Reviewerly/exHarmony huggingface dataset:
TERHADAP HASIL BELAJAR AKUNTANSI PADA PESERTA DIDIK KELAS X AK SMK NEGERI 1 BANDAR LAMPUNG TAHUN PELAJARAN 2020/2021

The main problems in this study are 1) accounting learning outcomes are still low, 2) students' lack of interaction and interest in learning and, 3) lack of maximum understanding of students in studying accounting using the E-learning method with the google classroom application. The purpose of this study was to determine the effect of the Blended Learning learning model on accounting learning outcomes in class X AK students at SMK Negeri 1 Bandar Lampung in the 2020/2021 academic year. This study uses an experimental method using the Blended Learning learning model. The population in this study were students of class X AK, totaling 74 students, the sample consisted of class X AK 1 as the experimental class, totaling 37 people, and X AK 2 as the control class, totaling 37 people. Hypothesis testing using the statistical formula ttest. Hypothesis testing and calculation of accounting learning outcomes data using statistical formulas, obtained thit = 5,99 and ttab = 2.00 at a significant level of 5%. This means that there is an effect of the Blended Learning learning model on accounting learning outcomes in class X AK students at SMK Negeri 1 Bandar Lampung for the 2020/2021 Academic Year.
Semi-Automated Text Categorization Using Demonstration and Integration Based Term Set

Manual Analysis of massive amounts of textual data requires incredible amount of processing time and effort in the interpretation of the text and organizing them in required format. In the current scenario, the major problem is with text or document categorization because of the high dimensionality of feature space. Now-a-days there are many methods available to deal with text feature selection. This paper aims at one such semi-automated text categorization feature selection methodology to deal with a enormous data using two phases of David Merrill’s First principles of instruction (FPI). It uses a pre-defined category group by providing them with the proper training set based on the demonstration and integration phase of FPI. The methodology involves the text tokenization, text categorization and text analysis.
LibGuides: ENG 151 - SIFT Method for Evaluating Sources: Home

This guide explains how to use the SIFT Method for evaluating sources. SIFT stands for Stop, Investigate the claim, Find better coverage, and Trace back to the original source.
Overview of Data Compression Methods

But now it is time for a first overview of the different methods of data compression. In run-length encoding, identical bit patterns within a digital string that occur several times in succession are stored only once together with the number of times they are repeated. In entropy coding, sequences of bits of different lengths are assigned to individual plaintext characters, under the motto “the more frequent, the shorter”. In dictionary coding, one creates a so-called dictionary step by step, to which one only ever refers in the case of repeating sequences. In irrelevance reduction and quantization, the limited resolution of the human eye and the limited human hearing ability are used and irrelevant information is simply omitted.
PROJETO “DESBRAVANDO O CORPO HUMANO ATRAVÉS DA ANATOMIA”: REFLEXÕES, ADAPTAÇÕES E EXPERIÊNCIAS NO ENFRENTAMENTO DA PANDEMIA DE COVID-19

The university outreach project "Discovering the human body through anatomy" has been part of the Open University for Seniors program (UNATI) from UNIFAL-MG since 2019. However, from an exclusive physical classroom attendance approach to its continuity in a remote perspective, profound changes have occurred due to the COVID-19 pandemic. This article aims to report the current scenery of the project, highlighting the reflections, adaptations and experiences in facing the pandemic period. A Whatsapp® group was created with approximately 80 seniors, teachers and students from UNATI and varied contents about anatomy were sent weekly. According to the participants' reports, the proposed activities helped them to face the feelings of insecurity, fear and fragility linked to the pandemic. The experience of this outreach project led us to the conclusion that the study of human anatomy becomes relevant for elderly individuals, since it better enables them to look after their own bodies and health.
Minimum Common String Partition: Exact Algorithms

In the minimum common string partition problem (MCSP), one gets two strings and is asked to find the minimum number of cuts in the first string such that the second string can be obtained by rearranging the resulting pieces. It is a difficult algorithmic problem having applications in computational biology, text processing, and data compression. MCSP has been studied extensively from various algorithmic angles: there are many papers studying approximation, heuristic, and parameterized algorithms. At the same time, almost nothing is known about its exact complexity. In this paper, we present new results in this direction. We improve the known 2ⁿ upper bound (where n is the length of input strings) to ϕⁿ where ϕ ≈ 1.618... is the golden ratio. The algorithm uses Fibonacci numbers to encode subsets as monomials of a certain implicit polynomial and extracts one of its coefficients using the fast Fourier transform. Then, we show that the case of constant size alphabet can be solved in subexponential time 2^{O(nlog log n/log n)} by a hybrid strategy: enumerate all long pieces and use dynamic programming over histograms of short pieces. Finally, we prove almost matching lower bounds assuming the Exponential Time Hypothesis.
The Anatomy of Cyber Risk

