Reviewerly/exHarmony huggingface dataset:
Applying an auction optimization algorithm to mobile edge computing for security

The great demand for mobile blockchain computing power is often unsatisfied by terminal devices, so computational tasks are offloaded to edge computing servers. This paper proposes a new mobile communication blockchain assumption, forms a computing power alliance (CPA), and builds a smart contract-based security model. First, mining difficulty compensates for the personal computing power outside the CPA to increase the block generation difficulty. Second, contract account funds are used to increase the cost of malicious nodes seeking to launch forking attacks, and the duration is used to limit mining. Finally, a court trial is opened to select validators to verify the fork. The auction algorithm is used to allocate computing power in the CPA, and a price utility function is constructed to maximize social welfare. The joint optimization algorithm increases the transaction price and improves the system security. Simulation results verify that the system security increases with the blocked funds and duration, and the forking attack success rate approaches zero as the number of validators increases. The proposed algorithm provides considerable sum utility and average revenue gains versus traditional methods under different numbers of mobile users and computational capabilities, and the security of the algorithm is also higher.
Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers.

Despite the success of fine-tuning pretrained language encoders like BERT for downstream natural language understanding (NLU) tasks, it is still poorly understood how neural networks change after fine-tuning. In this work, we use centered kernel alignment (CKA), a method for comparing learned representations, to measure the similarity of representations in task-tuned models across layers. In experiments across twelve NLU tasks, we discover a consistent block diagonal structure in the similarity of representations within fine-tuned RoBERTa and ALBERT models, with strong similarity within clusters of earlier and later layers, but not between them. The similarity of later layer representations implies that later layers only marginally contribute to task performance, and we verify in experiments that the top few layers of fine-tuned Transformers can be discarded without hurting performance, even with no further tuning.
The Situation and Development Strategy of Marxist Theory Subject Construction

In order to improve the comprehensive level of discipline construction, this study takes the Marxist theory discipline as an example to study the situation and development strategies of discipline construction.The problems of discipline construction are analyzed from three aspects: the uneven distribution of discipline construction resources, the lack of high-level leading talents, and the common phenomenon of repeated research.Combining the development trend of the rational layout of the disciplines and the transitional development of the connotation of the disciplines in the construction of the disciplines, the development strategy of consolidating the academic foundation of the disciplines, strengthening the professional construction of the discipline team, and establishing and perfecting the academic exchange mechanism is proposed to achieve the results of the existing discipline construction.The perfection.
Evaluating the use of a Personalized Learning Management System to Increase Student Enrollment in High School Physics (Evaluation, Diversity)

Engineering from the University of Victoria in 1996.She has taught engineering courses such as engineering mechanics, numerical analysis, control systems and advanced robotics.Her teaching excellence has been recognized by numerous awards.More
WMDecompose: A Framework for Leveraging the Interpretable Properties of Word Mover’s Distance in Sociocultural Analysis

Despite the increasing popularity of NLP in the humanities and social sciences, advances in model performance and complexity have been accompanied by concerns about interpretability and explanatory power for sociocultural analysis. One popular model that takes a middle road is Word Mover’s Distance (WMD). Ostensibly adapted for its interpretability, WMD has nonetheless been used and further developed in ways which frequently discard its most interpretable aspect: namely, the word-level distances required for translating a set of words into another set of words. To address this apparent gap, we introduce WMDecompose: a model and Python library that 1) decomposes document-level distances into their constituent word-level distances, and 2) subsequently clusters words to induce thematic elements, such that useful lexical information is retained and summarized for analysis. To illustrate its potential in a social scientific context, we apply it to a longitudinal social media corpus to explore the interrelationship between conspiracy theories and conservative American discourses. Finally, because of the full WMD model’s high time-complexity, we additionally suggest a method of sampling document pairs from large datasets in a reproducible way, with tight bounds that prevent extrapolation of unreliable results due to poor sampling practices.
Study on Gender Identification Based on Audio Recordings Using Gaussian Mixture Model and Mel Frequency Cepstrum Coefficient Technique

Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This experiment is fulfilled by training the pattern classifier using gender dependent data. In order to train the model, a speech database is obtained from an online speech corpus comprising of both male and female speakers. During the testing phase, apart from the data from speech corpus, audio recordings of UTM students will too be used to determine the accuracy rate of this speaker identification experiment. As for the technique to run this experiment, Mel Frequency Cepstrum Coefficient (MFCC) algorithm is used to extract the features from speech data while Gaussian Mixture Model (GMM) is used to model the gender identifier. Noise removal was not used for any speech data in this experiment. Python software is used to extract using MFCC coefficients and model the behavior using GMM technique. Experiment results show that GMM-MFCC technique can identify gender regardless of language but with varying accuracy rate.
Cross-Network User Matching Based on Association Strength

Cross-network user matching is the one of the fundamental problems in social network fusion and analysis. This paper proposes an unsupervised algorithm based on association strength to address this problem. Specifically, the algorithm firstly computes the association strength between nodes based on user behavior characteristics in the social network graph. Then, the node sequence is obtained by the biased walk method based on the association strength, and word2vec method is utilized to learn the node feature vector. Finally, cross-network user matching is achieved based on the distance of feature vectors. The highlight of this paper is that the bias walking based on the association strength can select nodes as the context to form a specific sequence, and then do benefits on feature vectors learning. The experimental results show that the proposed algorithm can effectively match user without any prior knowledge. Furthermore, the matching precision is improved compared to the baseline method.
Creativity and skills formation features creative thinking in preschool children through literary creation.

Abstract:  Research into the formation of creative thinking is based on works of foreign and domestic classics of pedagogy and psychology, pedagogical and psychological theories of the development of creative abilities personality. “Creativity in the narrow sense of the word is a divergent thinking, the hallmark of which is multidirectionality and variability of search for different, equally correct decisions regarding the same situation.
Elliptic net scalar multiplication upon Koblitz curves

Elliptic net scalar multiplication (ENSM) is a recent trend in cryptography. The first ENSM was constructed using short Weierstrass's division polynomials over a prime field. However, the ENSM over binary field is unknown. Hence, this study proposes a scalar multiplication via elliptic net upon Koblitz curves over binary field. The objectives outlined in this study are to investigate the relationships between division polynomials, elliptic divisibility sequences, and two types of Koblitz curve over binary field. Additionally, this study looked into the new relationship established between elliptic net and its scalar multiplication. The explicit formulae for ENSM are proposed and their computational costs of field operations are evaluated and discussed.
Eclipe Plug-in : An Effective way to identify Code Smells

Assuring the quality of software is a challenging task for software developers. Applying refactoring is an alternative to improvise the quality by detecting the bad smells in the code. Removing the code smells and avoiding anti-patterns is another idea for performing refactoring. Different perspectives of refactoring have been suggested by researchers using different tools. More than twenty types of code smells are generalized by researchers in the past. The aim of the paper is to identify “type check code smell” in an open source java based software. Eclipse Plug-in –jDeodorant have been considered in the study for uncovering the type checking code smell. For this propose, Java based open source project jHotDraw (Version 1.4R4) has been taken into account which is an open source java based software product, used for two dimensional graphical framework. The tool identified the type-checking bad smells and also applied the refactoring in the necessary areas as well.
RETRACTED: The Design of College English Teaching Platform Based on Artificial Intelligence

