Reviewerly/exHarmony huggingface dataset:
Source-Free Implicit Semantic Augmentation for Domain Adaptation

Unsupervised Domain Adaptation (UDA) challenges the problem of alleviating the effect of domain shift. Common UDA methods all require labelled source samples. However, in some real application scenarios, such as Federated Learning, the source data is inaccessible due to data privacy or intellectual property, and only a pre-trained source model and target data without labels are accessible. This challenging problem is called Source-Free Domain Adaptation (SFDA). To address this, we introduce a generation encoder to generate source prototypes depending on the hidden knowledge from the pre-trained source classifier. The generated source prototypes can describe the distribution of source samples in the feature space to a certain extent to solve the Source-Free problem. We also propose Source-Free Implicit Semantic Augmentation (SFISA) for adaptation. SFISA consists of two main stages: source and target class prototypes generation and Source-Free semantic augmentation adaptation based on generated class prototypes. Extensive experiments on the UDA benchmarks demonstrate the efficacy of our generation encoder and augmentation method SFISA.
Application of Dense Crowd Detection Method Based on Lightweight Neural Network in Subway Crowd Recognition

With the rapid development of rail transit network, scientific and effective urban public transportation management is of great significance to maintaining public order and planning transportation operations. This paper aims at the original network structure of YOLOv3, deletes the repeated data of the convolution module in the detection network, and optimizes the convolution method to design a lightweight network structure. Aiming at the problem of the complex posture and background of the crowd in the subway, the Darknet53 network with better performance is selected as the feature extraction network. At the same time, according to the actual speed requirements for subway pedestrian detection, the repeated data and parameters of the repeated convolution module in the deep network are deleted, and the original convolution method is replaced with a smaller depth convolution method. Thereby it reduces time complexity of network and improves the detection speed, and achieves the application of lightweight neural network model in subway crowd recognition.
«SOMETHING IS NOT COMING OUT FOR ME ON A WELL-DESERVED REST OF THE CALM LIFE»: LETTERS FROM A PASSIONATE BOOK LOVER TO THE LEADERSHIP OF THE ALL-UNION SOCIETY OF BOOK LOVERS

The article examines the correspondence of A. L., a member of the All-Union Society of Book Lovers, a pensioner from the city of Syktyvkar with the leadership of the organization. This is the longest exchange of letters, traces of which have been preserved in the State Archive of the Russian Federation. The analysis of the language, the content priorities of the addressee in the context of public policy, the reaction of the bureaucracy of the organization allows us to conclude that the public structure was not ready for activism aimed not at obtaining preferences in the space of a deficient reading culture, but at fulfilling the statutory provisions on the part of the officials of the All-Union Society of Book Lovers and other authorities in the field of reading.
Working with Factors: forcats

If you work with categorical data, you can often represent the categories by strings. Strings have some drawbacks, however. For example, a spelling mistake can easily go undiscovered. If you want your categories ordered, then the lexicographical order on strings will not always be the order you need. As an alternative to strings for categories is factors. In this chapter, we look at the functionality that the forcats package provides for creating and manipulating factors.
The Regulation of the Investment in Crypto-Assets and its role in the growth of International Trade

This article discusses the importance of enacting a regulatory framework for investing in Crypto-Assets where the need for this arose after the advent of blockchain technology and the accompanying development, The volume of trading on cryptocurrencies has increased, and Non-fungible Tokens (NFTs) have become the focus of many investors in every country in the world and are sold for a huge price. Crypto-assets are attracting investors, especially because of their features such as encryption and ease of proof of ownership equally, many countries seek to invest in crypto-assets to ensure that they have an additional financial resource that increases the volume of GDP. Crypto-assets have also played an important role in international trade by facilitating trade financing operations. Crypto-assets have helped remove traditional barriers and eliminate risks that have hindered investors, and the international trade process is proceeding more smoothly and efficiently. During this time, it was necessary to examine and discuss how to enact regulatory frameworks for investing in crypto-assets, ensuring that people and countries make the best use of them, and also prevent money-laundering and terrorist financing that might accompany it.
Ontology and web usage mining for website maintenance

The web mining and the semantic web are closely linked: on the one hand, web-mining techniques help in the construction of the semantic web on the other hand; the semantic web helps extract new knowledge. The present article presents an approach that uses ontology and web usage mining techniques for website maintenance. This work can help novice researchers start working enriched based on the extracted patterns on the site logs using an algorithm for maintain the website.
Workshop of bar soap making from waste cooking oil to the students guardian of learning class sabumi muslim homeschooling community in Sariwangi, West Bandung Regency