We construct novel text-based measures of firm-level cyber risk exposure based on quarterly earnings calls of 12,000+ firms from 85 countries over 20+ years. We categorize each cyber-related discussion into topics that capture sentiment, monetary loss, country names, etc. We document new facts on the worldwide rise of cyber risk and its industrial and geographical composition. We characterize most affected firms and show that our indices can predict future cyberattacks. Cyber risk exposure has significant direct and contagion effects on stock returns. Finally, there is a factor structure in our firm-level measures and shocks to the common factor are priced.
Space Station Power Forecasting with LSTMs on FPGAs

Autonomous operations of space systems is an important and difficult task that will become even more imperative as space missions become increasingly remote. Accurate prediction of telemetry data can improve system monitoring and facilitate fault detection. This thesis presents a methodology for the acceleration of short-term forecasting of power data on an embedded platform designed for space. Initially, a long short-term memory (LSTM) network is trained to forecast voltage and current values from the International Space Station. This LSTM forecasts voltage and current minutes into the future while maintaining a low error rate. This LSTM network’s weights and biases are then used to create a new accelerated network which can be deployed on the FPGA of a Zynq-7045 system on a chip (SoC). The Zynq-7045 was selected because it is the same SoC used on the SHREC Space Processor, a space computer targeted for this study. A number of networks of varying sizes and history lengths are realized in hardware and evaluated against a software baseline. These networks were designed to be deployed on the resource-constrained FPGA fabric of the Zynq-7045 while maintaining the LSTM network architecture. The best performing LSTM networks were able to achieve over 3× speedup against a software baseline with minimal increase in forecasting error.
Penerapan Model Pembelajaran Cooperative Tipe Student Team Achievement Divisions (STAD) Untuk Meningkatkan Prestasi Belajar Siswa Kelas IV SD Dwijendra

This study aims to improve the student’s achievement through the application of the STAD cooperative learning model. This is a classroom action research (CAR) which is according to Sanjaya; this research was conducted in two cycles, which included four stages: planning, implementing, observing and reflecting. The sample of this research is the fourth grade students of SD Dwijendra which consist of 35 students; 15 male and 20 female. The data of student learning achievement were collected by means of learning achievement tests which then analyzed by descriptive quantitative analysis. The research results shows an improvement in the percentage of student learning achievement by 11.14% from 69.14% in cycle 1 with moderate criteria to 80.28% in cycle II with high criteria. Pre-cycle classical completeness of 28.57% increased by 45.71% to 74.28% in cycle I increased again by 11.29% to 85.57% in cycle II. Based on these results, it can be concluded that the implementation of the STAD cooperative learning model can improve the learning achievement of the fourth grade students of SD Dwijendra Denpasar.
Cross-modal Pretraining and Matching for Video Understanding

Videos are generally accompanied with multi-modal information such as audio, text, and motion. The multi-modal information is becoming an important cue for understanding video content. How to model the correlation between multi-modalities in videos is still an unsolved problem in video understanding tasks such as video action recognition, video temporal grounding, and video description. In this talk, we focus on two specific video understanding tasks (i.e., cross-modal self-supervised pretraining and temporal grounding) by exploiting the video-text cross modal information. In particular, we notice that videos are naturally accompanied by abundant text information such as YouTube titles, Instagram captions, and Movie scripts. This textual information could serve as a general information to guide us train a multi-modal network, which could be used as a general video representation to be fine-tuned on the downstream tasks, or as cross-modal matching similarity to be used for video segment retrieval. Specifically, we first present a general cross-modal pair discrimination (CPD) framework to capture this correlation between a video and its associated text. We train our CPD models on both standard video dataset (Kinetics-210k) and uncurated web video dataset (Instagram-300k) to demonstrate its effectiveness. Without further fine-tuning, the learnt models obtain competitive results for action classification on Kinetics under the linear classification protocol. Moreover, our visual model provides an effective initialization to fine-tune on downstream tasks, which yields a remarkable performance gain for action recognition on UCF101 and HMDB51. Our CPD demonstrates that pre-training on a relatively small dataset is able to yield a comparable performance to those methods of using order magnitude more data, which is meaningful and practicable for the scenarios with limited computational facilities. Second, we present a Contrastive and Compatible Matching Network (C2M-Net), to directly model the relations between language queries and video moments in a joint embedding space. This new metric-learning framework enables fully exploiting negative samples from two new aspects: constructing negative pairs from a dual matching scheme and mining negative pairs across different videos. These new negative samples could enhance the joint representation learning of two modalities via contrastive learning to maximize their mutual information. In addition, to precisely rank relatively positive pairs for accurate temporal grounding, we also learn the compatibility between queries and moments by directly regressing their IoU-based similarity. Our C2M-Net yields state-of-the-art performance on three benchmarks of CharadesSTA, TACoS, and ActivityNet-Captions.
Professional development of preschool director