Abstract Entering the 21st century, with the rapid development of the computer Internet, the transformation of the functions of schools in moral education and teaching management has been further strengthened, and the construction of educational information in my country has entered a new stage of comprehensive development. English teaching methods have also undergone changes. Following the reform of the Ministry of Education, we seize the opportunity to promote the further development of artificial intelligence in the field of education and provide convenience for college English teaching. The purpose of this article is to study the design of college English teaching platform based on artificial intelligence. This article introduces the development process of the intelligent teaching integrated service system in detail, and also details the system’s demand analysis, function design, and database design. This system can realize the comprehensive management of students’ grades and comprehensive intelligent services at the teacher level. The design of the integrated teaching service system of the platform is to serve many college students, provide teaching and management services, and provide college students with information and results and other information query services. Combining with the predecessors to establish a teaching platform for research, the various management systems they used, analyze the experience and shortcomings that these systems and the platform can learn from in actual teaching management services, and research and develop a set of intelligent teaching suitable for use by the unit Integrated service system. Experimental investigations and studies have shown that the most balanced function of each platform is the Max Intelligent Assistant, whose system averages about 33% in teaching, analysis, and correction. The analysis system of Yu Classroom accounts for the largest proportion of all platforms, as high as 40%. The teaching system is the best geek big data, with 40%. In general, learn from the above teaching platform design and improve it to provide learners with the most suitable teaching platform.
Research on Design Scheme of Multi-campus College Student Management System Based on Cloud Computing

Abstract With the continuous expansion of the enrollment scale of colleges and universities, the number of students in school has doubled, and the amount of student information has increased dramatically; In addition, due to the development and merger of colleges and universities, there are many campuses in many colleges and universities, which are accompanied by the dispersion of students and administrators. To facilitate unified management, a multi-campus college student management system based on cloud computing is designed and developed, which integrates information management, statistical analysis, online information publishing and collection. This paper expounds the system analysis, functional module design, system design and key technologies involved in the process of system implementation. Through the interaction among various models, a complete and dynamic system is formed, which is committed to the management of multi-campus students, so as to improve the autonomous management, self-positioning and self-improvement.
VisualSem: a high-quality knowledge graph for vision and language.

An exciting frontier in natural language understanding (NLU) and generation (NLG) calls for (vision-and-) language models that can efficiently access external structured knowledge repositories. However, many existing knowledge bases only cover limited domains, or suffer from noisy data, and most of all are typically hard to integrate into neural language pipelines. To fill this gap, we release VisualSem: a high-quality knowledge graph (KG) which includes nodes with multilingual glosses, multiple illustrative images, and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural network) model pipeline. We encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval models are publicly available and can be downloaded in this URL: https://github.com/iacercalixto/visualsem.
On the (Im)possibility of Scalable Quantum Computing

The potential for scalable quantum computing depends on the viability of fault tolerance and quantum error correction, by which the entropy of environmental noise is removed during a quantum computation to maintain the physical reversibility of the computer’s logical qubits. However, the theory underlying quantum error correction applies a linguistic double standard to the words “noise” and “measurement” by treating environmental interactions during a quantum computation as inherently reversible, and environmental interactions at the end of a quantum computation as irreversible measurements. Specifically, quantum error correction theory models noise as interactions that are uncorrelated or that result in correlations that decay in space and/or time, thus embedding no permanent information to the environment. I challenge this assumption both on logical grounds and by discussing a hypothetical quantum computer based on “position qubits.” The technological difficulties of producing a useful scalable position-qubit quantum computer parallel the overwhelming difficulties in performing a double-slit interference experiment on an object comprising a million to a billion fermions.
BARTScore: Evaluating Generated Text as Text Generation

A wide variety of NLP applications, such as machine translation, summarization, and dialog, involve text generation. One major challenge for these applications is how to evaluate whether such generated texts are actually fluent, accurate, or effective. In this work, we conceptualize the evaluation of generated text as a text generation problem, modeled using pre-trained sequence-to-sequence models. The general idea is that models trained to convert the generated text to/from a reference output or the source text will achieve higher scores when the generated text is better. We operationalize this idea using BART, an encoder-decoder based pre-trained model, and propose a metric BARTScore with a number of variants that can be flexibly applied in an unsupervised fashion to evaluation of text from different perspectives (e.g. informativeness, fluency, or factuality). BARTScore is conceptually simple and empirically effective. It can outperform existing top-scoring metrics in 16 of 22 test settings, covering evaluation of 16 datasets (e.g., machine translation, text summarization) and 7 different perspectives (e.g., informativeness, factuality). Code to calculate BARTScore is available at this https URL, and we have released an interactive leaderboard for meta-evaluation at this http URL on the ExplainaBoard platform, which allows us to interactively understand the strengths, weaknesses, and complementarity of each metric.
PELATIHAN MENGELOLA DAFTAR REFERENSI DENGAN MENGGUNAKAN MENDELEY

