Reviewerly/exHarmony huggingface dataset:
THE APPLICATION OF GUESSING STRATEGY TO IMPROVE STUDENTS’ VOCABULARY MASTERY ON ENGLISH PHRASAL VERBS

This Classroom Action Research (henceforth, CAR) is mainly aimed at proving that the application of Guessing strategy is able to improve the students’ vocabulary mastery on English phrasal verbs. This CAR was performed in two cycles in which each cycle consisted of three meetings excluded the pre-test, formative test, and post-test. The subjects of this CAR are the fourth semester students of English Language Education Study Program, Faculty of Teachers Training and Education, Catholic University of Saint Thomas in the Academic Year of 2019/2020. There are 20 students as the research subjects. The quantitative data analysis shows that the mean of the students’ score in the pre-test, formative test, and post-test increases significantly after Guessing strategy was applied in the learning teaching processes of English phrasal verbs. The students’ total score of the pre-test is 1.316 and the pre-test mean is 65,8. The students’ total score of the formative test is 1.384 and the formative test mean is 69,2. The students’ total score of the post-test is 1.600 and the post-test mean is 80. There are 8 (eight) students (40%) who attained score &gt;75 in the pre-test, 8 (eight) students (40%) who attained score &gt;75 in the formative test, and 15 (fifteen) students (75%) who attained score &gt;75 in the post-test. The qualitative data analysis shows that the students are more active and enthusiastic in learning and mastering English phrasal verbs after Guessing strategy was applied. Their responses towards the application of Guessing strategy in the learning teaching processes of English phrasal verbs are very positive. All of them agree that learning English phrasal verbs through the application of Guessing strategy can trigger their curiosity and improve their knowledge and vocabulary mastery on English phrasal verbs. The researchers recommend that English learners implement Guessing strategy to improve their vocabulary mastery on English phrasal verbs.&#x0D;
Transition from the Gutenberg Galaxy to the Zuckerberg Galaxy

The era of printed books originates from the German city of Mainz, where at the turn of the XIV-XV centuries. Johann Gutenberg was born (according to some scholars, not earlier than 1399, according to others - in 1400; died February 3, 1468). The idea of ​​book printing at that time was quite relevant, and there are different views on the first printers, but it was this German inventor who talentedly solved many technical problems and began mass book printing in Europe. Between 1452 and 1455 he published one of the most famous books of the time, the so-called 42-line Bible, which was called the Gutenberg Bible. It was one of the first incunabula - that's what the books published in the XV century are called, literally "lullabies", ie those that stood near the cradle of book printing.&#x0D; The era of electronic books in Europe began in the Ukrainian city of Kiev, where in 1950 Europe's first Small Electronic Computer ("MESM") was developed at the Institute of Electrical Engineering of the Academy of Sciences of Ukraine under the leadership of Academician S. Lebedev. Thus began the era of computers in Europe, which in half a century led to the global spread of new information technologies, as well as e-books (computers) and magazines.&#x0D;
Scientific and journalistic study of transformational processes of modern reading in the digital society

Senchenko M. From the Gutenberg era to the Zuckerberg era… Advantages and disadvantages of the perception of printed and electronic text / Mykola Senchenko, Oksana Senchenko. - Kyiv: Lira-K, 2021. - 128 p.
Perancangan Sistem Penjualan Furniture Pada UD Usaha Jaya Mandiri Berbasis Website

UD Usaha Jaya Mandiri is a shop in the City of Tanjung Pinang which is engaged in selling furniture. Seeing the progress of the development of Science and Technology today, the demands on the development of sales and existence in the company must be increased, one way is to create a website, because in this way the UD Usaha Jaya Mandiri company engaged in the sale of furniture can be better known by the public broad, and can increase sales at UD Usaha Jaya Mandiri Company. The method used to obtain data requirements is the method of observation and interview methods. The software system design method used is System Development Life Cycle (SDLC) and the programming language used to create programs is PHP and to manage the database it uses PhpMyAdmin. The results of this application program have a shopping cart feature which is a very useful feature, because it makes it easier for customers to order and make purchases in stores, and this application program can provide information on sales and order reports.
IMPLEMENTASI METODE AGGLOMORATIVE HIERARCHICAL CLUSTERING PADA WEBSITE PEMILIHAN TEMPAT FUTSAL STUDI KASUS KOTA DEPOK

