Reviewerly/exHarmony huggingface dataset:
Analysis on the Application of Micro-blog in College Education and Its Enlightenment

Micro-media has built the "micro-era" in the field of media today, leading the public to participate in media interaction in a very personalized way and express their own opinions.Among them, micro-blog, as one of the latest auxiliary platforms for higher education, has a high degree of fit, high popularity and high utilization rate leading to the characteristics of subject and object, which will definitely promote and popularize the new paradigm of higher education, bringing people into a new era.The rapid rise of micro-media represented by micro-blog is largely due to the fact that it is a very "light" and simple application, which can be easily used by the widest range of people.However, as the most competitive forms of micro-media, micro-blog and WeChat are constantly changing new trends to meet user needs.According to the characteristics of micro-blog, it is possible to realize the improvement of the comprehensive literacy of universities and college students by virtue of the improvement of micro-blog use literacy.
THE ROLE OF RESEARCH COMPETENCE IN THE TRAINING OF FUTURE SPECIALISTS

The article analyzes the definitions of the concept of "competence", which have different accents. At the current stage, the traditional approach is changing to a competence approach, therefore research competence, which is one of the key factors in the training of specialists, is considered. Research competence is singled out as "a holistic, integrative quality of a person, which combines knowledge, abilities, skills, the experience of a researcher, valuable attitudes and personal qualities and is manifested in the readiness and ability to carry out research activities with the aim of obtaining new knowledge, and it is considered as an integrated personal – the professional quality of a specialist, which reflects the motivation for scientific research, the level of mastery of the methodology of pedagogical research, personally significant qualities of the researcher, in particular, such as innovative thinking, the ability to be creative and innovative. The structure of research competence of a teacher of a higher education institution was analyzed, which includes the following components: motivational and value – reflects the system of values, needs and motives of research activity; cognitive – reflects the system of methodological, professional, interdisciplinary scientific knowledge of research activity; procedural and operational - involves the possession of skills to select adequate goals and tasks methods of research and data processing, analyze scientific facts, discuss and interpret research results, implement them in practice; information and communication – involves mastering methods of data collection in accordance with hypotheses, creation of arrays of empirical data, processing of various information sources, etc.; communicative - involves the ability to work with respondents; cooperate with colleagues in research activities; personal-creative – reflects the level of development of the creative qualities of the individual; professional-reflective - reflects the ability to understand and evaluate the process and result of one's own research activity; the ability to self-regulate. The elements of research competence are singled out, which are manifested in the following abilities: identification of the purpose of activity; definition of the subject, means of activity, implementation of set actions; reflection, analysis of activity results (correlation of achieved results with the set goal). Emphasis is placed on the structure of research competence, which includes four components: motivation and stimulation of activity, organization of cognitive activity, coordinating and communicative.
CHALLENGES OF TOMORROW IN FRONT OF SCHOOLS TODAY

The article seeks to identify the main challenges that education will have to face today in order to adequately respond to the demands of the future. Visions and expectations of global organizations and forums, as well as such of national institutions responsible for educational policies, have been analyzed. Juxtaposition is also made between the projected needs of our society in the foreseeable future in terms of knowledge, skills and values, and the readiness of the contemporary educational system to teach and develop them in today’s students.
DIGITAL TOOLS FOR INNOVATIVE EDUCATIONAL DESIGN IN THE KINDERGARTEN

The integration and meaningful application of digital tools in early childhood education is one of the pressing innovative issues of our time. The article provides a brief analysis of a conceptual framework for the integration and assessment of the impact of technology on teaching and learning (SAMR), enabling pedagogues to become familiar with the methods of integration of digital technologies and tools in the teaching process. Some of the existing digital educational tools are structured and characterized in terms of functions and possibilities for creating innovative educational design in kindergarten.
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning

Recent works have shown that self-supervised learning can achieve remarkable robustness when integrated with adversarial training (AT). However, the robustness gap between supervised AT (sup-AT) and self-supervised AT (self-AT) remains significant. Motivated by this observation, we revisit existing self-AT methods and discover an inherent dilemma that affects self-AT robustness: either strong or weak data augmentations are harmful to self-AT, and a medium strength is insufficient to bridge the gap. To resolve this dilemma, we propose a simple remedy named DYNACL (Dynamic Adversarial Contrastive Learning). In particular, we propose an augmentation schedule that gradually anneals from a strong augmentation to a weak one to benefit from both extreme cases. Besides, we adopt a fast post-processing stage for adapting it to downstream tasks. Through extensive experiments, we show that DYNACL can improve state-of-the-art self-AT robustness by 8.84% under Auto-Attack on the CIFAR-10 dataset, and can even outperform vanilla supervised adversarial training for the first time. Our code is available at \url{https://github.com/PKU-ML/DYNACL}.
Amphibi House Sebagai Mitigasi Banjir Rob di Daerah Pesisir Belawan Bagan Deli

