Reviewerly/exHarmony huggingface dataset:
A Correlation Filter Tracking Algorithm Incorporating Time Information

A target tracking algorithm that incorporates time information into the relevant filter is proposed, LTCF. LTCF is a combination of LT model and CFM model. We first analyze from the tag data that the target has time continuous characteristics and relative time information during the movement. Then, the LT model is designed on the basis of LSTM algorithm, which mainly to predict the time information about the target of the next frame. Finally, the information which predicted form LT model is combined with the CFM model, which improves the calculation time of the filter on the one hand, and reduces the overall algorithm parameters on the other. Experiment results show that compared with STRCF and SRCF, the accuracy of the LTCF algorithm is improved by 7% and 11% respectively, and LTCF algorithm model also reduces the model size while ensuring real-time tracking.
Blockchain Methodology for Smart Academic Environment in Russia

The cryptocurrency market is only at the beginning of its evolutionary cycle in Russia. Cryptocurrencies are volatile and dependent on many factors. The problem of investing in cryptocurrency exchanges is relevant. The problem of developing and liberalizing the financial system to self-regulatory is important. Society and education need skills of a systems analysis for regulating the cryptocurrency market. In particular analysis of the possibilities of interactions such as P2P (Person-to-Person), P2B (Person-to-Business), G2B (Government-to-Business) are required. The use of blockchain technology in education is also an urgent problem. There are possibilities for liberalizing the educational system especially smart-education. The corresponding system analysis is represented in the study, a model of adaptive cryptocurrency selection and valuation of cryptocurrency reserves are proposed. The task of improving the performance of blockchain transactions by optimizing hash addressing is also considered. The market is seen as an evolving structure and analyzed for its sustainability. It can become the basis for system analytics and research on models of the cryptocurrency market evolution and development for smart education.
Sentiment Evolution in Social Network Based on Joint Pre-training Model

Sentiment analysis is one of the key tasks of natural language understanding. Most of sentiment analysis researches revolve around sentiment classification of subjective texts. However, research in the field of sentiment evolution analysis for complex interactive texts are notable. Sentiment evolution models the dynamics of sentiment orientation over time, it can predict the stage of event development. In this paper, we propose a sentiment evolution method based on a joint model to analyze the dynamics and interactions of individual sentiment on social media such as Weibo. The model contains two modules, sentiment encoder module based on pre-training model and time series prediction module based on Long Short-Term Memory(LSTM). We conducted experiments on real-world datasets which were crawled from Weibo. The experiment demonstrated a case study that analyzed the sentiment dynamics of topics related to COVID-19. Experimental results show that our method achieve an accuracy of 88.0%, which are about 14.7% higher than the existing methods.
Guided discovery learning model with the teacher-student active learning approach, assisted by the WhatsApp solution alternative application on the COVID-19 pandemic

Abstract All the Covid-19 pandemic has destroyed the normal activities of life on earth, including the education sector. As a professional educator, the Covid-19 pandemic disaster is a challenge to become the new normal in education. The internet is a vital tool for the implementation of online learning, the object of research is students of the 4th semester of electrical engineering study program UNISNU Jepara Indonesia with the Guided Discovery Learning model with the Teacher-Student Active Learning approach assisted by the WhatsApp application. Based on the results Mid-Semester Examination and Final Semester Examination, the SPSS calculation results show a positive correlation of 0.918, so it can be concluded that the Guided Discovery Learning model with the Teacher-Student Active Learning approach assisted with the WhatsApp application as a model and alternative learning approach for the Covid-19 pandemic and as a form of continuous learning online.
An Improved Collaborative Filtering Algorithm based on Dimension Reduction and Improved Clustering

Aiming at the problems of sparse rating data, complex similarity calculation and low recommendation accuracy in traditional collaborative filtering recommendation algorithm when processing large-scale data, this paper proposes a new improved collaborative filtering recommendation algorithm. Based on the traditional collaborative filtering algorithm, this algorithm first uses the PCA algorithm to reduce the dimension of the sparse user-item rating matrix; secondly, the dimension-reduced rating matrix is combined with user attributes to construct a matrix containing both ratings and user attributes, according to the matrix, bisecting K-means clustering is performed from the two dimensions of user and item respectively; then, collaborative filtering recommendation is performed from the two dimensions of user and item respectively; finally, weighted integration of the predicted score results of the two dimensions of the user and the item obtains the final predicted score. Experiments using MovieLens100K dataset show that the algorithm proposed in this paper improves the quality of system recommendations.
Improving the organization of modern postgraduate studies: system approach

