Reviewerly/exHarmony huggingface dataset:
THE EFFECTIVENESS OF USING INSTAGRAM APPLICATION IN TEACHING WRITING RECOUNT TEXT OF SMPN 1 TULUNGAGUNG

ABSTRACT Adytia, Peni. Student Registered Number 17203163058. 2019. The Effectiveness of Using Instagram Application in Teachimg Writing Recount Text at SMP Negeri 1 Tulungagung. Thesis. English Department. Faculty of Tarbiyah and Teacher Training State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. Sukarsono, M.Pd.Keyword: Teaching writing, Recount Text, and Instagram ApplicationTeaching writing is used to teach students in order to produce text correctly. Many phenomena showed that students find difficulties to learn a foreign language especially in the writing process. The students’ difficulty is to find out the ideas to write and could not organize their writing well. Therefore, this study investigated whether the use of instagram is effective or not to teach writing recount text at the eight grade students of SMP Negeri 1 Tulungagung academic year of 2019/2020.The formulation of research problems in this study : (1) how is the students’ achievement in writing recount text before being taught by using instagram application? (2) how is the students’ achievement in writing recount text after being taught by using instagram application? (3) is there any significant difference of the students’ achievement in writing recount text between before and after being taught by using instagram application? Based on the formulation of the research problem, the purpose of this research was to investigate students’ achievement before and after being taught by using instagram application and find out if there is any significant difference achievement of study between before and after being taught by using instagram application in order to know is it effective or not to be the media in teaching writing recount text. This research used quantitative approach with experimental method. The researcher used pre-experimental design. The population of this study was all of the eight grade students at SMP Negeri 1 Tulungagung that consist of 448 students. The sample of this study used purposive sampling and got 8A Class that consist of 37 students. While the research instrument used was test. The stages carried out by the researcher was pretest, treatment and posttest. The researcher analyze the data by using Paired Sample T-test with SPSS 16.0.The results of this study showed that there is a significant difference between the results of the pre-test and post-test. The average score at the post-test 21.57 was higher compared to the pre-test 14.51. from the results of the statistical calculations, the results obtained from the value of Sign. (2-tailed) was 0.000 and it can be concluded that (Ha) accepted and the null hypothesis (H0) was rejected. The use of instagram application in teaching writing recount text is effective and can be used as an alternative media to teach writing recount text at SMP Negeri 1 Tulungagung.
Transformer for Polyp Detection

In recent years, as the Transformer has performed increasingly well on NLP tasks, many researchers have ported the Transformer structure to vision tasks ,bridging the gap between NLP and CV tasks. In this work, we evaluate some deep learning network for the detection track. Because the ground truth is mask, so we can try both the current detection and segmentation method. We select the DETR as our baseline through experiment. Besides, we modify the train strategy to fit the dataset.
3rd Place Scheme on Instance Segmentation Track of ICCV 2021 VIPriors Challenges

In this paper, we introduce a data-efficient instance segmentation method we used in the 2021 VIPriors Instance Segmentation Challenge. Our solution is a modified version of Swin Transformer, based on the mmdetection which is a powerful toolbox. To solve the problem of lack of data, we utilize data augmentation including random flip and multiscale training to train our model. During inference, multiscale fusion is used to boost the performance. We only use a single GPU during the whole training and testing stages. In the end, our team achieved the result of 0.366 for AP@0.50:0.95 on the test set, which is competitive with other top-ranking methods while only one GPU is used. Besides, our method achieved the AP@0.50:0.95 (medium) of 0.592, which ranks second among all contestants. In the end, our team ranked third among all the contestants, as announced by the organizers.
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution

Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Resampling and re-weighting are common approaches used for addressing the class imbalance problem, however, they are not effective when there is label dependency besides class imbalance because they result in oversampling of common labels. Here, we introduce the application of balancing loss functions for multi-label text classification. We perform experiments on a general domain dataset with 90 labels (Reuters-21578) and a domain-specific dataset from PubMed with 18211 labels. We find that a distribution-balanced loss function, which inherently addresses both the class imbalance and label linkage problems, outperforms commonly used loss functions. Distribution balancing methods have been successfully used in the image recognition field. Here, we show their effectiveness in natural language processing. Source code is available at https://github.com/Roche/BalancedLossNLP.
Meta Cross-Modal Hashing on Long-Tailed Data

Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor search of multi-modal data. Most hashing methods assume that training data is class-balanced.However, in practice, real world data often have a long-tailed distribution. In this paper, we introduce a meta-learning based cross-modal hashing method (MetaCMH) to handle long-tailed data. Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and then introduces an associative memory module to learn the memory features of samples of the tail classes. It then combines the direct and memory features to obtain meta features for each sample. For samples of the head classes of the long tail distribution, the weight of the direct features is larger, because there are enough training data to learn them well; while for rare classes, the weight of the memory features is larger. Finally, MetaCMH uses a likelihood loss function to preserve the similarity in different modalities and learns hash functions in an end-to-end fashion. Experiments on long-tailed datasets show that MetaCMH performs significantly better than state-of-the-art methods, especially on the tail classes.
An Analysis of Electronic Voting in Belgium

Given the varying usability of paper voting and electronic voting (e-voting), this chapter assesses the impact of e-voting on voting behavior by focusing on three distinct phenomena: turnout, invalid voting and split-ticket voting. E-voting is still a recent phenomenon and remains limited to a small number of countries (mostly Brazil, India and the United States), where it often remains peripheral (for instance in Canada, France, Japan or Peru). Building on the uniqueness of the Belgian e-voting system, the chapter relies on a quasi-experimental research design. Patterns of voting behavior have been empirically studied by comparing national legislative election results in cantons using e-voting with cantons voting with paper ballots in two Belgian provinces for the period 1995–2019. Differences in party vote shares may not only be the consequence of the cantons’ sociodemographic features but also on the (higher) incentive for voters to express split-ticket votes in cantons using paper voting.
Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e.g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest. As camouflaged objects often present very ambiguous boundaries, how to determine object locations as well as their weak boundaries is challenging and also the key to this task. Inspired by the biological visual perception process when a human observer discovers camouflaged objects, this paper proposes a novel edge-based reversible re-calibration network called ERRNet. Our model is characterized by two innovative designs, namely Selective Edge Aggregation (SEA) and Reversible Re-calibration Unit (RRU), which aim to model the visual perception behaviour and achieve effective edge prior and cross-comparison between potential camouflaged regions and background. More importantly, RRU incorporates diverse priors with more comprehensive information comparing to existing COD models. Experimental results show that ERRNet outperforms existing cutting-edge baselines on three COD datasets and five medical image segmentation datasets. Especially, compared with the existing top-1 model SINet, ERRNet significantly improves the performance by $\sim$6% (mean E-measure) with notably high speed (79.3 FPS), showing that ERRNet could be a general and robust solution for the COD task.
TransMix: Attend to Mix for Vision Transformers

Mixup-based augmentation has been found to be effective for generalizing models during training, especially for Vision Transformers (ViTs) since they can easily overfit. However, previous mixup-based methods have an underlying prior knowledge that the linearly interpolated ratio of targets should be kept the same as the ratio proposed in input interpolation. This may lead to a strange phenomenon that sometimes there is no valid object in the mixed image due to the random process in augmentation but there is still response in the label space. To bridge such gap between the input and label spaces, we propose TransMix, which mixes labels based on the attention maps of Vision Transformers. The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map. TransMix is embarrassingly simple and can be implemented in just a few lines of code without introducing any extra parameters and FLOPs to ViT-based models. Experimental results show that our method can consistently improve various ViT-based models at scales on ImageNet classification. After pre-trained with TransMix on ImageNet, the ViT-based models also demonstrate better transferability to semantic segmentation, object detection and instance segmentation. TransMix also exhibits to be more robust when evaluating on 4 different benchmarks. Code will be made publicly available at https://github.com/Beckschen/TransMix.
ConRPG: Paraphrase Generation using Contexts as Regularizer