This application was made for the selection of futsal fields in the city of Depok. This application was made using the Agglomorative Hierarchical Clustering method which aims to recommend criteria for rental rates, distances, canteen facilities, toilet facilities and changing room facilities. This application will provide the recommended criteria based on the distance between the customer and the futsal location. Futsal place data is taken from futsal places in Depok City. The results of the Agglomorative Hierarchical Clustering method are the criteria values placed on the futsal field information.
PEMBUATAN GAME ARCADE “CITY PROTECTOR” DENGAN MENGGUNAKAN TOUCH GESTURE RECOGNIZER

City Protector is an Arcade Tower Defense Game with endless element. This game implements touch gesture recognizer feature. This game is developed with Unity game engine and playable in Android-based smartphone. To kill zombies, players must draw the symbols contained in the explosives that brought by zombies. Testing is done by blackbox testing, alpha testing, and beta testing through a survey of 32 respondents. The results indicate that City Protector has interesting features and gameplay concepts. It is provides new experiences because the Touch Gesture Recognizer feature is rarely found in general.
EXPLORING THE SUBSTANTIAL ROLE OF MULTIPLEX PCR TO DIAGNOSE MALARIA IN EASTERN NEPAL

Background: Malaria is still a major health problem in Nepal as 43.3% of population are at risk of infection. However, the national priorities have been given to achieve malaria elimination in Nepal, the question of diag­nostic accuracy could jeopardize the elimination target. Among others, multiplex PCR could be the reliable alternative tool to detect the low num­ber of parasitemia and circulating asymptomatic infection in low endemic countries like Nepal. Hence, this paper aims to analyse the diagnostic pre­cision among microscopy, rapid diagnostic test (RDT) and multiplex real time PCR assay. &#x0D; Methods: A total of 63 archived blood samples obtained from patient with clinical suspicion of malaria were subjected to multiplex PCR and their re­sults were compared with microscopy and rapid diagnostic test. The La­tent Class Analysis (LCA) approach was used to compare the diagnostic precision among three different assays. &#x0D; Results: Our study documented that PCR had 97.4% sensitivity and 83.9% specificity. Among the negative microscopy results, 10 (15.9%) were false negatives whereas 4 (6.3%) RDT results were false negatives based on PCR results. &#x0D; Conclusions: PCR could be the useful alternative tool to detect the low number of parasite in low endemic settings which could be crucial to rule out the false diagnosis determined by microscopy or RDT.
Analysis of Virtual Water Consumption and Flow

Abstract：The rapid economic development and urbanization accelerating the water consumption.Water shortage and irrational water use cause a series of questions, such as the imbalance between supply and demand and destruction of natural flow of water resources.Meanwhile, these problems will impose a height pressure over the further development of the social economy.This paper utilized the input-output model to calculate the coefficients of direct water consumption as well as indirect water consumption to explore the virtual water flows in different sectors based on data of Jiuquan City in 2012.Results show that the agriculture sector, having the highest water consumption but creating little economic benefits, virtually flows to food and tobacco manufacturing sectors and service sectors, as the main export sectors.Food and tobacco manufacturing sectors have a lot contribution to the indirect boost of economic system by water resources with the highest indirect water consumption.Finally, we should energetically increase the water saving capacity of agriculture, readjust industrial structure and strengthen the cooperation of water allocation among all sectors in order to promote coordinated and sustainable development of water resources utilization and economy.
Synergy Optimization Analysis of Heat Transfer Process and Its Application in Data Centers

The power consumption of pumps and fans is quite high in the transport systems, especially in data centers.Nearly 60% of the computer room air conditioning (CRAC) system's energy consumption is consumed by pumps and fans in winter.In this paper, a synergy optimization method is proposed to save energy in heat transfer process.The heat transfer constraint equations of the heat transfer process are established, and taking the highest energy efficiency as the objective function, the synergistic relationship between the power consumption and temperature difference is derived analytically by using variation principle.A synergy operation factor is defined to guide the practical operation optimization of heat transfer process.The closer the synergy operation factor is to 1, the higher the system energy efficiency is.An experiment of a separated heat pipe system is carried out to verify the accuracy of the synergy optimization analysis.
Environmental Sampling with the Boustrophedon Decomposition Algorithm