Community service in the form of a workshop on making solid soap from used cooking oil with the Sabumi Homeschooling Community aimed to utilize used cooking oil waste and reduce pollution caused by household waste, provide knowledge to the community about making solid soap, and open up entrepreneurial opportunities for the community. In general, the guardians of the Sabumi Homeschooling community and their students with raw materials derived from unused materials/daily household waste. The methods used the form trials of making soap from used cooking oil, counseling about waste cooking oil and making soap theoretically, demonstrations of making solid soap from used cooking oil raw materials, hands-on workshop on making solid soap from a mixture of palm oil and coconut oil raw materials, as well as assistance, reports, and consultations on solid soap printing results. This activity produces solid soap made from used cooking oil (pH 12), as well as solid soaps made from a mixture of palm oil and coconut oil. During the mentoring period, all participants were able to identify the process of curing solid soap, and 2 participants duplicated the process of making solid soap at home with their children
Efektivitas Model Think Talk Write (TTW) terhadap Kemampuan Menulis Teks Anekdot Siswa Madrasah Aliyah

This study aims to determine the effectiveness of the Think Talk Write (TTW) model on the ability to write anecdotal texts for class X MA Negeri 1 (Model) Lubuklinggau students. The research method used was quasi-experimental. Test data analysis techniques with steps to determine the average value and standard deviation, normality test, t test and non-test data techniques (Questionnaire). The results showed that the results of hypothesis testing and data analysis obtained tcount = 26,325 and Sig (2-tailed) that is 0.000.0 &lt;0.05. Meanwhile, the non-test data (questionnaire) obtained from the recapitulation of the positive response data from students showed that the answers strongly agreed and agreed with 54.8%, in the good category. In conclusion, the use of the Think Talk Write (TTW) model is effective on the ability to write anecdotal texts for class X MA Negeri 1 (Model) Lubuklinggau students.&#x0D; Keywords: Effectiveness of Think Talk Write (TTW) Model, Ability to Write Anecdo Text
Use of agile software development method to manage academic resources

Agile is a method that moves quickly, lightly, and approachable in developing a software process. The primary goal of agile methodology is to produce high-quality software in a quicker time. It is using this methodology for developing the Academic Resource in a system provided by software for maintaining academic and financial records of data efficiently and conveniently. The administration of any academic association must make records of students registered and the staff of the university on paper and keep them safe, which is a tedious task due to the increase in manual work that reduced the utilization of the efficiency of staff. This paper describes a systematic approach to developing a user-friendly, flexible, and easy-to-maintain environment for academic resources using agile's extreme programming. The execution of the different tasks to develop the system is used to widen the horizons in Agile Methodologies step by step.
Onsite Earthquake Alert and Safety Services Using Low-Cost MEMS Sensors

Due to the increase of earthquakes in the Korean peninsular, much effort has been made to prepare for earthquakes. Our prior work introduced an earthquake alert device using a low-cost MEMS acceleration sensor that can detect an earthquake using a simple artificial neural network and then send out an alert to nearby smart devices. In this study, we further extend the earthquake alert device, which can connect with multiple other earthquake alert devices to build an onsite earthquake alert system covering a small range of areas. To see the effectiveness of our system, we constructed a testbed at several camping sites that do not have earthquake alert systems. Throughput the testbed operation, our system detected two small earthquakes and successfully issued one alarm. Based on our experiments and testbeds, we believe that our earthquake alert system can be effectively used for earthquake preparedness and response even in locations where computing and networking infrastructures are not present.
Quality Assessment of 3D-Synthesized Images

In recent years, the technologies related to 3D-synthesized images, such as free viewpoint video and 3D television, have attracted considerable attention due to their extensive applications in fields like distance education, entertainment, and remote monitoring. The new viewpoints generated by the depth image-based rendering (DIBR) technique are synthesized in reference-free image environments (in “blind” environments), so there is an urgent need for no-reference quality assessment (QA) methods of 3D-synthesized images. However, most traditional no-reference QA models fail to effectively measure the geometric distortions caused by DIBR. For the 3D-synthesized image QA, this chapter first introduces the no-reference QA methods on the basis of autoregressive modeling and multi-scale natural scene statistical analysis to measure geometric distortion. Second, it describes the no-reference QA methods on the basis of pixel-based changes in transform domains to capture color and depth distortion. Third, it presents no-reference QA methods based on the structural variations caused by geometric, sharpness, and color distortions to evaluate the blurred, discontinuous, and stretched 3D-synthesized image quality. Finally, the above-mentioned QA methods are validated on relevant databases, and the necessity of proposing efficient 3D-synthesized image QA methods is pointed out.
Generating-Set Evaluation of Bloom Filter Hardening Techniques in Private Record Linkage

