Reviewerly/exHarmony huggingface dataset:
RANCANGAN BANGUN MODUL TATA KELOLA KARYAWAN UNTUK PENGELOLAAN SUMBER DAYA MANUSIA PADA PT. SINERGI INTI SOLUSI

PT.Sinergi Inti Solusi is one of the national software solutions providers, whichprovides service-based business and high-tech solutions for customers spreadacross Indonesia and hasdevelopment models on-site and off-site. The companyhas designed, developed and deployed many solutions for classy companies inbanking and finance using the latest technology and structured frameworks. PTSinergi Inti Solusi itself makes use of theapplication Human Resource InformationSystem (HRIS)within its company. The HRIS application at PT Sinergi Inti Solusiitself is an application that includes employee management. At PT. Sinergi IntiSolusi, this application greatly simplifies business processes which greatly benefitthe company in terms of practicality. Therefore, the authors conducted researchon employee governance at the company. This research usesmethods researchand development, by managing the data that has been obtained qualitatively, bydescribing the employee governance application from the data that has beenobtained. Methods of data collection using interviews, observation and literaturestudy. Based on the data analysis, it is concluded that the solution that can solvethese problems is to create a web-based application for employee governance thatfunctions to manage employee data and employee performance within thecompany. With this application, it is hoped that it can facilitate the management ofHR (Human Resources) which contains all employee data that work
Sequence Length is a Domain: Length-based Overfitting in Transformer Models

Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training. In practice, this is usually countered either by applying regularization methods (e.g. dropout, L2-regularization) or by providing huge amounts of training data. Additionally, Transformer and other architectures are known to struggle when generating very long sequences. For example, in machine translation, the neural-based systems perform worse on very long sequences when compared to the preceding phrase-based translation approaches (Koehn and Knowles, 2017). We present results which suggest that the issue might also be in the mismatch between the length distributions of the training and validation data combined with the aforementioned tendency of the neural networks to overfit to the training data. We demonstrate on a simple string editing tasks and a machine translation task that the Transformer model performance drops significantly when facing sequences of length diverging from the length distribution in the training data. Additionally, we show that the observed drop in performance is due to the hypothesis length corresponding to the lengths seen by the model during training rather than the length of the input sequence.
Self-Supervised Bug Detection and Repair

Machine learning-based program analyses have recently shown the promise of integrating formal and probabilistic reasoning towards aiding software development. However, in the absence of large annotated corpora, training these analyses is challenging. Towards addressing this, we present BugLab, an approach for self-supervised learning of bug detection and repair. BugLab co-trains two models: (1) a detector model that learns to detect and repair bugs in code, (2) a selector model that learns to create buggy code for the detector to use as training data. A Python implementation of BugLab improves by up to 30% upon baseline methods on a test dataset of 2374 real-life bugs and finds 19 previously unknown bugs in open-source software.
Research Guides: Open LibGuide Review: LORDS (CSU Wide)

This LibGuide is a documentation of the Open LibGuide Review Sessions projects at CalPoly. CSU Wide Efforts
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations

Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment. Existing task-oriented dialog datasets aimed towards virtual assistance fall short and do not situate the dialog in the user’s multimodal context. To overcome, we present a new dataset for Situated and Interactive Multimodal Conversations, SIMMC 2.0, which includes 11K task-oriented user assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes. The dialogs are collection using a two-phase pipeline: (1) A novel multimodal dialog simulator generates simulated dialog flows, with an emphasis on diversity and richness of interactions, (2) Manual paraphrasing of generating utterances to draw from natural language distribution. We provide an in-depth analysis of the collected dataset, and describe in detail the four main benchmark tasks we propose for SIMMC 2.0. Our baseline model, powered by the state-of-the-art language model, shows promising results, and highlights new challenges and directions for the community to study.
Adaptive Image Steganographic Analysis System Based on Deep Convolutional Neural Network

