Reviewerly/exHarmony huggingface dataset:
Penerapan Model Pembelajaran Cooperative Script dalam Memahami Materi Teks Fabel

This study aims to find out how students understand the material by using the cooperative script learning model in fable text material for class VII students of SMPN 3 Bangko Pusako. The sample in the study was 60 students of class VII using the Total Sampling Design technique used was a one-group pretest-posttest design. In this study there were two groups, namely the experimental group and the control group. Data collection techniques used tests. Data analysis techniques using the normality test, homogeneity test and t test. To test the hypothesis used t test, from the calculation results obtained a significant level of 0.005. It turns out that the significance value (2-tailed) is 0.000 &lt;0.05, so that H0 is rejected and H1 is accepted so that it can be concluded that there is significant influence and difference in the use of the cooperative script learning model in fable text material.
Pengaruh Persepsi Siswa Terhadap Metode Demonstrasi dengan Kemampuan Menulis Teks Prosedur Kompleks Hasil Belajar Siswa Kelas VII SMP Negeri 2 Taluk Kuantan Tahun Ajaran 2020/2021

This study examines the effect of students' perceptions on the implementation of the demonstration method with the ability to write complex procedure texts on the learning outcomes of seventh grade students of SMP Negeri 2 Teluk Kuantan. This study aims to describe, and analyze the data in detail and systematically, so that a real picture can be obtained about the effect of complex procedure text abilities on student learning outcomes of Class VII SMP Negeri 2 Teluk Kuantan. This research will analyze descriptive variable data from each research variable. The method used in this study is the correlational method. The approach used is a quantitative approach. The data collection technique used a questionnaire technique and a test technique. Based on the results of the study, it is known that the student's perception variable has a value of tcount &gt; ttable or 11,050 &gt; 1,663 and significant &lt; 0.05 or 0.000 &lt; 0.05 so that H0 is rejected and Ha is accepted. It was concluded that student perceptions had a significant effect on student learning outcomes for Class VII SMP Negeri 2 Teluk Kuantan. The results of this study are supported by the results of Listiani's (2014) research which shows that there is a positive and significant influence on students' perceptions of learning outcomes in writing using the demonstration method.
Analisis Psikologi Sastra Novel Selamat Tinggal karya Tere Liye

Literary psychology as a science that studies the human soul and personality associated with literary works because in literary works can be found various behaviors, dispositions and conflicts experienced by humans. With the above facts, the authors are interested in conducting aresearch entitled "Analysis of the Literary Psychology of Tere Liye's Novel Goodbye". Problems in the research: (1) What is the character's character in the study of literary psychology in Tere Liye's novel Selamat Datang? (2) What is the theme in Tere Liye's novel Selamat Malam? (3) How is the conflict of characters in the study of literary psychology in Tere Liye's novel Selamat Stay?. The theory used in literary psychology research refers to the theory of Mana Sikana (1986) and other supporting theories, namely, Burhan Nurgiyantoro (2013), Hamidy (2012). The approach used is a qualitative approach with the type of library research, and uses a descriptive method. Data collection techniques through hermeneutic techniques. The results of the study concluded that: (1) The character's character has 14 data, namely Sintong Stay: firm, patient, wise, diligent. Jess: Honestly. Inang (Mrs. Sintong): humorous, angry, Bulik Ningrum: compassionate. Paklik Maman: clever, cunning. Slamet: scared. Ucok: fine. Mawar Bright Bintang: bad and Mr. Dean is good. (2) 4 data themes, namely the central theme: bad deeds will be miserable and several complementary themes (3) character conflicts have 12 data, namely 2 internal conflicts that occurred in Sintong Stay and 10 external conflicts.
Psikologi Sastra dalam Naskah Drama Kekasihku Ingin Meraih Hujan dari Jendela Kamar dan Love Karya Ari Pahala Hutabarat dalam Kumpulan Naskah Drama Orang-Orang Setia Teater Satu

This research is motivated by several things, namely: (1) Seeing the many aspects of literary psychology in the collection of drama scripts from Ari Pahala Hutabarat's Theater One, (2) So far, research on drama scripts has not been done much. In fact, in UIR, especially FKIP, research on scripts using literary psychology theory is still rarely carried out and this is due to the tendency for people to understand and recognize a drama, always staging it compared to the drama script itself. The problem in this research is "What is the personality structure of the characters related to the id, ego, and superego in a collection of theatrical drama scripts by Ari Pahala Hutabarat?". The theory used to analyze this research is Sigmund Freud's theory. The approach used is a qualitative approach, this type of research is library research. The method used is descriptive method, the data analysis technique used is content analysis technique. The results showed that there were id, ego, and superego in a collection of drama scripts from Ari Pahala Hutabarat's Satu Karya Theater. The results of the most dominant literary psychology research is the ego personality structure while the id and superego personality structures have a balance in the character. Although there is a balance in the personality structure of the characters in the script, it does not guarantee that there will be no conflict. The conflicts that occur are in a state of war inside him, restless, restless, depressed and others. The psychology in the drama script Teater Satu shows that whatever we do in real life happens based on what we think so that when we think positively without realizing it helps humans to balance what we think with real life.
Pengembangan instrumen penilaian proyek pada materi menulis teks eksplanasi kelas XI di MAN 1 Pekanbaru