The article deals with the problems of training scientific and pedagogical personnel in Russia at the present stage of postgraduate education, which caused the need to discuss them, both at the level of the country’s leadership, and at the level of the scientific and University community. The directions of improving the organization of modern postgraduate studies in the context of a systematic approach are proposed. The directions of improving the training of postgraduates and the organization of the modern Russian postgraduate school in the context of its two types - scientific and University with the organization of different types of practice: research and teaching. The most significant among the directions proposed in the article are the following: improving the quality of recruitment of applicants to graduate school on the example of a pre-admission system of two-stage training of applicants in the system of professional development of the Research center for problems of quality of training of specialists; the implementation of master’s training programme “graduate student researcher” and “student-expert” on the basis of the principle of continuity of training programs for “masters-postgraduate” and implementation of the program and the model of problem-designresearch teaching academician I.A. Zimnyaya; formation of academic literacy in the process of formation of communicative competence of graduate students by means of educational disciplines «Academic writing» / «Scientific text», «Academic speech»; improving the organization of classroom and extracurricular independent work of postgraduates and its material and technical support; financing of postgraduate education; and a number of others. Along with the actualization of a systematic approach to improving the organization of modern graduate school activities, personal-activity, convergent and scenario approaches to improving the training of graduate students are identified. The proposed ways to solve the existing problems of the organizational plan of the postgraduate school in some of the indicated areas can have a positive impact on its improvement.
A Feature Analysis Tool for Batch RL Datasets

Batch RL is concerned about learning a decision policy from a given dataset without interacting with the environment. Although research is actively conducted on learning-related issues (e.g., convergence speed, stability, and safety), empirical challenges before learning are largely ignored. Many RL practitioners face the challenge of determining whether a designed Markov Decision Process (MDP) is valid and meaningful. This study proposes a model-based method to check whether an MDP designed for a given dataset is well formulated through a heuristic-based feature analysis. We tested our method in constructed as well as more realistic environments. Our results show that our approach can identify potential problems of data. As far as we know, performing validity analysis on batch RL data is a novel direction, and we envision that our tool serves as a motivational example to help practitioners apply RL more easily.
Short Term Power Load Forecasting Based on Meteorological Accumulation and AcA-GRNN

According to the characteristics of cumulative effect of meteorological conditions and different meteorological conditions on load, a weighted geometric distance formula is used to select training samples of neural network, which not only speeds up the training speed of neural network, but also strengthens the approximation ability of neural network. Compared with traditional neural networks, the training process of generalized regression neural network is actually adjusting the smoothing parameter sigma constantly. Therefore, the simultaneous interpreting of the different values of o has an important influence on the output of the network. When optimizing the smoothing parameters of the generalized regression neural network, the ant colony algorithm, a new optimization algorithm based on ant colony population, is used to optimize the smoothing parameters, which greatly reduces the subjective influence of artificial selection of parameters. Finally, the effectiveness of the model is verified by an example.
An interactive e-book development based on green chemistry study on Hydrocarbon

Abstract The aim of this study is to make an interactive e-book based on the green chemistry approach. This interactive e-book was prepared by using the development rearch method by Richey, Klein, and Nelson with the DDE model (Design, Development, and Evaluation). Interactive aspects of this teaching material are the “watch for know” coloumn that presents related learning videos, links that contain related general knowledge, practice questions and competency test at the end of learning activities that consist of questions, scores, and the answer key. This interactive e-book includes green chemistry aspect as an approach by providing contextual cases in hydrokarbon subject. This interactive e-book is valid after going through two types of validation, that were content and media validation. There are two validator in each type of validation. The validation questionnaire is an instrument used in this study. The content validation questionnaire consist of subject accuracy, recency, and communication aspect. The media valiodation questionnaire consist of media design and navigation/operation.
Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems

Recently, Federated Learning (FL) gained considerable popularity as it offers an isolated and privacy-preserving mechanism to train Machine Learning models on unseen data. However, the use of the cloud server to build the global model might raise fairness and trust concerns since any FL server might try to regenerate the original data of some users. In this chapter, we review the key trust requirements for Decentralized Federated Learning (DFL) and provide the analysis in terms of fairness, trust, and privacy. We also present and compare the existing blockchain solutions for the development of fair and trustworthy FL systems.
Activities to support students' professional self-determination in higher education institutions