A long-standing issue with paraphrase generation is how to obtain reliable supervision signals. In this paper, we propose an unsupervised paradigm for paraphrase generation based on the assumption that the probabilities of generating two sentences with the same meaning given the same context should be the same. Inspired by this fundamental idea, we propose a pipelined system which consists of paraphrase candidate generation based on contextual language models, candidate filtering using scoring functions, and paraphrase model training based on the selected candidates. The proposed paradigm offers merits over existing paraphrase generation methods: (1) using the context regularizer on meanings, the model is able to generate massive amounts of high-quality paraphrase pairs; and (2) using human-interpretable scoring functions to select paraphrase pairs from candidates, the proposed framework provides a channel for developers to intervene with the data generation process, leading to a more controllable model. Experimental results across different tasks and datasets demonstrate that the effectiveness of the proposed model in both supervised and unsupervised setups.
Marsel Proust, V. Domontovych and Yaroslav Ivashkevych: Music that Generates Love

The article describes the interaction of national literatures on the material of two volumes of the epic “In Search of Lost Time” by Marcel Proust, the novel “Without Foundation” by V. Domontovich, and the story “Dreams” by Yaroslav Ivashkevich. The object of study is the motif of a musical work associated with love lines in the analyzed texts. Within the methodological framework of comparative studies, theories of intertextuality, intermediality, and biographical method, the study attempts to explain how Marcel Proust depicts the influence of music on the state and feelings of a hero; how a musical motif from a Proust novel was borrowed by V. Domontovych and Y. Ivashkevich, modified, and introduced into their literary texts. In Proust’s novel the plot element is a phrase of the composer Vinteuil. The music phrase gives birth to Swann’s love for Odette without his will. At the structural level, the mark of music is imposed on a woman’s sign, and in the character’s consciousness, these signs cannot be distinguished. In the novel “Without Foundation,” V. Domontovych introduces Karol Shimanovsky’s Second Sonata, which becomes the reason for the emergence of love between Rostyslav Mykhailovych and Larysa Solska. The author uses Proust`s method of recollection by association and supplements it with synesthetic images. Y. Ivashkevych’s story “Dreams” is dedicated to the early Ukrainian period of the Polish writer’s life. The author enters into the text a reference to M. Proust. The center of the story is Richard Wagner’s song “Die Träume” and the story of its appearance, which becomes a factor in the plot of the work. In all these analyzed texts human sensitivity to music is also important as is the musical image itself. Research on the musical image in the works of M. Proust, V. Domontovych, and Y. Ivashkevych shows the powerful interaction between French, Ukrainian and Polish literatures and their thematic and figurative affinity.
WAB Learns: Teachers Edtech: MS-HS Remote Learning

Find out about WAB's tech systems, tools and subscriptions and how to use them A step by step guide for getting set up for remote learning in a hurty.
SCALE VALUE IN STUDYING THE SIZE OF PLACER GOLD

Placer gold particles are an integral part of terrigenous sediments. When studying the size of placer gold, it is advisable to use a logarithmic scale to explain the processes of gold accumulation and solve applied problems.
Object Visual Detection for Intelligent Vehicles

Traffic scene perception (TSP) aims to extract accurate real-time on-road environment information, which involves three phases detection of objects of interest, recognition of detected objects, and tracking of objects in motion. In this project, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning-based detection framework. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object sub categorization method as a means of capturing the intra class variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection.
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding

Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data. However, it’s unclear whether they learn similar representations or they can be effectively combined. In this paper, we show that TAPT and ST can be complementary with simple TFS protocol by following TAPT -> Finetuning -> Self-training (TFS) process. Experimental results show that TFS protocol can effectively utilize unlabeled data to achieve strong combined gains consistently across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. We investigate various semi-supervised settings and consistently show that gains from TAPT and ST can be strongly additive by following TFS procedure. We hope that TFS could serve as an important semi-supervised baseline for future NLP studies.
Decision-Focused Summarization

Relevance in summarization is typically defined based on textual information alone, without incorporating insights about a particular decision. As a result, to support risk analysis of pancreatic cancer, summaries of medical notes may include irrelevant information such as a knee injury. We propose a novel problem, decision-focused summarization, where the goal is to summarize relevant information for a decision. We leverage a predictive model that makes the decision based on the full text to provide valuable insights on how a decision can be inferred from text. To build a summary, we then select representative sentences that lead to similar model decisions as using the full text while accounting for textual non-redundancy. To evaluate our method (DecSum), we build a testbed where the task is to summarize the first ten reviews of a restaurant in support of predicting its future rating on Yelp. DecSum substantially outperforms text-only summarization methods and model-based explanation methods in decision faithfulness and representativeness. We further demonstrate that DecSum is the only method that enables humans to outperform random chance in predicting which restaurant will be better rated in the future.
Programming with union, intersection, and negation types