Private record linkage is an active field of research targeted towards linking data sets from two or more sources, while preserving the privacy of contained sensitive content. With computation and communication efficiency as other two important requirements in such a process, much attention has been given to use Bloom filters for fast encoding of data records, while maintaining privacy of the records at the same time. A number of techniques to modify a typical Bloom filter have also appeared and addresses the need to harden them against known attacks. However, the field significantly lacks quantitative measures of the privacy level introduced by such techniques. In this work, we motivate and propose the generating-set amplification factor measure to bridge some of this gap. This privacy measure aims to capture the level of uncertainty that a hardening technique introduces between its output and the input used to create a Bloom filter. We provide algorithms to compute the measure and provide an empirical assessment of the state-of-the-art Bloom filter hardening techniques with respect to the measure. Our assessment shows that current techniques may still be retaining much of the characteristics of the input, although attacks to exploit them are yet to appear.
Design and application of online living teaching cloud platform architecture for universities

In view of the common problems of the current online teaching platforms, such as high technical requirements, limited support number, difficulty in meeting the demand of blackboard writing, and unable to meet the "online+offline" hybrid teaching requirement, and so on, we propose an architecture scheme of a universal online living teaching cloud platform for universities across the country. The scheme uses the cameras and multimedia teaching equipment in the existing classroom for live broadcast, supports the CARSI federal identity authentication so that universities across the country can access and self-service management in the form of "cloud tenants". We also establish a data model based on tenants and course attributes, which realizes fine-grained authority division and authorization and strengthens the security of resource sharing. The scheme has been applied to the construction of "Yenching Cloud Classes" platform. Up to September 2020, the total number of video hits on the platform is 978 000, and the number of online viewers during peak hours is 26 837.
Application of Abstract Rotations for Forecasting the Signals of Nonlinear Dynamic Systems

Occurrence of delayed signals is quite general in control technology. In most cases it appears as a problem that partly can be tackled by signal forecasting or prediction techniques. In certain applications it is intentionally applied for realizing a special kind of machine learning as e.g., in the Fixed Point Iteration-based adaptive controllers. To improve the efficiency of such adaptive controllers a special function was constructed in 2018. This function received two input columns of different sizes and computed a rotational operator acting in a space of augmented dimension so that it exactly rotated one of the augmented input vector to the other one. Later it was revealed that its good generalization property makes it possible to develop topologically very simple neural networks as soft computing-based dynamic model representations in which the activation function of each neuron makes abstract rotations. The idea of using such functions for the prediction of the signals produced by nonlinear dynamic systems naturally arose. To the best knowledge of the Authors this is the first time when such investigations are reported. The strongly dynamic signal of a van der Pol oscillator is predicted for a relatively short time interval by this nonlinear rotational method. The results of this prediction are compared with that of a simple prediction method that applies fitted linear combinations for transforming the input array into the output. The numerical simulations indicate that the suggested nonlinear method is considerably better (more precise) than the linear one. It seems to be expedient to make further investigations for dynamical systems of different order and noise sensitivity.
A novel Blockchain-Based Data-Aggregation scheme for Edge-Enabled Microgrid of Prosumers

The concept of a microgrid has emerged as a promising solution for the management of local groups of electricity consumers and producers. The use of end-users' energy usage data can help in increasing efficient operation of a microgrid. However, existing data-aggregation schemes for a microgrid suffer different cyber attacks and do not provide high level of accuracy. This work aims at designing a privacy-preserving data-aggregation scheme for a microgrid of prosumers that achieves high level of accuracy, thereby benefiting to the management and control of a microgrid. First, a novel smart meter readings data protection mechanism is proposed to ensure privacy of prosumers by hiding the real energy usage data from other parties. Secondly, a blockchain-based data-aggregation scheme is proposed to ensure privacy of the end-users, while achieving high level of accuracy in terms of the aggregated data. The proposed data-aggregation scheme is evaluated using real smart meter readings data from 100 prosumers. The results show that the proposed scheme ensures prosumers' privacy and achieves high level of accuracy, while it is secure against eavesdropping and man-in-the-middle cyber attacks.
Offline Signature Verification in Reconstructed Space