The research is entitled "Development of a project assessment instrument on the material of writing explanation text XI at MAN 1 Pekanbaru". Problem Formulation How is the Process of Developing Project Assessment Instruments on Explanatory Text Writing Materials for Class XI at MAN 1 Pekanbaru and How is the Quality of Project Assessment Instruments for Explanatory Text Writing Materials for Class XI at MAN 1 Pekanbaru?. The objectives of this study were to determine the process of developing a project assessment instrument for writing explanatory text for class XI at MAN 1 Pekanbaru and to know the quality of a project appraisal instrument for writing an explanatory text for class XI at MAN 1 Pekanbaru. This type of research is research and development (R&amp;D) or research and development that aims to produce a product that is used to assess student assignments that meet the criteria, are practical and effective. The development of the instrument uses a 4D model that converts into 3D. Sources of research data are Indonesian language subject educators at MAN 1 Pekanbaru and students of class XI ASC 2 (class XI Religion). The data collection technique used an instrument validation sheet to measure the validity, a teacher response questionnaire sheet to measure the practicality of the instrument, and an analysis of the results of students' project assignments to measure the effectiveness of the instrument. The data obtained were analyzed by using analytical techniques for the validity, practicality and effectiveness of the assessment instrument. The results of the study said that the instrument developed by the assessment instrument to measure the students' explanatory text writing skills had met the valid criteria with an average value of 97.03 which was in the very valid category with a value of 85.01 - 100.00%, the criteria for good practicability with an average of 84.61% which is in the very practical category with a value of 81-100%, and the effectiveness criterion is very effective with a percentage of 85.7%. Thus the project instrument developed is of high quality, practical and effective, so that this instrument can be used in learning activities to assess students' explanatory text writing projects.
Attentive Multi-Layer Perceptron for Non-autoregressive Generation

Autoregressive (AR) generation almost dominates sequence generation for its efficacy. Recently, non-autoregressive (NAR) generation gains increasing popularity for its efficiency and growing efficacy. However, its efficiency is still bottlenecked by quadratic complexity in sequence lengths, which is prohibitive for scaling to long sequence generation and few works have been done to mitigate this problem. In this paper, we propose a novel MLP variant, Attentive Multi-Layer Perceptron (AMLP), to produce a generation model with linear time and space complexity. Different from classic MLP with static and learnable projection matrices, AMLP leverages adaptive projections computed from inputs in an attentive mode. The sample-aware adaptive projections enable communications among tokens in a sequence, and model the measurement between the query and key space. Furthermore, we marry AMLP with popular NAR models, deriving a highly efficient NAR-AMLP architecture with linear time and space complexity. Empirical results show that such marriage architecture surpasses competitive efficient NAR models, by a significant margin on text-to-speech synthesis and machine translation. We also test AMLP’s self- and cross-attention ability separately with extensive ablation experiments, and find them comparable or even superior to the other efficient models. The efficiency analysis further shows that AMLP extremely reduces the memory cost against vanilla non-autoregressive models for long sequences.
Hierarchical Graph Contrastive Learning

Unsupervised graph representation learning with GNNs is critically important due to the difficulty of obtaining graph labels in many real applications. Graph contrastive learning (GCL), a recently popular method for unsupervised learning on graphs, has achieved great success on many tasks. However, existing graph-level GCL models generally focus on comparing the graph-level representation or node-level representation. The hierarchical structure property, which is ubiquitous in many real world graphs such as social networks and molecular graphs, is largely ignored. To bridge this gap, this paper proposes a novel hierarchical graph contrastive learning model named HIGCL. HIGCL uses a multi-layered architecture and contains two contrastive objectives, inner-contrasting and hierarchical-contrasting. The former conducts inner-scale contrastive learning to learn the flat structural features in each layer, while the latter focuses on performing cross-scale contrastive learning to capture the hierarchical features across layers. Extensive experiments are conducted on graph-level tasks to show the effectiveness of the proposed method.
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders Are Better Dense Retrievers