The automation of data collection via mobile robots holds promise for increasing the efficacy of environmental investigations, but requires the system to autonomously determine how to sample the environment while avoiding obstacles. Existing methods such as the boustrophedon decomposition algorithm enable complete coverage of the environment to a specified resolution, yet in many cases sampling at the resolution of the distribution would yield long paths with an infeasible number of measurements. Downsampling these paths can result in feasible plans at the expense of distribution estimation accuracy. This work explores this tradeoff between distribution accuracy and path length for the boustrophedon decomposition algorithm. We quantify algorithm performance by computing metrics for accuracy and path length in a Monte-Carlo simulation across a distribution of environments. We highlight conditions where one objective should be prioritized over the other and propose a modification to the algorithm to improve its effectiveness by sampling more uniformly. These results demonstrate how intelligent deployment of the boustrophedon algorithm can effectively guide autonomous environmental sampling.
Cross Tensor Approximation for Image and Video Completion

This paper proposes a general framework to use the cross tensor approximation or tensor ColUmn-Row (CUR) approximation for reconstructing incomplete images and videos. The key importance of the new algorithms is their simplicity and ease of implementation with low computational complexity. For the case of data tensors with 1) structural missing components or 2) a high missing rate, we propose an efficient smooth tensor CUR algorithms which first make the sampled fibers smooth and then apply the proposed CUR algorithms. The numerical experiments show the significant benefit of this smoothing procedure. The main contribution of this paper is to develop/investigate improved multistage CUR algorithms with filtering (smoothing ) preprocessing for tensor completion. The second contribution is a detailed comparison of the performance of image recovery for four different CUR strategies via extensive computer simulations. Our simulations clearly indicated that the proposed algorithms are much faster than most of the existing state-of-the-art algorithms developed for tensor completion, while performance is comparable and often even better. Furthermore, we will provide in GitHub the MATLAB codes which can be used for various applications. Moreover, to our best knowledge, the CUR (cross approximation) algorithms have not been investigated nor compared till now for image and video completion.
Quantum state tomography with tensor train cross approximation

It has been recently shown that a state generated by a one-dimensional noisy quantum computer is well approximated by a matrix product operator with a finite bond dimension independent of the number of qubits. We show that full quantum state tomography can be performed for such a state with a minimal number of measurement settings using a method known as tensor train cross approximation. The method works for reconstructing full rank density matrices and only requires measuring local operators, which are routinely performed in state-of-art experimental quantum platforms. Our method requires exponentially fewer state copies than the best known tomography method for unstructured states and local measurements. The fidelity of our reconstructed state can be further improved via supervised machine learning, without demanding more experimental data. Scalable tomography is achieved if the full state can be reconstructed from local reductions.
Symmetry-Aware Transformer-based Mirror Detection

Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly focus on integrating the semantic features and structural features to mine specific relations between mirror and non-mirror regions, or introducing mirror properties like depth or chirality to help analyze the existence of mirrors. In this work, we observe that a real object typically forms a loose symmetry relationship with its corresponding reflection in the mirror, which is beneficial in distinguishing mirrors from real objects. Based on this observation, we propose a dual-path Symmetry-Aware Transformer-based mirror detection Network (SATNet), which includes two novel modules: Symmetry-Aware Attention Module (SAAM) and Contrast and Fusion Decoder Module (CFDM). Specifically, we first adopt a transformer backbone to model global information aggregation in images, extracting multi-scale features in two paths. We then feed the high-level dual-path features to SAAMs to capture the symmetry relations. Finally, we fuse the dual-path features and refine our prediction maps progressively with CFDMs to obtain the final mirror mask. Experimental results show that SATNet outperforms both RGB and RGB-D mirror detection methods on all available mirror detection datasets. Codes and trained models are available at: https://github.com/tyhuang0428/SATNet.
Efficient error and variance estimation for randomized matrix computations

Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid to compute for randomized low-rank approximation algorithms such as the randomized SVD and randomized Nystr\"om approximation, and they provide useful information that can be used to assess the quality of the computed output and guide algorithmic parameter choices.
Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach

Continual learning (CL) over non-stationary data streams remains one of the long-standing challenges in deep neural networks (DNNs) as they are prone to catastrophic forgetting. CL models can benefit from self-supervised pre-training as it enables learning more generalizable task-agnostic features. However, the effect of self-supervised pre-training diminishes as the length of task sequences increases. Furthermore, the domain shift between pre-training data distribution and the task distribution reduces the generalizability of the learned representations. To address these limitations, we propose Task Agnostic Representation Consolidation (TARC), a two-stage training paradigm for CL that intertwines task-agnostic and task-specific learning whereby self-supervised training is followed by supervised learning for each task. To further restrict the deviation from the learned representations in the self-supervised stage, we employ a task-agnostic auxiliary loss during the supervised stage. We show that our training paradigm can be easily added to memory- or regularization-based approaches and provides consistent performance gain across more challenging CL settings. We further show that it leads to more robust and well-calibrated models.
Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites

Consumers often read product reviews to inform their buying decision, as some consumers want to know a specific component of a product. However, because typical sentences on product reviews contain various details, users must identify sentences about components they want to know amongst the many reviews. Therefore, we aimed to develop a system that identifies and collects component and aspect information of products in sentences. Our BERT-based classifiers assign labels referring to components and aspects to sentences in reviews and extract sentences with comments on specific components and aspects. We determined proper labels based for the words identified through pattern matching from product reviews to create the training data. Because we could not use the words as labels, we carefully created labels covering the meanings of the words. However, the training data was imbalanced on component and aspect pairs. We introduced a data augmentation method using WordNet to reduce the bias. Our evaluation demonstrates that the system can determine labels for road bikes using pattern matching, covering more than 88\% of the indicators of components and aspects on e-commerce sites. Moreover, our data augmentation method can improve the-F1-measure on insufficient data from 0.66 to 0.76.
Scalable and Programmable Phononic Network with Trapped Ions

Controllable bosonic systems can provide post-classical computational power with sub-universal quantum computational capability. A network that consists of a number of bosons evolving through beam-splitters and phase-shifters between different modes, has been proposed and applied to demonstrate quantum advantages. While the network has been implemented mostly in optical systems with photons, recently alternative realizations have been explored, where major limitations in photonic systems such as photon loss, and probabilistic manipulation can be addressed. Phonons, the quantized excitations of vibrational modes, of trapped ions can be a promising candidate to realize the bosonic network. Here, we experimentally demonstrate a minimal-loss phononic network that can be programmed and in which any phononic states are deterministically prepared and detected. We realize the network with up to four collective-vibrational modes, which can be straightforwardly extended to reveal quantum advantage. We benchmark the performance of the network with an exemplary algorithm of tomography for arbitrary multi-mode states with a fixed total phonon number. We obtain reconstruction fidelities of 94.5 $\pm$ 1.95 % and 93.4 $\pm$ 3.15 % for single-phonon and two-phonon states, respectively. Our experiment demonstrates a clear and novel pathway to scale up a phononic network for various quantum information processing beyond the limitations of classical and other quantum systems.
Bootstrap Latent Representations for Multi-modal Recommendation

This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendation accuracy. Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e.g., user-user or item-item relation graph) to augment the learned representations of users and/or items. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. Specifically, BM3 first bootstraps latent contrastive views from the representations of users and items with a simple dropout augmentation. It then jointly optimizes three multi-modal objectives to learn the representations of users and items by reconstructing the user-item interaction graph and aligning modality features under both inter- and intra-modality perspectives. BM3 alleviates both the need for contrasting with negative examples and the complex graph augmentation from an additional target network for contrastive view generation. We show BM3 outperforms prior recommendation models on three datasets with number of nodes ranging from 20K to 200K, while achieving a 2-9X reduction in training time. Our code is available at https://github.com/enoche/BM3.
GriddlyJS: A Web IDE for Reinforcement Learning

Progress in reinforcement learning (RL) research is often driven by the design of new, challenging environments -- a costly undertaking requiring skills orthogonal to that of a typical machine learning researcher. The complexity of environment development has only increased with the rise of procedural-content generation (PCG) as the prevailing paradigm for producing varied environments capable of testing the robustness and generalization of RL agents. Moreover, existing environments often require complex build processes, making reproducing results difficult. To address these issues, we introduce GriddlyJS, a web-based Integrated Development Environment (IDE) based on the Griddly engine. GriddlyJS allows researchers to visually design and debug arbitrary, complex PCG grid-world environments using a convenient graphical interface, as well as visualize, evaluate, and record the performance of trained agent models. By connecting the RL workflow to the advanced functionality enabled by modern web standards, GriddlyJS allows publishing interactive agent-environment demos that reproduce experimental results directly to the web. To demonstrate the versatility of GriddlyJS, we use it to quickly develop a complex compositional puzzle-solving environment alongside arbitrary human-designed environment configurations and their solutions for use in automatic curriculum learning and offline RL. The GriddlyJS IDE is open source and freely available at https://griddly.ai.