Adaptive steganography emplaces the message into the hard-to-detect noise area or the complex texture area of the image, so the steganography analysis method based on artificial design features needs to design a very complex feature extraction algorithm to detect the steganography image. In view of the advantages of deep learning steganography in automatic extraction of image features and high detection accuracy, an image steganography analysis algorithm is designed by using deep convolutional neural network, and an image steganography analysis system based on deep learning is developed. The system realizes the functions of neural network structure analysis, image steganography and verification, steganography analysis and so on. The system simplifies the operation process of algorithm comparison, reduces the complexity of algorithm performance evaluation, and verifies the feasibility of the proposed algorithm.
Exploring the SHA-2 Design Space

While SHA-2 is a ubiquitous cryptographic hashing primitive, its role in emerging application domains, such as blockchains or trusted IoT components, has made the acceleration of SHA-2 very challenging due to new stringent classes of requirements imposed by such domains, especially implementation cost and energy efficiency.This Ph.D. thesis explores the SHA-2 design space from different viewpoints. Its first contribution is a reasoned classification of the many SHA-2 designs proposed in the literature according to their architectural choices, each of them having different implications on the application requirement. Based on this analysis, this thesis introduces a framework and a methodology for evaluating and comparing different implementation options, which is used to assess the impact of each architectural technique on the application requirements, as well as the effect of variations in the underlying target technology. The last contribution of this thesis explores a different approach, namely utilising a specific target technology with maximum efficiency, and the resulting SHA-2 accelerator shows the best area efficiency reported so far in the literature.
Backtranslation in Neural Morphological Inflection

Backtranslation is a common technique for leveraging unlabeled data in low-resource scenarios in machine translation. The method is directly applicable to morphological inflection generation if unlabeled word forms are available. This paper evaluates the potential of backtranslation for morphological inflection using data from six languages with labeled data drawn from the SIGMORPHON shared task resource and unlabeled data from different sources. Our core finding is that backtranslation can offer modest improvements in low-resource scenarios, but only if the unlabeled data is very clean and has been filtered by the same annotation standards as the labeled data.
USING JIGSAW LEARNING TO TEACH STUDENT’S PRONUNCIATIONABILITY TOWARD ENGLISH CLUB AT MAN 3 TULUNGAGUNGTHESIS

ABSTRACTPutro, Deri Bagus Handoyo (17203163030). Using Jigsaw Learning to TeachStudents Pronunciation Ability toward English Club at MAN 3Tulungagung. A Thesis of English Education Department, Faculty of Tarbiyahand Teacher Training, State Islamic Institute (IAIN) of Tulungagung 2020.Advisor: Dr. Dwi Astuti Wahyu Nurhayati, SS., M.Pd.Keywords: Pronunciation, American video, JigsawPronunciation is one of kinds skill in speaking that insist the students to haveability in speak up. Besides, the students should have the ability to develop theirpronunciation ability in speaking. To facilitate the students to master those aspect inpronunciation, the teaching pronunciation should be interesting. One of the ways howto create an interesting teaching learning process in pronunciation by using the strategy.The strategy is Jigsaw learning. This study was done to prove that Jigsaw learning canuse to teach students pronunciation ability in English club.The purpose of this study was to know Jigsaw learning could be teach studentspronunciation ability toward English club at MAN 3 TulungagungResearch method of this study was using qualitative research design, especiallyin descriptive qualitative. The population in this study was in English club. The samplein this study were the students of English club consists 11 students. Researchinstrument of this study administering from the observation, interview anddocumentation to analyze Jigsaw learning could be teach in pronunciation or not. Fromdeep discussion Jigsaw learning could be teach in teaching pronunciation.Based on the result of the observation, interview and documentation, it wasfound that Jigsaw learning could be teach in teaching pronunciation ability. Thosestrategy have some effect for teaching pronunciation. Based on the finding it issuggested for the teacher to use Jigsaw learning in teaching pronunciation at Englishclub of MAN 3 Tulungagung.
Track planning of multi-rotor unmanned aerial vehicle in the complex environment space