Pre-trained Transformers (e.g., BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-training tasks for further improving the quality of dense vectors. Although various novel and effective tasks have been proposed, their different input formats and learning objectives make them hard to be integrated for jointly improving the model performance. In this work, we aim to unify a variety of pre-training tasks into the bottlenecked masked autoencoder manner, and integrate them into a multi-task pre-trained model, namely MASTER. Concretely, MASTER utilizes a shared-encoder multi-decoder architecture that can construct a representation bottleneck to compress the abundant semantic information across tasks into dense vectors. Based on it, we integrate three types of representative pre-training tasks: corrupted passages recovering, related passages recovering and PLMs outputs recovering, to characterize the inner-passage information, inter-passage relations and PLMs knowledge. Extensive experiments have shown that our approach outperforms competitive dense retrieval methods. Our code and data are publicly released in https://github.com/microsoft/SimXNS .
Design and performance test of series underwater Savonius rotors with horizontal axis

&lt;span lang="EN-US"&gt;The energy of river water flow or irrigation canals can be utilized by using a horizontal axis Savonius turbine, which converts low-speed river flow into electrical power with good design. The design of this turbine needs to pay attention to several parameters, namely, the deflector angle, the number of blades, the diameter and thickness of the blades, and the diameter and thickness of the end plates. However, the problem usually encountered is imperfect turbine construction due to the large drag force that occurs so that the power generated is low. Based on this background, it is proposed to manufacture and test the performance of a series Savonius underwater rotors with a horizontal axis. The research results found that the generator voltage without load was 25.6 V when the turbine only rotated at 47.2 rpm, whereas when under load, the average power produced was 8.5 watts with an average turbine speed of 31 rpm. The highest efficiency value on the rotor is 86.73%, with a torque value of 3.36 at a turbine speed of 29.9 rpm. This indicates that the tool can generate large torque at low speeds.&lt;/span&gt;
Linear codes invariant under cyclic endomorphisms

In this paper, we study linear codes invariant under a cyclic endomorphism [Formula: see text] called [Formula: see text]-cyclic codes. Since every cyclic endomorphism can be represented by a cyclic matrix with respect to a given basis, all of these matrices are similar. For simplicity we restrict ourselves to linear codes invariant under the right multiplication by a cyclic matrix [Formula: see text] that we call [Formula: see text]-cyclic codes, and when [Formula: see text] is the companion matrix [Formula: see text] of a given nonzero polynomial [Formula: see text] we call them [Formula: see text]-cyclic codes. The similarity relation between matrices helps us find connections between [Formula: see text]-cyclic codes and [Formula: see text]-cyclic codes, where [Formula: see text] is the minimal polynomial of [Formula: see text] The class of [Formula: see text]-cyclic codes contains cyclic codes and their various generalizations such as constacyclic codes, right and left polycyclic codes, monomial codes, and others. As common in the study of cyclic codes and their generalizations, we make use of the one-to-one correspondence between [Formula: see text]-cyclic codes and ideals of the polynomial ring [Formula: see text] where [Formula: see text] is the minimal polynomial of [Formula: see text] This correspondence leads to some basic characterizations of these codes such as generator and parity check polynomials among others. Next, we study the duality of these codes, where we show that the [Formula: see text]-dual of an [Formula: see text]-cyclic code is an [Formula: see text]-cyclic code, where [Formula: see text] is the adjoint matrix of [Formula: see text] with respect to [Formula: see text] and we explore some important results on the duality of these codes. Finally, we give examples as applications of some of the results and we construct some optimal codes.
A biologically inspired architecture with switching units can learn to generalize across backgrounds

Humans and other animals navigate different environments effortlessly, their brains rapidly and accurately generalizing across contexts. Despite recent progress in deep learning, this flexibility remains a challenge for many artificial systems. Here, we show how a bio-inspired network motif can explicitly address this issue. We do this using a dataset of MNIST digits of varying transparency, set on one of two backgrounds of different statistics that define two contexts: a pixel-wise noise or a more naturalistic background from the CIFAR-10 dataset. After learning digit classification when both contexts are shown sequentially, we find that both shallow and deep networks have sharply decreased performance when returning to the first background - an instance of the catastrophic forgetting phenomenon known from continual learning. To overcome this, we propose the bottleneck-switching network or switching network for short. This is a bio-inspired architecture analogous to a well-studied network motif in the visual cortex, with additional "switching" units that are activated in the presence of a new background, assuming a priori a contextual signal to turn these units on or off. Intriguingly, only a few of these switching units are sufficient to enable the network to learn the new context without catastrophic forgetting through inhibition of redundant background features. Further, the bottleneck-switching network can generalize to novel contexts similar to contexts it has learned. Importantly, we find that - again as in the underlying biological network motif, recurrently connecting the switching units to network layers is advantageous for context generalization.
Accelerated Simulation of Non-Markovian Quantum Systems Using Quantum Trajectory

