Reviewerly/exHarmony huggingface dataset:
Peningkatan Prestasi Belajar Ekonomi Melalui Metode Pembelajaran Student Teams Achievement Devision Tahun Pelajaran 2019/2020

&#x0D; &#x0D; The purpose of this research is to identify whether there is an increase in economic learning achievement in class VIII SMP N 2 Kartasura in the use of the STAD (student teams achievement division) procedure. This type of research is experimental research that intends to equate something as a result of certain treatments. The population in this research is all grade VIII students of SMP Negeri 2 Kartasura for the academic year 2019/2020 which consists of 6 classes with a total of 240 students. On the other hand, the illustration in this research is 1 class (40 students) taken using the Simple Random Sampling method. The required information is obtained through the provision of explanatory tests. The test questions to be used were tested for validity and reliability. Besides that, documentation procedures are used to clarify information. The method of analysis uses normality test, homogeneity test, and makes hypothesis testing with mean difference test (t test). From the results of the analysis it can be concluded that: STAD cooperative education can improve student achievement in class VIII SMP Negeri 2 Kartasura in the 2009/2010 academic year. before the experiment (66, 675) or there is a difference of 14, 4. It means that STAD cooperative education is better (more efficient) in improving student achievement.&#x0D; Keywords: STAD (Student Teams Achievement Division), economics learning achievement
A Novel Consensus Algorithm Based on Segmented DAG and BP Neural Network for Consortium Blockchain

Currently, because of the excellent properties of decentralization, hard tamperability, and traceability, blockchain is widely used in WSN and IoT applications. In particular, consortium blockchain plays a fundamental role in the practical application environment, but consensus algorithm is always a key constraint. Over the past decade, we have been witnessing the obvious growth in blockchain consensus algorithms. However, in the existing consortium blockchain consensus algorithms, there is a limited characteristic of scalability, concurrency, and security. To address this problem, this work introduces a new consensus algorithm that is derived from a directed acyclic graph and backpropagation neural network. First, we propose a partitioned structure and segmented directed acyclic graph as data storage structure, which allows us to improve scalability, throughput, and fine-grained granularity of transaction data. Furthermore, in order to provide the accuracy of node credit evaluation and reduce the possibility of Byzantine nodes, we introduce a novel credit evaluation mechanism based on a backpropagation neural network. Finally, we design a resistant double-spending mechanism based on MapReduce, which ensures the transaction data are globally unique and ordered. Experimental results and security analysis demonstrate that the proposed algorithm has advantages in throughput. Compared with the existing methods, it has higher security and scalability.
Fast quantum state discrimination with nonlinear PTP channels

We investigate models of nonlinear quantum computation based on deterministic positive trace-preserving (PTP) channels and evolution equations. The models are defined in any finite Hilbert space, but the main results are for dimension $N \! = \! 2$. For every normalizable linear or nonlinear positive map $ϕ$ on bounded linear operators $X$, there is an associated normalized PTP channel $ ϕ(X) / {\rm tr}[ϕ(X)]$. Normalized PTP channels include unitary mean field theories, such as the Gross-Pitaevskii equation for interacting bosons, as well as models of linear and nonlinear dissipation. They classify into 4 types, yielding 3 distinct forms of nonlinearity whose computational power we explore. In the qubit case these channels support Bloch ball torsion and other distortions studied previously, where it has been shown that such nonlinearity can be used to increase the separation between a pair of close qubit states, suggesting an exponential speedup for state discrimination. Building on this idea, we argue that this operation can be made robust to noise by using dissipation to induce a bifurcation to a novel phase where a pair of attracting fixed points create an intrinsically fault-tolerant nonlinear state discriminator.
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks

Unsupervised fine-grained class clustering is a practical yet challenging task due to the difficulty of feature representations learning of subtle object details. We introduce C3-GAN, a method that leverages the categorical inference power of InfoGAN with contrastive learning. We aim to learn feature representations that encourage a dataset to form distinct cluster boundaries in the embedding space, while also maximizing the mutual information between the latent code and its image observation. Our approach is to train a discriminator, which is also used for inferring clusters, to optimize the contrastive loss, where image-latent pairs that maximize the mutual information are considered as positive pairs and the rest as negative pairs. Specifically, we map the input of a generator, which was sampled from the categorical distribution, to the embedding space of the discriminator and let them act as a cluster centroid. In this way, C3-GAN succeeded in learning a clustering-friendly embedding space where each cluster is distinctively separable. Experimental results show that C3-GAN achieved the state-of-the-art clustering performance on four fine-grained image datasets, while also alleviating the mode collapse phenomenon. Code is available at https://github.com/naver-ai/c3-gan.
Better Intermediates Improve CTC Inference