Aiming at the dynamic track planning problem of multi-rotor unmanned aerial vehicle (UAV) in complex environment space, this paper proposes a high-dimensionality-reduced space environment modellin...
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification

Graph-based Aspect-based Sentiment Classification (ABSC) approaches have yielded state-of-the-art results, expecially when equipped with contextual word embedding from pre-training language models (PLMs). However, they ignore sequential features of the context and have not yet made the best of PLMs. In this paper, we propose a novel model, BERT4GCN, which integrates the grammatical sequential features from the PLM of BERT, and the syntactic knowledge from dependency graphs. BERT4GCN utilizes outputs from intermediate layers of BERT and positional information between words to augment GCN (Graph Convolutional Network) to better encode the dependency graphs for the downstream classification. Experimental results demonstrate that the proposed BERT4GCN outperforms all state-of-the-art baselines, justifying that augmenting GCN with the grammatical features from intermediate layers of BERT can significantly empower ABSC models.
A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing

Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs to segment the graph into subgraphs and align each such subgraph to a word in a sentence; this is normally done at preprocessing, relying on hand-crafted rules. In contrast, we treat both alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. As marginalizing over the structured latent variables is infeasible, we use the variational autoencoding framework. To ensure end-to-end differentiable optimization, we introduce a differentiable relaxation of the segmentation and alignment problems. We observe that inducing segmentation yields substantial gains over using a ‘greedy’ segmentation heuristic. The performance of our method also approaches that of a model that relies on the segmentation rules of Lyu and Titov (2018), which were hand-crafted to handle individual AMR constructions.
EKSPERIMENTASI MODEL PEMBELAJARAN EXAMPLE NON EXAMPLE DAN TALKING STICK PADA MATERI BANGUN DATAR DITINJAU DARI KEMANDIRIAN BELAJAR SISWA SMP NEGERI KELAS VII Se-KABUPATEN PEMALANG TAHUN 2019/2020

The objectives of this study were to find out on the topic of two dimentional figure: (1) which was better between Example Non Example, talking stick or Direct learning  that produce learning achievement; (2) which one had better learning achievement between students with high, medium or low categories of independency learning; (3) at each category of independency learning, which was better between Example Non Example, talking stick or Direct learning that produce learning achievement; and (4) at each model pembelajaran, which one had better learning achievement between students with high, medium or low categories of independency learning. This study was a quasi-experimental with 3´3 factorial design. The population was all of students in seventh grade of State Junior High School in Pemalang Regency on Academic Year 2019/2020. The samples were taken by using a stratified cluster random sampling technique. The samples was students in seventh grade of SMP Negeri 1 Randudongkal, Moga and Pulosari. The instrument of this study were independency learning questionnaire and learning achievement test. The questionnaire tryout included content validity, internal consistency and reliability, and the test tryout included content validity, difficulty level, discrimination power, and reliability. Before the experiment was done, the balance of prior knowledge was examined by using unbalanced one way anova test. The hypothesis test used unbalanced two ways anova. Test requirements included normality test used Lilliefors method and the homogeneity test used the Bartlett test. The results of the study were as follows: (1) the Example Non Example model produce the learning achievement better than the Talking Stick and direct learning, the Talking Stick model give the same learning achievement as the direct learning; (2) the high and medium categories of independency learning have the same learning achievement, and both category of independency learning have better learning achievement from low categories of independency learning; (3) at each categories of independency learning, the Example Non Example model produce the learning achievement better than the Talking Stick and direct learning, the Talking Stick give the same learning achievement as the direct learning; and (4) at each models, the high and medium categories of independency learning have the same learning achievement, and both of category of independency learning style have better learning achievement from low categories of independency learning.
LibGuides: System Wide Holds - Training Guide: Horizon Reports & Notices