Higher education institutions can assist students in supporting professional self-determination through various forms and areas of activity: interaction with employers, organization of additional education, use of career guidance technologies. A study was conducted to identify the relationship between the work of universities to promote professional self-determination, the features of professional strategies and the level of employment of graduates. The following research methods were used: a survey of 150 4th and 5th-year undergraduate students from five Smolensk universities, analysis of the activities of universities in the studied area and analysis of employment data. The study showed that the number of students with targeted strategies significantly prevails in specialized universities (medicine – 100%, energy – 80%, physical education and sports – 73%) compared to classical University (63%). There is also a positive relationship between the participation of respondents in various events held by the University and the indicators of employment monitoring, as well as the percentage of graduates with active professional strategies. The results of the study showed that the effectiveness of the University's activities depends on the specifics of the provided education and the demand for the offered specialties in the labor market, the subjective attitudes of students to receive education, as well as the degree of actual implementation by universities of measures aimed at improving the competitiveness of students.
CECMLP: New Cipher-Based Evaluating Collaborative Multi-layer Perceptron Scheme in Federated Learning

Due to the large volume of available datasets and powerful computing infrastructures, federated learning has been widely explored in many scenarios, e.g. medical screening, and image processing. It refers to all participants to jointly learn shared models under the orchestration of the server without exposing their datasets. In federated learning, since the data qualities of the participants are extremely diverse, reliability is used to measure the data qualities of the participants. To make the learning task liberally and non-discriminative, participants' reliability privacy related to their data quality should be well preserved. However, the existing work assumed that the reliability of participants is transparent for the server provider, resulting in a severe challenge in practical applications. To thwart this challenge, we propose a novel federated learning scheme, which prevents each participant's training set privacy and reliability privacy from being revealed to the public. Moreover, to further reduce the impact of unreliable participants and improve training efficiency, we design a cipher-based reliability weighted method to differentiate and intensify different contributions of the (un)reliable participants for joint model training. Security analysis shows that our proposed scheme can achieve the desired security requirements. Moreover, extensive performance evaluations demonstrate that our design achieves higher accuracy and is more robust against unreliable participants than conventional federated learning.
Intelligent solar energy management service platform based on open source software architecture

The design of this platform is an intelligent service platform with open source cloud computing framework OpenStack as the carrier, open source big data software Hadoop as the driving engine, and IOT devices as the underlying devices. The underlying design of this project realizes intelligent heating manager through Internet of things technology, and improves the efficiency of solar hot water management based on cloud platform and big data. This platform achieves basic computing storage and network service by building private cloud platform. Enterprise users would log in to the front-end web server mounted under the cloud server through computers or mobile terminal devices to monitor and remotely control intelligent public thermal energy management equipment in real-time. In addition, a big data platform is built on the computing node of the cloud platform to analyze the stored business data, providing a factual basis for future business development and product optimization.
A Nonparametric High-Dimensional Mean-Vector Test Method Based On Marginal Sign

The test of the mean vector is one of the most important kinds of problem in multivariate statistics. The classic Hotelling T2 Test has a good performance. Whlie the dimension of the data is close to the sample size or even larger than the sample size, the classic Hotelling T2 Test loses its advantages due to the singularity of the sample covariance matrix. Faced with such a problem, this paper proposes a non-parametric High-Dimensional test method based on Marginal Sign. After transformation, it can be expressed as the sum of the sign test statistics of each dimension, causing it easier to infer the progressive distribution of the test statistics when the dimension approaches infinity. After giving the test statistic, we conduct the numerical experiment in this paper to try to compare the advantages and disadvantages of the new method with the existing methods. With the comparison, the method proposed in this paper has performed well both on the normal data and the the non-normal data. The non-parametric method proposed in this paper reflects its non-distribution characteristics, which can better control the probability of the II error.
Preliminary Study on BDS Participation in ISO Standardization

On the requirement of devlopment and revision of standards for global navigation satellite systems (GNSS) in the International Standardization Organization (ISO), the GNSS-related standards, which either have been standardized or are being developed in ISO, are firstly investigated in detail in this paper. Secondly, for promotion of the global application of the BeiDou Navigation Satellite System (BDS), the major areas and the key directions in the ISO technical committees are comprehensively analyzed for the future standardization by our nation. Furthermore, the specific measures and the cultivation projects that can be carried out in ISO are also presented.
Deadline Aware Task Submission and Dynamic Virtual Machine Creation Technique in Cloud Computing

