainly uses experimental methods to test the data of the monitoring system designed in this article, and then uses the comparative method to compare the speed of the three methods to calculate the data, and the data results are obtained. According to the experiment, the data processing time of the binary algorithm in the video surveillance system is within 15s. Image detection in database technology uses binary algorithms to operate and analyze it more quickly.
Implementation of a cloud-based solar radiation model using Regression Analysis for estimating photovoltaic power generation

The accurate estimation of irradiance and PV electricity output necessitate costly equipment that need regular maintenance, thereby inflating the capital cost of investment of solar PV system. In this paper, a cloud-based regression model is implemented to estimate irradiance and electricity parameters using cloud cover data. Testing the model in a site having sky regimes characterized as clear sky with scattered clouds revealed the accurate performance of the model. Daily estimations of irradiance were performed to acceptable levels of accuracies, with root mean square error of 90.2 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and correlation coefficient value of 0.77. Even higher accuracies were reported for monthly mean estimations of irradiance, with root mean square error of 60.9 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and correlation coefficient value of 0.94. Using the monthly mean irradiance estimates, the PV electricity generation output was estimated and compared to simulated performance on NREL PV Watts for a 20 kW DC system at Universite des Mascareignes. Relatively high levels of accuracies have been reported for the Black model with root mean square error of about 149 Wh and correlation of 0.92.
Type Theory

Some type theories can serve as an alternative foundation of mathematics, as opposed to standard set theory. One such well-known type theory is Martin-Löf’s intuitionistic theory of types, which is an extension of Alonzo Church’s simply-typed λ-calculus. Before you begin working with Idris, you will become familiar with the theories upon which Idris is built as a language.
Formal Systems

Before you can construct proofs of correctness for software, you need to understand what a proof is and what it means for a proof to be valid. This is the role of formal systems. The purpose of formal systems is to let you reason about reasoning—to manipulate logical proofs in terms of their form, rather than their content. This level of abstraction makes formal systems powerful tools.
IMPROVING SELF-SUPERVISED LEARNING FOR MULTI-LABEL CLASSIFICATION USING MIX-BASED AUGMENTATIONS

Self-supervised learning is an active area of research that has had great advances recently. It is concerned with overcoming the bottleneck of any supervised deep learning framework that relies on hand-crafted labels to achieve good results. Several state-of-the-art self-supervised algorithms have been proposed recently, where most of these methods were designed to learn from ImageNet. Because ImageNet is an object-centric dataset, little focus was given in the design of the pretext task to adapt to complex scene understanding tasks such as multi-label classification or object detection. In this work, we investigate the question of whether applying mix-based augmentations in the self-supervised pretraining would lead to better transferability to the complex downstream task of multi-label classification. SimSiam, a non-contrastive self-supervised algorithm, is used for pretraining along with three proposed variations of mix-based augmentations. The task of multi-label classification on PascalVOC as a non-object centric dataset is selected for evaluation of the learned features. The SimSiam with the use of the UnMix augmentation technique achieved the best performance among all our experiments with higher Mean Average Precision (mAP) than the baseline by (+3.7). We conclude that lowering the mixing probability and using the new mixed image pairs as additions to the loss function is more efficient than replacing the original images completely. Our results confirm that more research that targets multi-object centric datasets is needed in the self-supervised domain. It also shows that applying mix-based augmentations to an already existing self-supervised algorithm improves downstream performance on multi-object centric datasets. Keywords: Self-Supervised Learning, Non-Contrastive Learning, UnMix, SimSiam, Mix-Based Augmentations DOI： https://doi.org/10.35741/issn.0258-2724.58.1.66
What Is an Information Ecosystem?

What is the information ecosystem? This study reviews the surprisingly long history of ecological concepts like the “ecosystem” in library and information sciences, as well as the potential difficulties of uncritically carrying over ecological terms into the information age.
Proposing an Automatic System for Generating Super Mario Bros Datasets

Video games have become a critical part of the entertainment industry. In addition to being designed for fun, games can also be used in education, health, and many other areas. For example, some methods from the Procedural Content Generation (PCG) approach are used to create content for Super Mario Bros (SMB) algorithmically seeking to develop personalized levels based on players’ mechanics. This work aims to provide an automatic system for generating datasets composed of game logs and video frames in the Mario AI Framework, which is expected to speed up the development and validation of PCG, player modeling/profiling, and knowledge tracing methods. This also helps in the reproducibility of researches using the SMB game.
The State Program “Development of Culture”. Implementation Problems and Ways to Solve Them

The paper describes the goals and main structural elements of the State Program "Development of Culture", the first stage of which was completed in 2021. The results of its implementation were analyzed in terms of the main indicators and milestones at the levels of the state program and subprograms, including the FTP. Conclusions are drawn regarding the decrease in the effectiveness of the implementation of the structural elements of the state program in 2020, due to the detrimental effect of the coronavirus pandemic on crucial indicators. In general, the sphere of culture has ceased to be isolated and most of its branches receive state support through special programs. However, such areas as museums, theatres and libraries are experiencing financial difficulties. The authors made recommendations to improve the state policy in the field of culture within the framework of the state program and national projects.
Current problems of training specialists for IT and ICT directions