In this essay, I present the advantages and, I dare say, the beauty of programming in a language with set-theoretic types, that is, types that include union, intersection, and negation type connectives. I show by several examples how set-theoretic types are necessary to type some common programming patterns, but also how they play a key role in typing several language constructs-from branching and pattern matching to function overloading and type-cases-very precisely. I start by presenting the theory of types known as semantic subtyping and extend it to include polymorphic types. Next, I discuss the design of languages that use these types. I start by defining a theoretical framework that covers all the examples given in the first part of the presentation. Since the system of the framework cannot be effectively implemented, I then describe three effective restrictions of this system: (i) a polymorphic language with explicitly-typed functions, (ii) an implicitly-typed polymorphic language \`a la Hindley-Milner, and (iii) a monomorphic language that, by implementing classic union-elimination, precisely reconstructs intersection types for functions and implements a very general form of occurrence typing. I conclude the presentation with a short overview of other aspects of these languages, such as pattern matching, gradual typing, and denotational semantics.
Post-Training Sparsity-Aware Quantization

Quantization is a technique used in deep neural networks (DNNs) to increase execution performance and hardware efficiency. Uniform post-training quantization (PTQ) methods are common, since they can be implemented efficiently in hardware and do not require extensive hardware resources or a training set. Mapping FP32 models to INT8 using uniform PTQ yields models with negligible accuracy degradation; however, reducing precision below 8 bits with PTQ is challenging, as accuracy degradation becomes noticeable, due to the increase in quantization noise. In this paper, we propose a sparsity-aware quantization (SPARQ) method, in which the unstructured and dynamic activation sparsity is leveraged in different representation granularities. 4-bit quantization, for example, is employed by dynamically examining the bits of 8-bit values and choosing a window of 4 bits, while first skipping zero-value bits. Moreover, instead of quantizing activation-by-activation to 4 bits, we focus on pairs of 8-bit activations and examine whether one of the two is equal to zero. If one is equal to zero, the second can opportunistically use the other's 4-bit budget; if both do not equal zero, then each is dynamically quantized to 4 bits, as described. SPARQ achieves minor accuracy degradation and a practical hardware implementation. The code is available at https://github.com/gilshm/sparq.
How Blockchain Will Improve Private Ambulance Service Challenge

The ambulance service system for the patient's transport to the hospital is essential for the healthcare system. However, traffic congestion increases the urban population, a centralized management system, and increased demand for prehospital emergency services challenge the health system; there is a risk of lack of information sharing and information reliability, the consequences of which may directly and detrimentally affect patient well-being. This paper has built a blockchain-based ambulance model to solve these problems. The model can solve existing problems by decentralizing centralized ambulance system information and processes. All users in the emergency medical services (EMS) network can participate and use the service in the blockchain, using real-time state information such as patient location, ambulance, and ambulance attendants. To effectively implement blockchain in the medical sector, the ambulance business logic for a smart contract must reflect and confirm the operation of the defined functions suggested in this paper.
PENERAPAN COSINE SIMILARITY SEBAGAI METODE PENGUKURAN SIMILARITY INDEX PADA SISTEM PENGAJUAN JUDUL SKRIPSI STKIP PGRI SUMATERA BARAT

The process of submitting a thesis will pass the validation stage by the validator team. However, as the number of titles that have been submitted increases, it will be difficult for the validator to know whether the title has been submitted before or not. We need a system that can process information retrieval against the thesis title database and is able to calculate the degree of similarity of the title. In this study, a system development was carried out by applying the Cosine Similarity method to obtain the similarity value of the proposed title to the thesis title contained in the database. The result of this research is a system that can be used as a tool for information retrieval. The information generated from this process is the degree of similarity index of the title of thesis database, which becomes a tool in conducting the title of thesis validation process in STKIP PGRI West Sumatra