The growth of cloud computing has resulted in uneconomic energy consumption, which has negatively impacted the environment through the generation of carbon emissions. One of the most important issues for green cloud environments concerns where to place new virtual machine (VM) requests across physical servers in a way that ensures reduced energy consumption. This paper proposes a deadline based dynamic virtual machine (DBDV) migration algorithm to reduce energy consumption in Cloud datacenters. Virtual machines are classified in three categories: compute intensive task, data intensive task and mix load task. Tasks have been allocated based on similar category of virtual machine. Compute intensive task is assigned to compute intensive virtual machine, data intensive task is assigned to data intensive virtual machine and mix load task is assigned to mix load virtual machine. All tasks are assigned to similar type of virtual machine without classifying virtual machine. The proposed algorithm for virtual machine mapping is based on deadline of task and migration of virtual machine. The proposed algorithm is compared with threshold-based method, PSO and Ant Colony Optimization algorithm and analyzed the result for the energy utilization and number of virtual machine migration as well as completion time. The experimental results show that a DBDV migration algorithm outperforms the existing algorithms in terms of these parameters.
Class Forge: Boosting Feature Encoder for Few-Shot Learning with Synthesized Classes

Few-shot Learning (FSL) aims to gain classification ability on novel classes with only a few labeled samples. Previous works explore meta-learning, metric learning, and graph based methods. Though data augmentation is important to enhance the generalizability of neural networks, it is not well exploited in the field of FSL. We investigate the augmentation in FSL and propose Class Forge to synthesize forged classes that help to learn an encoder with better generalization to novel classes. Specifically, Class Forge divides given base visual classes into parts and combines these parts to synthesize forged visual classes. Training with the additional forged classes forces the encoder to learn richer features that can embed different parts, so as to boost the generalization to novel classes. Intrinsically, Class Forge is a "class augmentation" method that provides a simple yet effective way to synthesize classes, other than synthesizing samples of given classes in previous works. Extensive experiments show that Class Forge yields consistent performance gain on different datasets for FSL. And the ablation studies validate that features learned with Class Forge demonstrate better generalization ability.
Texture Based Supervised Learning for Crater-Like Structures Recognition Using ALOS/PALSAR Images

Meteorite impacts participated in the formation of the Solar System and continue to modify the planetary surfaces, originating a structure present in all of them, the craters. Terrestrial craters are abundant, geological and biological significant structures and are related to large mineral ores. The Earth impact record continues to be deciphered, currently 190 terrestrial impact structures have been confirmed, and it is estimated that several hundred remain to be discovered. One of the techniques to detect a crater candidate site is Remote Sensing, however it is a difficult task, due to the large information that must be processed, the lack of discriminant features for crater and non-crater regions and appropriated methods to recognize them. We propose an approach to identify meteorite impact structures, based on textural features of ALOS/PALSAR grayscale radar images, using supervised automatic learning. For this, the quotient of HV and HH polarimetric bands of these images was calculated. The resulting images were segmented by global thresholding to generate two sets of training samples: structure type and regions type of craters and non-craters, with them different kinds of classifiers (Bayesian, Fuzzy, Genetics, Bagging, and Boost) were trained, getting accuracy between 81 to 99% for craters identification.
Aggregation and Summarization of Thematically Similar Twitter Microblog Messages

Information is one of the most important resources in our modern lifestyle and society. Users on social network platforms, like Twitter, produce thousands of tweets every second in a continuous stream. However, not all written data are important for a follower, i.e., not necessary relevant information. That means, trawling through uncountable tweets is a time-consuming and depressing task, even if most of the messages are useless and do not contain news. This paper describes an approach for aggregation and summarization of short messages like tweets. Useless messages will be filtered out, whereas the most important information will be aggregated into a summarized output. Our experiments show the advantages of our promising approach, which can also be applied for similar problems.
Pairwise Constrained Clustering and Robust Regression: A Case Study on French Enterprise Activities and Expenses Data

Argedis, a subsidiary of Total France in charge of the management and operation of part of its network of service stations. Argedis seeks to better control its expenses and its annual budget by modeling the station loads using regression models. The company believes that the service stations have different profiles depending on the type of activity, which has an impact on the level of expenses. To obtain accurate predictive models, we used the results of grouping the service stations using a semi-supervised clustering algorithm. The data describing the activities of the company show irregular values. Therefore, we choose robust regression models to remedy the irregularity detected in the data. The results obtained show that the removal of outliers improves the metrics of the models. However, other types of outliers are deemed necessary to be preserved in the data modeling steps.