Introduction. The course towards the digitalization of all processes on a global scale has indicated a very clear prospect for the need for qualified personnel in the field of infocommunication technologies (ICT). Given the ever-growing need for the use of ICT in all areas of modern life, the issue of professional requirements for the level of training of IT and infocommunication specialists becomes relevant. Obviously, after identifying the needs and demands of manufacturing, the progression of universities in this direction should begin with an assessment of the current state of the higher education system and identifying problematic moments and interfering factors. Aim: To analyze the problem occuring in the training of IT and ICT specialists. Methodology and research methods. a review and generalization of the content of scientific sources related to the issues under discussion were used in the course of the study; collection and processing of statistical data for FESTU. Results and scientific novelty. The research part of the work includes analysis of the values of the indicators of the effectiveness of the current activities of universities based on the values of these indicators for a single university (FESTU). An analysis was performed of the values of some accreditation indicators, an analysis of the dynamics of the composition of the faculty in the Institute of Control, Automation and Telecommunications and the dynamics of the average USE score (full-time education in bachelor’s and specialist’s programs), as well as an analysis of the values of the indicators of the effectiveness of the current activities of universities. Practical significance. The results obtained allow us to form an understanding of the state of the modern system of higher education with an emphasis on training personnel for the fields of ICT and IT, which will allow in the future, based on research data, to form a sound strategy for reforming and developing the higher education system.
The pedagogical project as a way to improve the quality of teacher training

In the article, the author describes the project as a way to improve the quality of training of future teachers. The importance of using project activities in teaching students of pedagogical specialties is substantiated. The advantages of teaching students on the basis of projects are noted. Examples of project topics and a description of the stages are given. The author uses a student project to form the ability to create an educational program, since methodological knowledge gives the future teacher the freedom to implement their own ideas.
Applying Predictive Modeling to Enhancing Solar Energy

Though analysis has been provided on how factors like temperature or humidity impact panel efficiency, there has not been as much research conducted on how the various environmental conditions all relate with each other to affect solar energy output real-time. Having a model that correlates several environmental predictor variables known to impact solar energy can help determine what adjustments need to be made to optimize solar panel performance. In this project, prediction models like artificial neural networks (ANN), multiple linear regression (MLR), elastic, ridge, and lasso were used for relating environmental variables like high temperature, outside humidity, or rain rate to the total solar energy output produced. Data containing thirty-three different weather measurements and their respective solar energy outputs was obtained from the UK Power Networks and will be used as the principal dataset for the models. To check linear model assumptions like normality of residuals or heteroscedasticity for models like MLR, several functions provided from a model performance package in R verified whether the assumptions for the model were met. Predictive model selection was based on cross validation with k = 10 and Mean Squared Error (MSE). Neural networks were the highest performing model, but lasso and elastic net were the most interpretable models in terms of how conditions affected energy output.
Temporal Sentence Grounding in Videos: A Survey and Future Directions

Temporal sentence grounding in videos (TSGV), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a.k.a.,</i> natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn significant attention from researchers in both communities. This survey attempts to provide a summary of fundamental concepts in TSGV and current research status, as well as future research directions. As the background, we present a common structure of functional components in TSGV, in a tutorial style: from feature extraction from raw video and language query, to answer prediction of the target moment. Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities. We construct a taxonomy of TSGV techniques and elaborate the methods in different categories with their strengths and weaknesses. Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.
Optimum design of distance education assistant system based on wireless network

In order to solve the problems of low operating efficiency, long response time and poor stability of distance education auxiliary system, a new distance education auxiliary system based on wireless network is designed. Design the hardware part of the system business process, optimize the subsystem functions. The subsystem includes course information query, teacher and face-to-face teaching process management, plan management and so on. In order to better realise related applications of hardware, audio and video signals are collected in software design by combining video and audio signal coding technology, and MMX technology is used for synchronous processing. The experimental results show that the designed system can effectively reduce the system response time and improve the system stability. Conclusion: This study lays a solid foundation for the stable operation of the distance education auxiliary system and realises the optimisation of one distance education auxiliary system.
Network dynamic routing and spectrum allocation algorithm based on blockchain technology

In order to overcome the problems of low resource utilisation rate and high bandwidth blocking rate of traditional network dynamic routing and spectrum allocation, a network dynamic routing and spectrum allocation algorithm based on blockchain technology is proposed. In this algorithm, a hybrid integer linear model of network dynamic routing and spectrum allocation is constructed to minimise spectrum consumption and frequency. Based on the extended static heuristic algorithm of blockchain, the link with the largest load is selected to optimise the spectrum allocation, and the linear model and extended static heuristic algorithm are combined to update the frequency gap state of the link where the path is located, so as to achieve the purpose of dynamic routing and spectrum allocation of the network. The experimental results show that the spectrum utilisation rate is as high as 99.66%, and the bandwidth blocking rate is as low as 0.
DIGITALIZATION OF THE EDUCATIONAL ENVIRONMENT OF THE HIGHER EDUCATION SYSTEM

The purpose of the study is to analyze the problems of the educational environment of the higher education system identified as a result of an expert survey (on the example of the educational environment of the Academy of Civil Protection of the Ministry of Emergency Situations of Russia). The methodology of the work is determined by the need to conduct a sociological analysis of the state of the educational environment of the university (Academy of Civil Protection of the Ministry of Emergency Situations of Russia). In the article, based on the results of studying the current state of digitalization of the educational process at the Academy of Civil Protection of the Ministry of Emergency Situations of Russia in the humanitarian direction of training, the main problems of interaction between participants in the educational process, typical for universities in organizing and implementing educational activities using modern information technologies, are considered. The empirical basis of the work was the results of expert and questionnaire surveys. The article was prepared as part of the scientific and practical work carried out at the Academy of Civil Protection of the Ministry of Emergency Situations of Russia on the topic “Development of a managerial and pedagogical model for the digitalization of the educational process of a university in the humanitarian areas of training (on the example of the Academy of Civil Protection of the Ministry of Emergency Situations of Russia)”