Compiling scientific papers is an unavoidable aspect when conducting studies in tertiary institutions. Scientific work can be in the form of theses, theses, dissertations, articles sent to journals and other writings. cannot be separated from scientific work. Citation becomes an important element in writing scientific papers. Often novice writers, students, students and the academic community in general, are caught in the act of unintentional plagiarism because they are not careful in making a citation. For example a reference is quoted, but not written on the bibliography, or vice versa. So making a and a good bibliography, is mandatory in a scientific work. Mendeley is a software whose birth was inspired by an effort to integrate citation & reference managers into a social network of scientists / researchers. With this kind of network, researchers in various parts of the world can collaborate and share research data.
Low-Rank Subspaces in GANs

The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich semantics within some subspaces. To identify these subspaces, researchers typically analyze the statistical information from a collection of synthesized data, and the identified subspaces tend to control image attributes globally (i.e., manipulating an attribute causes the change of an entire image). By contrast, this work introduces low-rank subspaces that enable more precise control of GAN generation. Concretely, given an arbitrary image and a region of interest (e.g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces. There are three distinguishable strengths of our approach that can be aptly called LowRankGAN. First, compared to analytic algorithms in prior work, our low-rank factorization of Jacobians is able to find the low-dimensional representation of attribute manifold, making image editing more precise and controllable. Second, low-rank factorization naturally yields a null space of attributes such that moving the latent code within it only affects the outer region of interest. Therefore, local image editing can be simply achieved by projecting an attribute vector into the null space without relying on a spatial mask as existing methods do. Third, our method can robustly work with a local region from one image for analysis yet well generalize to other images, making it much easy to use in practice. Extensive experiments on state-of-the-art GAN models (including StyleGAN2 and BigGAN) trained on various datasets demonstrate the effectiveness of our LowRankGAN.
Regional differentiation of higher education in Russian regions in 2020

The aim of this study was to evaluate the specific values of the indices that describe the spread of higher education institutions in all regions of Russia and the number of their students in the total working-age population living in these regions. The initial empirical data were the results of official statistical surveys conducted on information on the development of higher education, as well as the number of working -age population in eighty-two regions of the Russian Federation for 2020. In the course of the research, four mathematical models were developed. The study showed that on average, there are almost 14.8 higher education organizations per million working-age residents in the regions. It is proved that every twenty-fourth person of working age in 2020 studied under higher education programs. The conducted analysis showed the presence of a significant differentiation of the values of the considered indicators by region. The regions with the maximum and minimum values of the considered indicators were identified. It is shown that higher education has received significant development in Russia.
Perceived Social Support and Psychological Well-being in College Students’ Physical Activities : The Mediating effects of Basic Psychological Needs and Physical Self-efficacy

초록·키워드 목차 오류제보하기 The purpose of this research is to investigate the structural relationships among college students" perceived social support, basic psychological needs, physical self-efficacy and psychological well-being, and to explore the ways to improve the psychological well-being of college students. To this end, a questionnaire survey will be conducted among Chinese college students. Finally, 356 valid questionnaires were statistically processed, and SPSS 23.0 and AMOS 23.0 were used for statistical analysis. The results are as follows. First, the social support perceived by college students has a significant impact on basic psychological needs. Second, the social support perceived by college students has a significant impact on physical self-efficacy. Third, the social support perceived by college students has a significant impact on psychological well-being. Fourth, the structural equation model support the mediating effects of basic psychological needs. Fifth, the structural equation model support the mediating effects of physical self-efficacy. The result of this research is demonstrated positive direct and indirect relationships between social support, basic psychological needs, physical self-efficacy and psychological well-being. #Social support #Psychological well-being #Basic psychological needs #Physical self-efficacy AbstractⅠ. 서론Ⅱ. 연구방법Ⅲ. 결과Ⅳ. 논의Ⅴ. 결론 및 제언참고문헌