Biometrics offers a reliable routine to uniquely recognize individuals using their physical and/or behavioral characteristics. One of the widely used behavioral biometric for a person's unique verification is signature. It is widely used as a means of authenticating a person in financial and business applications. We have proposed an efficient methodology for offline signature verification by reconstructing image space utilizing the principles of nonlinear dynamics. The performance judgment of the proposed system model is specified using public datasets such as CEDAR, and MCYT-75. The obtained outcomes are further compared against the state of the art techniques attaining acceptable results.
Development of cognitive processes in connection with the age particularities of students as a determining factor in the process of forming and developing investigative competence

The paper is an analysis of the stages of formation of investigative competence in correlation with the cognitive potential of students. The cognitive development of children is achieved in several consecutive stages, each stage being characterized by the formation of a certain type of thinking and the different structuring of mental operations. The analysis of these psycho-pedagogical aspects reveals human potential and can be correlated with the learning methodology for effective learning.
Understanding a Robot’s Guiding Ethical Principles via Automatically Generated Explanations

The continued development of robots has enabled their wider usage in human surroundings. Robots are more trusted to make increasingly important decisions with potentially critical outcomes. Therefore, it is essential to consider the ethical principles under which robots operate. In this paper we examine how contrastive and non-contrastive explanations can be used in understanding the ethics of robot action plans. We build upon an existing ethical framework to allow users to make suggestions about plans and receive automatically generated contrastive explanations. Results of a user study indicate that the generated explanations help humans to understand the ethical principles that underlie a robot’s plan.
A Novel Automated Machine-Learning Model for Lithofacies Recognition

Lithofacies represent stratigraphic structure information with high resolution, but the accuracy of lithofacies analysis is often largely affected by the complexity of geological environment. In recent years, machine learning has received increasing attention due to its feasibility in lithofacies recognition. Nevertheless, prevailing machine learning models such as CNN (Convolutional Neural Network) and DNN (Deep Neural Network) still show some disadvantages, such as: significant amounts of human interpreted data are required for data labeling, which means the accuracy level of model interpretation is still largely dominated by human factors and therefore often inefficient or unreliable. In this work, an automated hybrid framework is proposed to overcomes these shortcomings by combining K-means procedure and neural network. The framework consists of two aspects: 1) Images with the same category of lithofacies features are segmented from the resistivity images and they are fed into a network based on the K-means algorithm for clustering, which also uses other available logs to improve the accuracy of the clustering; 2) The clustered images are reconstructed into a matrix that represents extracted image features. This matrix along with the previously labeled dataset or training logs (if available) are fed into a customized CNN and MLP network, and the network assigns a determined lithofacies type to each cluster. Ultimately, automated lithofacies recognition is realized. The proposed model is applied to an oilfield in Jimusar area in Junggar basin, Xinjiang, China. The reservoir sections are heterogeneous and primarily composed of thin interbedded transitional lithologies. Rock types are complex, mainly including dolomitic siltstone, dolarenite and dolomicrite, etc. The lithofacies analysis of some oil wells in this area is carried out. The two-dimensional resistivity images and one-dimensional logs are input into the above model, and the model training results are compared with the core information and artificial analysis results, and good matching results are obtained.
Marine Predators Algorithm For Tuning DC Motor

In this study, a proportional-integral-derivative (PID) was used to drive a direct current (DC) motor. The PID parameter is set optimally based on the marine predator algorithm (MPA). The proposed MPA method aims to improve the PID control capability of DC motors. To get performance, this research takes transient response. To get MPA-PID method validation compared to other onward optimizers algorithms, such as particle swarm optimization (PSO), and conventional PID control. The performance of the developed algorithm has also been shown to be better. The performance of the developed algorithm has also been proven to be better. MPA has the ability to reduce overshoot better by 1.04% than the conventional method. MPA has a better settling time of 4.59% than the conventional method
Hubungan Minat Baca dengan Kemampuan Literasi Informasi Siswa Kelas VIII di Perpustakaan SMP Negeri 6 Malang

SMP Negeri 6 Malang has implemented the School Literacy Movement. Therefore, every day before the lesson takes place, at 0 o'clock students is required to read and be accompanied by a teacher. In this activity, each student can choose all types of reading with good quality reading interest from an early age, it is hoped that the quality of Indonesian human resources will increase and have a positive impact. This research uses a quantitative approach with a correlational method. The results of this study indicate that there is a strong relationship between reading interest and information literacy. Reading interest consists of an average of 60.36 and the frequency of results is low with a result of 46% of 100%. Information literacy skills consisted of an average of 63.75 and the frequency of results was low with a result of 55% out of 100%. With the details of the research results based on the interpretation guidelines, the correlation coefficient has a significant relationship between reading interest and information literacy of 0.635 which is included in the strong category.