Quantum trajectory can be viewed as a numerical simulation method for wavefunctions which consume smaller memories compared to density matrices in master equations. In this paper, we adopt quantum trajectory to simulate the evolution of a non-Markovian quantum system modeled by an augmented system and further use parallel computing to improve efficiency. Compared to the Lindblad master equation solver in QuTiP, it can run more efficiently using less memory. In the example of a 8-spin-chain non-Markovian system, it only takes about 0.57% of the memory footprint and 4.12% of the runtime of the solver for the Lindblad master equation.
Accelerated Simulation of Master Equation for Open Quantum Systems

Simulation of open quantum systems is undoubtedly critical to design of quantum devices and applications of quantum technologies since dynamics of open quantum systems cannot be solved analytically in general. However, the dimension of open quantum systems grows exponentially as the number of their subsystems increases or as involving complicated environments, which reduces the computational efficiency of the simulation. In this paper, we boost numerical simulation of master equations of open quantum systems from several aspects with which the total time and the occupied memory in the simulation are significantly reduced without losing precision. Our method can be applied to the simulation of non-Markovian quantum systems described by an augmented system model as well. We conduct numerical experiments on simulation of a non-Markovian spin chain. Compared with the algorithm in a well-known toolbox, QuTiP, our method can take 26.3% time and less memory under fair conditions.
Ensemble Learning Based on Group Decision Making Using Projection Method

Ensemble learning (EL) is a machine learning paradigm which aims to improve the generalization performance of models. In this paper, EL based on group decision making (GDM) using projection method is proposed, which takes base learners as decision makers and regards combination of EL as the process of GDM. Specifically, after the base learners have been trained on the training set, their performance on the validation set will be taken as the prior information of learners. Next, several attributes are derived and calculated in the test set, which constitutes multiple attribute group decision making formulation. Then GDM based on projection method is applied to determine the weights of base learners. Finally, the integrated results are obtained and the highest is taken as predictive label. The experimental results in a real industrial dataset demonstrate the effectiveness and superiority of the proposed EL method by comparing with other typical EL methods.
Quantum generative adversarial learning in photonics

Quantum generative adversarial networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of noisy intermediate-scale quantum (NISQ) computing, it is essential to investigate whether QGANs can perform learning tasks on near-term quantum devices usually affected by noise and even defects. In this Letter, using a programmable silicon quantum photonic chip, we experimentally demonstrate the QGAN model in photonics for the first time to our knowledge and investigate the effects of noise and defects on its performance. Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90%, even under conditions where up to half of the generator's phase shifters are damaged, or all of the generator and discriminator's phase shifters are subjected to phase noise up to 0.04π. Our work sheds light on the feasibility of implementing QGANs on the NISQ-era quantum hardware.
Towards Resource-Efficient Edge AI: From Federated Learning to Semi-Supervised Model Personalization

A central question in edge intelligence is “how can an edge device learn its local model with limited data and constrained computing capacity?” In this study, we explore the approach where a global model initialization is first obtained by running federated learning (FL) across multiple edge devices, based on which a semi-supervised algorithm is devised for a single edge device to carry out quick adaptation with its local data. Specifically, to account for device heterogeneity and resource constraints, a global model is first trained via FL, where each device conducts multiple local updates only for its customized subnet. A subset of devices can be selected to upload updates for aggregation during each training round. Further, device scheduling is optimized to minimize the training loss of FL, subject to resource constraints, based on the carefully crafted reward function defined as the one-round progress of FL each device can provide. We examine the convergence behavior of FL for the general non-convex case. For semi-supervised model personalization, we use the FL-based model initialization as a teacher network to impute soft labels on unlabeled data, thereby addressing the insufficiency of labeled data. Experiments are conducted to evaluate the performance of the proposed algorithms.
Research on Text Summarization Techniques Based on Deep Learning-WS-Attention

The bonafide semantic understanding of human language text, exhibited by its effective summarization, may well be the holy grail of natural language processing (NLP). That statement isn't as hyperbolic as it sounds: as true human language understanding definitely is the holy grail of NLP, and genuine effective summarization of said human language would necessarily entail true understanding, transitivity would back me up on this. However, the show must go on, and there currently exist an array of actual techniques for summarizing text, some of which stretch back decades. These techniques take different approaches to reach the same goal, and can be classified into a fairly narrow set of categories for pursuing their shared goal.