The professional development of a preschool director is a pressing issue of today that requires further scientific development. The key component of the preschool director’s professional development is his/her professionalism being a complex system formation. Professional and personal development of a preschool director contributes to the institution’s competitiveness. We consider the professional and personal development to be the primary task of every manager in conducting effective institution activity.
Management Of Accreditation Archive Governance In Faculty Of Economics And Business At Jenderal Soedirman University 10 Th Isca 2020

This research was aimed to analyze the management of accreditation archive governance in Faculty of Economics and Business at Jenderal Soedirman University. The scope of the research was to analyze the need for accreditation archive and their management. The purpose of this research was to create detailed data of documents used in the accreditation process and create the representation of the management of accreditation archive during-and-post accreditation process. These findings would become the foundation to reform the management of accreditation archive governance so that if accreditation is going to be held, then the document could be provided easily and accreditation archive that is still usable could be found effectively. This research used a qualitative approach with descriptive method. Subjects in this research were the administrator, superintendent, and lecturer from each study program and department in Faculty of Economics and Business at Jenderal Soedirman University. Objects of this research were study programs and department in Faculty of Economics and Business at Jenderal Soedirman University. Result shows the details of the documents used in the accreditation process, amongst them are documents that support every standard (explained in detail in the contents of this article). There has not been any governance management applied specifically to manage documents that will be used in the accreditation process nor post accreditation archives in every study program and department in Faculty of Economics and Business at Jenderal Soedirman University. Keywords : Accreditation archive; Archive Governance; Management
INTELLIGENCE-LED POLICING AND CRIME PREVENTION IN PLATEAU STATE NIGERIA

This study investigated intelligence-led policing and crime prevention in plateau State Nigeria. The study employed survey design; the study sample size was 110 respondents who were sampled using purposive sampling techniques. Data were sourced from Personal interview and observation. The primary data for this study were generated from interview of chosen agencies within Plateau State that have knowledge of the effect of democratic rule on national security in Nigeria: evidence from post-election violence in Plateau State,( 2011-2019. data collected for this research were analyzed qualitatively through simple percentage and Chi-square.. Findings revealed that: i. Intelligence-led policing have a significant impact on crime prevention in Plateau state. ii. The Intelligence-led policing approaches are effective in crime prevention in Plateau state. iii. There are challenges associated with Intelligence-led policing in crime prevention in Plateau state. Conclusion, the study found that there is a positive relationship between; police-community partnership and crime prevention in Plateau state, intelligence policing strategies and crime prevention in Plateau state. The study therefore concludes that intelligence policing strategies can be used in the reduction and prevention of crime rate in Plateau state. The study recommends the need for more funding; more police posts; and accessible police posts that will create collaborative synergy and partnership in community policing that will be geared towards combating crime in Plateau State. The study also recommends the need for constant police patrol on hot spots; using contact persons for information; using watch programs; intelligence sharing; effective use of door to door contacts; improve hotline responses; employ familiar police officers; and reduce response time to crimes in regard to community policing preventive strategies to reducing crime in Plateau State.
LibGuides: Cataloging Work Group: Documentation

LSP Cataloging Work Group Main Lib Guide- information on events, documentation, and resources
Pedagogical Management, Affective Domain and Digital Competence in Early Childhood Teachers in Peru

The objective of this research is to determine how the variables named digital competence; affective domain and pedagogical management are related in a context of remote teaching in the educational institutions of the initial level of the UGEL 06. The population was of the census type with 105 teachers, the technique used was the survey and the instrument was the 30-question Likert scale questionnaire. The study showed that the variables named digital competence; pedagogical management and affective domain have a positive and significant relationship in a remote teaching context. Likewise, all the components of digital competence (instrumental, didactic, communicative and information search) have a positive and significant relationship with pedagogical management and affective domain.
LibGuides: High Impact Practices (HIPS): Math