The purpose of this guide is to provide staff a reference resource for processing Requests and Holds on NLPL materials.
The Effect of Using English Song as Teaching Media to Improve Vocabulary Mastery on The Eleventh Grade Students of SMK Negeri 11 Samarinda

The purpose of this study was to determine whether there was a significant difference in vocabulary mastery between students in the eleventh grade at SMK Negeri 11 Samarinda who used English songs and those who used conventional methods. Cluster random sampling was employed in this study, which is a strategy for determining sample size based on a group's cluster and homogeneous samples. Sixty pupils in the eleventh grade were engaged in this investigation. The research subjects were chosen at random and divided into two groups: experimental and control. Descriptive and inferential statistical approaches were used to obtain the data. Prior to conducting the hypothesis test, the normality and homogeneity tests were undertaken, and the hypothesis was tested using a t-test. The study's findings indicate that there is a significant difference between using English songs and traditional methods taught by the teacher for eleventh-grade students, with a significance value of 0,000 less than 0,05 and a t-value greater than the t-table, or 7,421 greater than 2,002, indicating that the use of English songs as a teaching medium is sufficiently effective to increase learning results.
Library guides / Biblioteekgidse: Library Staff Training and Development Libguide: Research Support

In the Stellenbosch University Strategic Directions 2021-2025, goal 6 speaks of developing and sustaining competent, diverse and agile staff.
LibGuides: A LibGuide for Film Studies: Film Equipment

Welcome to the film studies LibGuide! If you are a film studies student or simply have an interest for film, this guide will help you out.
Memory-efficient Transformers via Top-k Attention.

Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length. While these variants are memory and compute efficient, it is not possible to directly use them with popular pre-trained language models trained using vanilla attention, without an expensive corrective pre-training stage. In this work, we propose a simple yet highly accurate approximation for vanilla attention. We process the queries in chunks, and for each query, compute the top-*k* scores with respect to the keys. Our approach offers several advantages: (a) its memory usage is linear in the input size, similar to linear attention variants, such as Performer and RFA (b) it is a drop-in replacement for vanilla attention that does not require any corrective pre-training, and (c) it can also lead to significant memory savings in the feed-forward layers after casting them into the familiar query-key-value framework. We evaluate the quality of top-*k* approximation for multi-head attention layers on the Long Range Arena Benchmark, and for feed-forward layers of T5 and UnifiedQA on multiple QA datasets. We show our approach leads to accuracy that is nearly-identical to vanilla attention in multiple setups including training from scratch, fine-tuning, and zero-shot inference.
See the Future through Art Museum Education in the Post-COVID 19 Era Focused on the Discovering the Identity of Community-based by Suwon Museum of Art

본 연구는 지역 미술관에서의 문화 자원을 활용한 지역기반 미술관 교육 전시·교육프로젝트 사례 연구를 통해 미술관의 정체 성을 발견하고 미술관 교육 경험의 중요성을 재인식하고자 했다. 이에 따라 수원시립미술관에서 운영되고 있는 교육관련 전시와 교육 프로젝트의 최근 여러 사례 중 지역기반 문화적 연관성이 높은 창의 워크숍 <49일 동안 예술가로 사는 법>과 문화예술 프로젝트 <거대한 안녕> 전시를 사례연구 대상으로 선정하였으며 연구 결과를 바탕으로 미술관 교육 경험을 통한 교육적 시사점을 발견하고자 하였다. 팬데믹 시대의 사회적 변화, 지속성, 장소 특정적 요소는 지역기반 미술관의 정체성을 발견하기 위한 요소로서 작용하며 미술관 교육에서의 경험은 개인의 자아 형성에 긍정적인 영향을 미칠 수 있을 것으로 기대한다. 지역기반 미술관 교육 사례, 지역기반 미술관의 정체성 발견을 위한 요소의 적용 사례를 통해 미술관 교육 경험의 중요성을 발견하는 것은 미래 교육의 가치를 전망해 볼 수 있는 계기가 될 것이다.The purpose of this study is to discover the identity of art museums and to recognize the importance of the experience of art museum education through case studies of community-based museum education and educational project using cultural resources. Accordingly, among recent examples of education-related exhibitions and educational projects run by Suwon Museum of Art, the creative workshop called “How to Live as an Artist for 49 Days” and the cultural and artistic project called “Huge Well-being” were selected as case studies in order to find the educational implication through art museum educational experience. In addition, social changes, persistence, and location factors in the Pandemic era are expected to play a positive role in discovering the identity of community-based art museums. Finding the importance of the experience of art museum education through community-based art museum education cases and application of factors that is to discover the identity of community-based art museums will be an opportunity to predict the value of future education.
THE EFFECTIVENESS OF CONDITIONAL CHAIN GAMES INTEACHING ENGLISH VOCABULARY OF THE SEVENTH GRADERSAT MTSN 7 TULUNGAGUNG