Generally, tidal flooding that comes every day to belawan Bagan Deli beach is a serious problem. Many of the causes of this tidal flood include global warming, seawater at high tide, and poor drainage due to the large piles of garbage. The tide level reached 2.7 meters from the current sea level, and people's economic activities stopped for a while. The pile of garbage caused the surrounding houses to become shabby and dirty. This study aims to obtain a home design solution in anticipation of high tide due to tidal flooding that enters residents' homes with the concept of floating houses in the Bagan Deli area. The research method uses the floating foundation engineering design method by using a floating system on the foundation (base) of the house and the connection on the pole so that the house remains in place (fixed position). The results of this study will be conveyed back to the community and local government to improve the quality of life of the people in the Bagan Deli Belawan area.
Analisis Data PMB di STITEK Bontang dengan FP Growth dan Apriori Untuk Mendukung Strategi Promosi di Masa Pandemi

Data on new student admissions is always available and owned by all universities, both private and public. One of the private universities that has this data is STITEK Bontang (Bontang College of Technology). This data can be used as a reference or database owned by STITEK Bontang to be used optimally. This utilization is used as strategic information for new student admissions during the pandemic. This study aims to group new student data using data mining techniques. The data mining technique used is the FP Growth algorithm and the Apriori Algorithm. For the research steps using the CRISP-DM methodThis research is used to determine the right Promotion Strategy. Determining the right promotional strategy will be able to reduce promotional costs and achieve the right promotion goals. 1) Fp-Growth and Apriori methods in building a knowledge base from a collection of student databases accepted at STITEK Bontang by showing the relationship between student identity and the study program that the student chooses. 2) Obtained the most entrances with a lift ratio of 2.
¿Puede un registro basado en blockchain funcionar como una herramienta de “resguardo/protección” para las obras de arte generadas por la inteligencia artificial?

The present paper intends to reflect how artificial intelligence (AI) artworks question the need to dynamise copyright and expose the role of technology both in the creative process and the management of copyright. The purpose is to evidence the intersection between art and technology and evoke, faced with the present legal gap of intellectual property (IP) with regard to AI generated artworks, the chance to implement the use of systems or mechanism lined up with technology to execute, enforce and claim rights as well as for an adequate, active and agile surveillance based on safe, traceable and immutable information. In order to do this, the tensions and frictions between human and artificial creativity within the framework of IP are approached and special characteristics of different cases are analysed. At the same time, the effectiveness of blockchain technologies and their impact on the IP ecosystem are studied and discussed through the whitepapers published by the world’s highest IP organization (WIPO). A highlight in this work is the underlying need of legal certainty about authorship and protection for the ever-changing realities even when there are still some gaps in the law. Finally, a solution to the dilemma and lack of copyright in this subject is not proposed but rather an alternative to contribute in this matter: this is the potential of a blockchain-based registry as an efficient tool for safeguarding “authorship/ownership” of AI generated works of art, in adittion of it’s value for that purpose and it usefulness, sustainability in time, providing transparency in the period of time before and after its legal regulation –if it’s occurs– without being an obstacle but fostering innovation.
Effort Estimation of Software products by using UML Sequence models with Regression Analysis

Software product development is an indispensible part of the society we live in. In order to produce quality products economically, efficiently and within targeted completion date, estimation for development needs to be fairly precise. This work comes up with quite a viable estimation of the development efforts for the current day web applications. The modus operandi in this work collects facts existing in the Unified Modeling Language Sequence models generated for Object based systems. These facts, in combination with customized regression analysis programs specifically written for this work were used for the required estimation. To be specific: Decision Tree, Support Vector, Extreme Gradient Boosting and Bayesian Ridge Regression methods were used to estimate the efforts. The outcomes obtained by these methodologies, established its preciseness. As per the observations from experiments conducted, it was quite evident that the Bayesian Ridge Regression is providing the best accuracy compared to other Machine Learning models.
From DSP to GSP: Sampling in Both Domains

