Reviewerly/exHarmony huggingface dataset:
EFEKTIVITAS MODEL PEMBELAJARAN DISCOVERY LEARNING TERHADAP KETERAMPILAN MENULIS PUISI KELAS IV SD NEGERI 81 PALEMBANG

Abstract: This research is motivated by the fact that the poetry writing skills of fourth grade students at SDN 81 Palembang are still low and students are less active in learning activities. The purpose of this study was to determine the effectiveness of the discovery learning model on poetry writing skills in fourth grade students of SDN 81 Palembang. The method used in this research is a pre experimental design with a one group pretest-posttest design. The pretest results obtained an average of 66.5 with the lowest score of 40 and the highest score of 85. The posttest results obtained an average score of 79.3 with the lowest score of 50 and the highest score of 95. The sample used in the study was class IV F with a total of 30 students. . Data collection techniques used in the research are tests and documentation. The data analysis technique used in the study is normality test, homogeneity test and hypothesis testing with a significant level of 5% using Microsoft Excel. Based on the results of the t-test calculation, the thing obtained is 3,947 and the table is 2,001. The criteria for testing the hypothesis are counting labels, then Ho is rejected and Ha is accepted. so it can be concluded that the discovery learning model is effective on the poetry writing skills of fourth grade students of SD Negeri 81 Palembang.Keywords: Discovery Learning, Writing PoetryAbstrak: Penelitian ini dilatarbelakangi fakta bahwa keterampilan menulis puisi pada siswa kelas IV SDN 81 Palembang masih rendah dan siswa kurang aktif dalam kegiatan pembelajaran. Tujuan penelitian ini adalah untuk mengetahui ada tidaknya efektivitas model pembelajaran discovery learning terhadap keterampilan menulis puisi pada siswa kelas IV SDN 81 Palembang. Metode yang digunakan dalam penelitian ini adalah pre experimental design dengan desain one group pretestposttest design. Hasil pretest diperoleh rata-rata 66,5 dengan nilai terendah 40 dan nilai tertinggi 85. Hasil posttest diperoleh rata-rata nilai 79,3 dengan nilai terendah 50 dan nilai tertinggi 95. Sampel yang digunakan dalam penelitian adalah kelas IV F dengan jumlah 30 siswa. Teknik pengumpulan data yang digunakan dalam penelitian yaitu tes dan dokumentasi. Teknik analisis data yang digunakan dalam penelitian yaitu uji normalitas, uji homogenitas dan uji hipotesis dengan taraf signifikan 5% menggunakan microsoft excel. Berdasarkan hasil perhitungan uji-t diperoleh thitung sebesar 3,947 dan ttabel sebesar 2,001. Kriteria uji hipotesis yaitu thitung ≥ ttabel, maka Ho ditolak dan Ha diterima. sehingga disimpulkan bahwa model pembelajaran discovery learning efektif terhadap keterampilan menulis puisi siswa kelas IV SD Negeri 81 Palembang.Kata Kunci: Discovery Learning, Menulis Puisi
Application Of Fuzzy Logic In Color Detection Robot Arm Simulator

Robot is one of the important tools in the industry under certain conditions. The world of education itself has done a lot of research in the field of robotics. The robot arm is one of them, the existence of a robot arm is expected to provide a function or performance in accordance with the work of the human arm and has stronger strength. The robot arm that is built applies a fuzzy logic method that determines the color of the object and will be moved according to the predetermined color, the color consists of three colors, namely red, green and blue. Robots are designed to be flexible and easy to use. It has controllers that have to be reprogrammed at any time to suit their function, like the Arduino Uno, and actuators like servomotors that are low-voltage and easy. This is why the TowerPro SG90 Servo motor was chosen to build the robot arm manipulator.
Analisis Faktor-Faktor Pemanfaatan Teknologi Informasi dan Dampaknya Terhadap Keahlian End User Computing (Euc)

This study aims to determine the factors that influence IT utilization and its impact on end user computing (EUC). There are 2 categories of end users, namely ordinary end users (termed End users) and skilled end users (end user computing). End-users are people who interact with computer-based information systems only as consumers / users or people who need the results of software applications to carry out work, while End User Computing (EUC) is an end user who has expertise, so that he is able to solve computer problems appropriately. The importance of EUC in work activities is of concern to researchers. Some researchers use Information Technology (IT) utilization factors in measuring its impact on EUC. There are several IT utilization factors, including: Social Factors, Convenience Factors, Usability Factors and Long-term Consequences Factors. These 4 factors are used in this research. The research was conducted on second semester students of Management Study Program, Khairun University. Using multiple regression analysis. The result: there are 24% of students utilizing IT because of the convenience factor, although it is not significant. While social factors, usability factors and long-term consequences factors do not affect IT utilization. This explains that second semester students have not utilized Information Technology significantly, so there is no impact on EUC.&#x0D;
Pengaruh Model Pembelajaran Flipped Classroom Di Masa Pandemi Covid 19 Terhadap Hasil Belajar IPA Sekolah Dasar