ABSTRACTHusna, Farisa Qurrotul. 2020. The Effectiveness of Conditional Chain Games inTeaching English Vocabulary of Seventh Graders at Mtsn 7 Tulungagung.Thesis. English Education department. Faculty of Tarbiyah and TeacherTraining. State Islamic Institut (IAIN) of Tulungagung.Advisor: Dr. Erna Iftanti, S.S., M.Pd.Keywords: Conditional chain game, vocabulary.Self-confidence, suggestion about English is a tough language to belearn, worry or frightened with foreign language becomes some aspectswhich can influence of the successfulness of the students in learning alanguage. When the students have a high anxiety, it can be a barrier for themin increasing their knowledge especially in learning English. In student‟sage of junior high school, it cannot be a part from the fact that they reallyneed happiness beside their seriousness to study. At this situation, applyinggames is intended to reduce student anxiety in learning English process.Research problem formulation in this study was: Is Conditional ChainGames effective for teaching English vocabulary of the seventh graders atMTsN 7 Tulungagung? this study purpose base on the research problem wasto find out the effectiveness of Conditional Chain Games in teachingEnglish vocabulary of the seventh graders at MTsN 7 Tulungagung.This study design is pre-experimental research study with one groupgiven pre-test - post-test. The research population was seventh gradestudents at MTsN 7 Tulungagung on academic year 2018/2019. Theresearch sample was VII-C class consist of 38 students chosen by purposivesampling. Additionally, the research instrument was test while the dataanalysis using t-test.This study finding showed about a significant difference scorebetween pre-test - post-test results. It proved by pre-test mean 60.42 whilepost-test was 77.19, or simply said that post-test mean higher than pre-testmean with the criteria of mastery learning 70, a difference mean both test is16.77. In addition, the result of statistical computation used paired sample ttest with SPSS proved that significance value smaller than significance level(0.000<0.05). In other words, alternative hypothesis (H1) stated there issignificant difference of the students‟ English vocabulary afterimplementing by Conditional Chain Game, accepted while the nullhypothesis (H0) rejected. So, Conditional Chain Game in teaching Englishvocabulary on the Seventh Graders at Mtsn 7 Tulungagung is effective.
Numerical Modeling of A New Virtual Trajectory Password Architecture

Abstract With the development of digital technology, Computer technology, communication technology and multimedia technology gradually infiltrate into each other and become the main core of information technology. In information technology, digital, text, graphics, images, sound, video, animation and other information carriers are spread through computers and the Internet. Due to the openness, sharing, dynamic and other characteristics of the Internet, information security is threatened and interfered. Information security has become a strategic issue that people must pay attention to, which is related to social stability, economic development and national security. This paper studies a new kind of virtual track cryptography, which breaks through the traditional character memory method and transforms it into memory graphics by using mathematical modeling, and changes the surface memory method from memorizing numbers and characters to memorizing trajectory graphics, so as to make the traditional process of people memorizing passwords easier.