In Discrete Signal Processing (DSP), sampling in the time domain chooses time samples from which the original bandlimited signal can be perfectly reconstructed. Uniform sampling in the time domain can be interpreted in the frequency domain as replication using a LSI filtering. Sampling in Graph Signal Processing (GSP) has been explored in either the vertex and spectral domains, but not both. Current GSP literature [1] recognizes that sampling in the vertex domain is not the same as frequency replication in the spectral domain, leading to two different sampling methods (one in the vertex domain and the other in the spectral domain). Recently [2], we showed that one can indeed develop an unified GSP sampling theory with equivalent interpretations in both the vertex and spectral domains like in DSP. We explore the steps of DSP sampling in GSP: subsampling, decimation, upsampling, and interpolation. In DSP, each of these steps has interpretations in both the time and frequency domain. We interpret each step in both the vertex and spectral domain for GSP. The unified GSP sampling theory provides many different, but valid choices for the sampling set. This paper considers the impact of these choices on GSP sampling. We show that DSP frequency replication is a result of specific choices and conditions made in the GSP sampling theory, providing new insight and intuition on DSP sampling. As a result, we show that replication in frequency and, by extension, proposed spectral sampling methods, e.g., [3], in the GSP literature, are not generalizable to arbitrary graphs and GSP, in the sense that they do not sample the original signal in the vertex domain, but rather, distort it.
An Acoustic and Voice Source Characteristics Study of Love Songs in Ewenki

This paper takes the love songs of the Ewenki traditional folk songs as the research object, extracts the acoustic parameters of five representative folk songs, including the duration, intensity and formant of the speech, as well as the fundamental frequency, open quotient and speed quotient of the voice signal. Finally, the acoustic characteristics of the speech and voice are obtained. The findings of the study can provide a new method to the study of speech and singing acoustics, and also provide parameters for reference and identification of singing speech and synthesis.
Multi-Data Center Realization and Operation Efficiency Modeling Analysis of Intelligent Physical Education System

Distributed data centers shield the metadata differences of multi-source and heterogeneous remote sensing data, and provide a basis for multi-source and multi-center remote sensing data integration. Implementation in teaching and application in extracurricular sports activities, students' physical health, etc., in order to give full play to the auxiliary role of CAI in physical education, namely user management, online course management, question bank maintenance management, real-time examination management, scoring review management, System management, user health information collection, database design. When students log in to the teaching system to inquire about required materials, they can also communicate and discuss with teachers online, which promotes good communication between students and teachers. In the application process of the system, it can be found that the teaching of this system has relatively strong flexibility and is not constrained by location and time.
Combinatorial wall-crossing for the sign representation of prime size

We present an algorithm to calculate the result of combinatorial wall-crossing at every step starting with the column partition of prime size. This algorithm is confirmed by computer calculations. The output of the algorithm is consistent with Bezrukavnikov's conjecture in the case of the sign representation.
Pedestrian Re-identification Based on Spatial Transformation and Multi-methods Feature Fusion

Aiming at the problem that the spatial features of pedestrian images are not aligned in current pedestrian re-identification and the network model cannot fully express the pedestrian information due to pose changes and occlusions, a method based on spatial transformation and multi-methods feature fusion is proposed. Firstly, for the pedestrian re-identification system, a processing method for enhancing the retrieval of pedestrians is provided, and the pedestrian images with more noise to be retrieved are denoised by means of side-window filtering; secondly, the spatial transformation network is improved. Channel attention and self-constrained branches are introduced to automatically align pedestrian spatial features to solve the problem of inconsistency in spatial semantic information caused by unaligned pedestrian image regions; then, multi-scale features are extracted from different deep layers of the backbone network, and coordinates attention and batch normalization of instances are integrated into different deep branches. Finally, the features of each branch are fused to obtain feature information with high representation ability. During the network training process, the dual loss function strategy is used to jointly optimize the model. Multiple experiments show that the proposed method has a higher recognition rate than other existing methods.
Design of Intelligent Education Management Information System in Colleges and Universities from the Perspective of Big Data

There are many internal data types in the education management information system, which leads to the poor management effect of the intelligent education management information system in colleges and universities. Design an intelligent education management information system in Colleges and universities from the perspective of big data. The hardware part of the system focuses on the design of DSP module, FPGA module, analog-to-digital conversion module, A/D conversion module interface circuit and communication interface. In the system software part, the big data technology is used to divide and allocate data sets, so as to realize the design of intelligent education management information system in colleges and universities. Experimental results show that the proposed intelligent education management information system can effectively improve the accuracy and efficiency of resource mining, and has a good effect.
Remote Sharing of National Music Teaching Resources Based on Big Data Analysis