Abstract. The purposes of this study are to determine whether there is an effect of the flipped classroom learning model during the covid 19 pandemic on the science learning outcomes of fifth grade students at MI Al Abror Pekauman and to determine how much influence the flipped classroom learning model during the covid 19 pendemic has on the science learning outcomes of fifth grade students at MI Al Abror Pekauman . This research uses experimental quantitative research using the Pre Experimental – One Group Pretest Posttest Design. The results showed that there was a positive and significant effect of the flipped classroom learning model on the science learning outcomes of fifth grade students of MI Al Abror Pekauman, as evidenced by the value of 7,184 &gt; 2.064 and the magnitude of the effect can be seen from Cohen's d calculations which show the results of 1,44 &gt; 0.8. The conclusion of this study is that there is a large positive and significant effect between the flipped classroom learning model on the science learning outcomes of fifth grade students at MI Al Abror Pekauman.Keywords: Covid 19 Pandemic, Flipped Classroom Learning Model, Science Learning OutcomesAbstrak. Penelitian ini bertujuan untuk mengetahui pengaruh model pembelajaran flipped classroom di masa pandemi covid 19 terhadap hasil belajar IPA siswa kelas V MI Al Abror Pekauman dan untuk mengetahui seberapa besar pengaruh model pembelajaran flipped classroom di masa pandemi covid 19 terhadap hasil belajar IPA kelas V MI Al Abror Pekauman. Penelitian ini menggunakan jenis penelitian kuantitatif eksperimen dengan menggunakan desain penelitian pre experimental – one group pretest posttest design. Hasil penelitian ini menunjukkan terdapat pengaruh positif dan signifikan model pembelajaran flipped classroom terhadap hasil belajar IPA siswa kelas V MI Al Abror Pekauman, dibuktikan dengan nilai thit 7,184 &gt; ttab 2,064 dan besarnya pengaruh dapat dilihat dari perhitungan Cohen’s d yang menunjukan hasil 1,44 &gt; 0,8. Kesimpulan penelitian ini adalah terdapat pengaruh positif dan signifikan yang besar antara model pembelajaran flipped classroom terhadap hasil belajar IPA siswa kelas V MI Al Abror Pekauman.Kata Kunci: Pandemi Covid 19, Model Pembelajaran Flipped Classroom, Hasil Belajar IPA
XLDA: Linear Discriminant Analysis for Scaling Continual Learning to Extreme Classification at the Edge

Streaming Linear Discriminant Analysis (LDA) while proven in Class-incremental Learning deployments at the edge with limited classes (upto 1000), has not been proven for deployment in extreme classification scenarios. In this paper, we present: (a) XLDA, a framework for Class-IL in edge deployment where LDA classifier is proven to be equivalent to FC layer including in extreme classification scenarios, and (b) optimizations to enable XLDA-based training and inference for edge deployment where there is a constraint on available compute resources. We show up to 42x speed up using a batched training approach and up to 5x inference speedup with nearest neighbor search on extreme datasets like AliProducts (50k classes) and Google Landmarks V2 (81k classes)
"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement

Tidying up a messy table may appear simple for humans, but articulating clear criteria for tidiness is challenging due to the ambiguous nature of common sense reasoning. Large Language Models (LLMs) have proven capable of capturing common sense knowledge to reason over this vague concept of tidiness. However, they alone may struggle with table tidying due to the limited grasp on the spatio-visual aspects of tidiness. In this work, we aim to ground the common-sense concept of tidiness within the context of object arrangement. Our survey reveals that humans usually factorize tidiness into semantic and visual-spatial tidiness; our grounding approach aligns with this decomposition. We connect a language-based policy generator with an image-based tidiness score function: the policy generator utilizes the LLM's commonsense knowledge to cluster objects by their implicit types and functionalities for semantic tidiness; meanwhile, the tidiness score function assesses the visual-spatial relations of the object to achieve visual-spatial tidiness. Our tidiness score is trained using synthetic data generated cheaply from customized random walks, which inherently encode the order of tidiness, thereby bypassing the need for labor-intensive human demonstrations. The simulated experiment shows that our approach successfully generates tidy arrangements, predominately in 2D, with potential for 3D stacking, for tables with various novel objects.
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication

Handwriting authentication is a valuable tool used in various fields, such as fraud prevention and cultural heritage protection. However, it remains a challenging task due to the complex features, severe damage, and lack of supervision. In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues. It can dynamically learn complex yet important features and accurately predict writer identities. Specifically, to remove the negative effects of imperfections and redundancy, we design an information-theoretic filter for pre-processing and propose a novel adaptive matching scheme to represent images as patches of local regions dominated by more important features. Through online optimization at inference time, the most informative patch embeddings are identified as the "most important" elements. Furthermore, we employ contrastive self-supervised training with a momentum-based paradigm to learn more general statistical structures of handwritten data without supervision. We conduct extensive experiments on five benchmark datasets and our manually annotated dataset EN-HA, which demonstrate the superiority of our CSSL-RHA compared to baselines. Additionally, we show that our proposed model can still effectively achieve authentication even under abnormal circumstances, such as data falsification and corruption.
Towards General Game Representations: Decomposing Games Pixels into Content and Style

On-screen game footage contains rich contextual information that players process when playing and experiencing a game. Learning pixel representations of games can benefit artificial intelligence across several downstream tasks including game-playing agents, procedural content generation, and player modelling. The generalizability of these methods, however, remains a challenge, as learned representations should ideally be shared across games with similar game mechanics. This could allow, for instance, game-playing agents trained on one game to perform well in similar games with no re-training. This paper explores how generalizable pre-trained computer vision encoders can be for such tasks, by decomposing the latent space into content embeddings and style embeddings. The goal is to minimize the domain gap between games of the same genre when it comes to game content critical for downstream tasks, and ignore differences in graphical style. We employ a pre-trained Vision Transformer encoder and a decomposition technique based on game genres to obtain separate content and style embeddings. Our findings show that the decomposed embeddings achieve style invariance across multiple games while still maintaining strong content extraction capabilities. We argue that the proposed decomposition of content and style offers better generalization capacities across game environments independently of the downstream task.
Floquetifying the Colour Code

Floquet codes are a recently discovered type of quantum error correction code. They can be thought of as generalising stabilizer codes and subsystem codes, by allowing the logical Pauli operators of the code to vary dynamically over time. In this work, we use the ZX-calculus to create new Floquet codes that are in a definable sense equivalent to known stabilizer codes. In particular, we find a Floquet code that is equivalent to the colour code, but has the advantage that all measurements required to implement it are of weight one or two. Notably, the qubits can even be laid out on a square lattice. This circumvents current difficulties with implementing the colour code fault-tolerantly, while preserving its advantages over other well-studied codes, and could furthermore allow one to benefit from extra features exclusive to Floquet codes. On a higher level, as in arXiv:2303.08829, this work shines a light on the relationship between 'static' stabilizer and subsystem codes and 'dynamic' Floquet codes; at first glance the latter seems a significant generalisation of the former, but in the case of the codes that we find here, the difference is essentially just a few basic ZX-diagram deformations.
Time-optimal multi-qubit gates: Complexity, efficient heuristic and gate-time bounds

Multi-qubit entangling interactions arise naturally in several quantum computing platforms and promise advantages over traditional two-qubit gates. In particular, a fixed multi-qubit Ising-type interaction together with single-qubit X-gates can be used to synthesize global ZZ-gates (GZZ gates). In this work, we first show that the synthesis of such quantum gates that are time-optimal is NP-hard. Second, we provide explicit constructions of special time-optimal multi-qubit gates. They have constant gate times and can be implemented with linearly many X-gate layers. Third, we develop a heuristic algorithm with polynomial runtime for synthesizing fast multi-qubit gates. Fourth, we derive lower and upper bounds on the optimal GZZ gate-time. Based on explicit constructions of GZZ gates and numerical studies, we conjecture that any GZZ gate can be executed in a time O(n) for n qubits. Our heuristic synthesis algorithm leads to GZZ gate-times with a similar scaling, which is optimal in this sense. We expect that our efficient synthesis of fast multi-qubit gates allows for faster and, hence, also more error-robust execution of quantum algorithms.
The Impact of Emotional Distress and Empathy on Knowledge Sharing in Entrepreneurial Communities

Are entrepreneurs in online communities more willing to respond to their peers who express emotional distress like sadness and anxiety? While emotional distress may arouse others’ empathy and promote prosocial behavior, it can signal a seeker’s high expectation of receiving helpful responses, which raises a provider’s emotional and cognitive costs. Building on the emotion as social information (EASI) model, we emphasize the role of a seeker’s expressed emotional distress in triggering a provider’s empathy as an affective reaction and a provider’s inference for cost-benefit analysis. We argue that online requests with more expressed emotional distress will likely receive fewer but above-average quality responses and that a provider’s empathy is the mediating mechanism that mitigates the cost concern. Furthermore, a provider’s tenure and a seeker’s reputation can moderate the impact of expressed emotional distress on the occurrence of knowledge sharing. We created a novel dataset comprising 8.3 million seeker–provider pairs from an online community of entrepreneurs and found full support for our hypotheses, using rare event logistic regressions and hierarchical OLS regressions. This study extends the EASI model to explain how and when negative emotions affect empathic help and enriches our understanding of online knowledge sharing among entrepreneurs.
Generalized End-to-End Loss for Forensic Speaker Verification