LibGuide to explain and illustrate the different HIPS programs at South Piedmont Community College
Research of Personal and Educational Orientations of Chinese Students in the Context of Organization of Intercultural Interaction in the Educational Space of a Russian University

The inclusion of foreign students in the educational process of Russian higher education makes the problem of intercultural interaction actual. The selection of foreign students as subjects of intercultural interaction introduces a certain specificity in the functioning and construction of the educational space of the university, requires a preliminary study of their personal and educational needs. From the side of foreign students, the article examines Chinese students, since this contingent of students is the bearer of a specific oriental culture; represents a country that prioritizes the development of Russian-Chinese bilateral relations; and dominates in number among foreign students from the Far Abroad. The purpose of the article is to identify specific motivational, value orientations and expectations of Chinese students studying together with Russian students in vocational training programs at a Russian university in order to develop diagnostic tools used to collect data on the subjects involved in the process of interaction within the educational space of the university. Research methodology and methods. The study was carried out from the standpoint of a culturological approach, in which the subjects of pedagogical interaction are considered as representatives of a certain socio-cultural environment. In the conclusion, based on the data obtained, the main guidelines of pedagogical activity for the organization of intercultural interaction between Chinese and Russian students within the educational space of the university are presented.
RedCaps: web-curated image-text data created by the people, for the people

Large datasets of paired images and text have become increasingly popular for learning generic representations for vision and vision-and-language tasks. Such datasets have been built by querying search engines or collecting HTML alt-text -- since web data is noisy, they require complex filtering pipelines to maintain quality. We explore alternate data sources to collect high quality data with minimal filtering. We introduce RedCaps -- a large-scale dataset of 12M image-text pairs collected from Reddit. Images and captions from Reddit depict and describe a wide variety of objects and scenes. We collect data from a manually curated set of subreddits, which give coarse image labels and allow us to steer the dataset composition without labeling individual instances. We show that captioning models trained on RedCaps produce rich and varied captions preferred by humans, and learn visual representations that transfer to many downstream tasks.
A survey on deep learning in big data analytics

Over the last few years, Deep learning has begun to play an important role in analytics solutions of big data. Deep learning is one of the most active research fields in machine learning community. It has gained unprecedented achievements in fields such as computer vision, natural language processing and speech recognition. The ability of deep learning to extract high-level complex abstractions and data examples, especially unsupervised data from large volume data, makes it attractive a valuable tool for big data analytics. In this paper, we review the deep learning architectures which can be used for big data processing. Next, we focus on the analysis and discussions about the challenges and possible solutions of deep learning for big data analytics. Finally, have been outlined several open issues and research trends.
Construction of Talent Training Mode Based on Green Petrochemical Strategic Pillar Industry Cluster

Through deep understanding of the connotation of green petrochemical strategic pillar industry cluster policy, and thorough analysis of the current situation, this paper finds out the existing problems in college talents training, and explores how to build a talent training mode based on green petrochemical strategic pillar industry cluster by taking Guangdong University of PetrochemicalTechnology as an example, with the purpose of changing the traditional "professional talents" training orientation and training mode of colleges and universities, as well as cultivating high-tech talents to meet the needs of the development of industrial clusters and serving regional economic and social development.
Comparing visualizations of dimensionality reduction methodsAutoencoders and t-SNE

We present dimensionality reduction methods like autoencoders and t-SNE for visualization of high-dimensional data into a two-dimensional map. In this thesis, we initially implement basic and deep autoencoders using breast cancer and mushroom datasets. Next, we build another dimensionality reduction method t-SNE using the same datasets. The obtained visualization results of the datasets using the dimensionality reduction methods are documented in the experiments section of the thesis. The evaluation of classification and clustering for the dimensionality reduction techniques is also performed. The visualization and evaluation results of t-SNE are significantly better than the other dimensionality reduction techniques.
Like Chalk and Cheese? On the Effects of Translationese in MT Training

We revisit the topic of translation direction in the data used for training neural machine translation systems and focusing on a real-world scenario with known translation direction and imbalances in translation direction: the Canadian Hansard. According to automatic metrics and we observe that using parallel data that was produced in the “matching” translation direction (Authentic source and translationese target) improves translation quality. In cases of data imbalance in terms of translation direction and we find that tagging of translation direction can close the performance gap. We perform a human evaluation that differs slightly from the automatic metrics and but nevertheless confirms that for this French-English dataset that is known to contain high-quality translations and authentic or tagged mixed source improves over translationese source for training.