The traditional long-distance sharing method of teaching resources is insufficient in the analysis of resources, and it is difficult to achieve a clear classification, which leads to the low integrity of resource collection and transmission. According to the functional characteristics of the curriculum resources, adjust the screening process of music teaching resources, collect and sort out folk music materials, use big data analysis technology to build open teaching mode, and design a remote sharing method. The results show that the transmission integrity of the remote sharing method is 66.990%, 57.450% and 58.190% respectively, which shows that the remote sharing method has better performance.
Using Learning Management System to Motivate Merchant Ship Crew to Learn Maritime English

This paper evaluates the uses of the Learning Management System (LMS) in motivating merchant ship crew members to learn Maritime English.The study was conducted in February 2022 on 26 participants who were undertaking the Ratings' Maritime English course at a premier maritime education and training institution in Malaysia.The findings of the quantitative study which used the Instructional Materials Motivational Survey showed that there was a medium to high level of motivation in all evaluated domains of the Attention, Relevance, Confidence and Satisfaction Model of Motivation.Further Pearson correlation indicated that there were significant correlations between the good organization of the LMS content with learners' confidence and between the layout of the LMS with learners' enjoyment learning the lesson.These findings help to further improve the delivery of the course and the development of LMS especially in features that could enhance learners' motivation to learn Maritime English in asynchronous mode.
PENGARUH ANALISIS SISTEM INFORMASI AKUNTANSI PENJUALAN, PENERIMAAN KAS, DAN PENGELUARAN KAS TERHADAP PENGENDALIAN INTERNAL

This study aims to analyze existing accounting information systems, namely implementation procedures that can monitor sales accounting information systems, monitor cash receipts accounting information systems properly, and supervise cash disbursements. This research method uses descriptive quantitative methods, with a non-probability sampling technique, with employees of PT Surya David Susanto as respondents. Data distribution of questionnaires with a sample of 31 respondents. The technique used is the Validity Test, Reliability Test, Classical Assumption Test, Multiple Linear Regression, Coefficient of Determination Test (R2), and Hypothesis Testing (T-Test and F Test). The results of this study are sales accounting information system variables do not affect internal control, cash receipts accounting information system variables affect internal control, and cash disbursements accounting information system variables do not affect internal control. Meanwhile, based on the results of the F test showing F count 12.545 &gt; F table 2.95, it is evident that the variables Sales, Cash Receipts, and cash disbursements simultaneously affect internal control.
A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery

Intent Detection is one of the tasks of the Natural Language Understanding (NLU) unit in task-oriented dialogue systems. Out of Scope (OOS) and Out of Domain (OOD) inputs may run these systems into a problem. On the other side, a labeled dataset is needed to train a model for Intent Detection in task-oriented dialogue systems. The creation of a labeled dataset is time-consuming and needs human resources. The purpose of this article is to address mentioned problems. The task of identifying OOD/OOS inputs is named OOD/OOS Intent Detection. Also, discovering new intents and pseudo-labeling of OOD inputs is well known by Intent Discovery. In OOD intent detection part, we make use of a Variational Autoencoder to distinguish between known and unknown intents independent of input data distribution. After that, an unsupervised clustering method is used to discover different unknown intents underlying OOD/OOS inputs. We also apply a non-linear dimensionality reduction on OOD/OOS representations to make distances between representations more meaning full for clustering. Our results show that the proposed model for both OOD/OOS Intent Detection and Intent Discovery achieves great results and passes baselines in English and Persian languages.
Describe me an Aucklet: Generating Grounded Perceptual Category Descriptions

Human speakers can generate descriptions of perceptual concepts, abstracted from the instance-level. Moreover, such descriptions can be used by other speakers to learn provisional representations of those concepts. Learning and using abstract perceptual concepts is under-investigated in the language-and-vision field. The problem is also highly relevant to the field of representation learning in multi-modal NLP. In this paper, we introduce a framework for testing category-level perceptual grounding in multi-modal language models. In particular, we train separate neural networks to generate and interpret descriptions of visual categories. We measure the communicative success of the two models with the zero-shot classification performance of the interpretation model, which we argue is an indicator of perceptual grounding. Using this framework, we compare the performance of prototype- and exemplar-based representations. Finally, we show that communicative success exposes performance issues in the generation model, not captured by traditional intrinsic NLG evaluation metrics, and argue that these issues stem from a failure to properly ground language in vision at the category level.