<p id="C1">In recent years, various speech embedding methods based on deep learning have been proposed and have shown better performance in speaker verification. Those new technologies will inevitably promote the development of forensic speaker verification. We propose a new forensic speaker verification method based on embeddings trained with loss function called generalized end-to-end (GE2E) loss. First, a long short-term memory (LSTM) based deep neural network (DNN) is trained as the embedding extractor, then the cosine similarity scores between embeddings from same speaker comparison pairs and different speaker comparison pairs are trained to represent within-speaker model and between-speaker model respectively, and finally, the cosine similarity scores between the questioned embeddings and enrolled embeddings are evaluated in the above two models to get the likelihood ratio (LR) value. On the subset of LibriSpeech, test-other-500, we achieve a new state of the art. Both all the same speaker comparison pairs and different speaker comparison pairs get correct results and can provide considerable strong evidence strength for courts.
Multi-criteria Decision Support System for Prioritizing Challenges of Internet of Things (IoT)

Abstract: The adoption and utilization of Internet of Things (IoT) technologies such as IoT Platforms, 5G, connected sensors, WAN, etc mostly come with numerous, seemingly complex and expensive challenges for institutions and organisations, particularly in developing countries as they work to fill their fair share of the global IoT market. Multi-criteria decision support systems (MCDSS) can be indispensable tools for evaluating and prioritizing these complex competing difficulties by multistakeholder decision makers. While eliminating conventional decision problems such as, human biases, time consumption, cost of decision, etc, MCDSS make it possible for companies to concentrate on solving challenges which their limited resources can cater for. This study presents an approach for design and implementation of a prototype MCDSS for prioritizing challenges that institutions and organizations face while adopting and utilizing IoT technologies using Fuzzy Analytic Hierarchy Process (FAHP) methodology. ASP.Net model View controller (MVC) framework and C Sharp programming Language was used for the GUI and the Logic development. The system's default IoT challenges and dataset were adapted from the works of A.K. Mohammadzadeh (Baseline dataset). The system usability test result obtained shows that the system is friendly and usable. The output weights and rating of the IoT technology adoption challenges/sub-challenges exhibit over 80% similarity when compared to the Baseline dataset. Further studies are recommended to make the system a
Role on a Rule: Strengthening Learning Continuity with Parental and Police Involvement in the New Normal

The study examined the effectiveness of the strategy in strengthening the Learning Continuity Plan (LCP) during the COVID-19 pandemic. The study utilized qualitative research design, employing in-depth interviews with at least 10 participants who were partners in assisting and guiding students during modular learning. Findings showed that the strategy was effective in strengthening the LCP and that it steered to the development of students’ comprehension and sense of responsibility during modular supervision. The results serve as reference point in creating school policies that will intensify collaboration among stakeholders. The results were analyzed through thematic analysis utilizing Participatory Action Research method.
Genetic Programming: A Tutorial Introduction

tutorial Open Access Share on Genetic Programming: A Tutorial Introduction Authors: Una-May O'Reilly ALFA Group MIT CSAIL, Boston, USA ALFA Group MIT CSAIL, Boston, USA https://orcid.org/0000-0001-6923-8445View Profile , Erik Hemberg ALFA Group MIT CSAIL, Boston, USA ALFA Group MIT CSAIL, Boston, USA https://orcid.org/0000-0002-2153-3506View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1026–1036https://doi.org/10.1145/3583133.3595061Published:24 July 2023Publication History 0citation28DownloadsMetricsTotal Citations0Total Downloads28Last 12 Months28Last 6 weeks22 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Light-weight recurrent network for real-time video super-resolution

Real-time video super-resolution (VSR) has been considered a promising solution to improving video quality for video conferencing and media video playing, which requires low latency and short inference time. Although state-of-the-art VSR methods have been proposed with well-designed architectures, many of them are not feasible to be transformed into a real-time VSR model because of vast computation complexity and memory occupation. In this work, we propose a light-weight recurrent network for this task, where motion compensation offset is estimated by an optical flow estimation network, features extracted from the previous high-resolution output are aligned to the current target frame, and a hidden space is utilized to propagate long-term information. We show that the proposed method is efficient in real-time video super-resolution. We also carefully study the effectiveness of the existence of an optical flow estimation module in a lightweight recurrent VSR model and compare two ways of training the models. We further compare four different motion estimation networks that have been used in light-weight VSR approaches and demonstrate the importance of reducing information loss in motion estimation.