This paper proposes a method for improved CTC inference with searched intermediates and multi-pass conditioning. The paper first formulates self-conditioned CTC as a probabilistic model with an intermediate prediction as a latent representation and provides a tractable conditioning framework. We then propose two new conditioning methods based on the new formulation: (1) Searched intermediate conditioning that refines intermediate predictions with beam-search, (2) Multi-pass conditioning that uses predictions of previous inference for conditioning the next inference. These new approaches enable better conditioning than the original self-conditioned CTC during inference and improve the final performance. Experiments with the LibriSpeech dataset show relative 3%/12% performance improvement at the maximum in test clean/other sets compared to the original self-conditioned CTC.
A comprehensive survey of zero-shot image classification: methods, implementation, and fair evaluation

&lt;abstract&gt;&lt;p&gt;Deep learning methods may decline in their performance when the number of labelled training samples is limited, in a scenario known as few-shot learning. The methods may even degrade the accuracy in classifying instances of classes that have not been seen previously, called zero-shot learning. While the classification results achieved by the zero-shot learning methods are steadily improved, different problem settings, and diverse experimental setups have emerged. It becomes difficult to measure fairly the effectiveness of each proposed method, thus hindering further research in the field. In this article, a comprehensive survey is given on the methodology, implementation, and fair evaluations for practical and applied computing facets on the recent progress of zero-shot learning.&lt;/p&gt;&lt;/abstract&gt;
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution

Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing a variety of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. This paper proposes an effective non-blind deconvolution approach by learning discriminative shrinkage functions to model these terms implicitly. Most existing methods use deep convolutional neural networks (CNNs) or radial basis functions to learn the regularization term simply. In contrast, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with Maxout layers to learn discriminative shrinkage functions, which directly approximates the solutions of these two sub-problems. Moreover, the fast-Fourier-transform-based image restoration usually leads to ringing artifacts. At the same time, the conjugate-gradient-based approach is time-consuming; we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art methods in terms of efficiency and accuracy. Source codes, models, and more results are available at https://github.com/setsunil/DSDNet .
HuSpaCy: an industrial-strength Hungarian natural language processing toolkit

Although there are a couple of open-source language processing pipelines available for Hungarian, none of them satisfies the requirements of today's NLP applications. A language processing pipeline should consist of close to state-of-the-art lemmatization, morphosyntactic analysis, entity recognition and word embeddings. Industrial text processing applications have to satisfy non-functional software quality requirements, what is more, frameworks supporting multiple languages are more and more favored. This paper introduces HuSpaCy, an industry-ready Hungarian language processing toolkit. The presented tool provides components for the most important basic linguistic analysis tasks. It is open-source and is available under a permissive license. Our system is built upon spaCy's NLP components resulting in an easily usable, fast yet accurate application. Experiments confirm that HuSpaCy has high accuracy while maintaining resource-efficient prediction capabilities.
Perspektiva primene elektronske knjige iz ugla studenata učiteljskih i pedagoških fakulteta Zapadnog Balkana

The development of electronic media and changes of the social context in the situation of the global health crisis in the field of education have resulted in an increased interest in digital resources, especially in the application of e-books in teaching. Resorting to this book format raises the question of the perspective of its application in the future changed social circumstances. The authors examine (a) the experiences of students of teacher education and pedagogical faculties (N = 394) from four countries of the Western Balkans regarding the use of digital libraries; (b) the anticipated purpose of using e-books upon the completion of their studies; (c) students' views on the universal values of books, regardless of the format in which they are published. The attitudes of these students regarding the future application of e-books is determined by their open-mindedness relative to the application of modern technologies for educational purposes, experience in using e-books, their views on the purpose of the future application of this type of books, and values regarding different book formats and books in general. The research findings show that future preschool and primary school teachers are characterized by the values that qualify them for an adequate approach to the outcomes of future changes in the development of books and modern education.
STUDY ON FRAGILITY FUNCTION UPDATING FOR PARTITION WALL USING IMAGE PROCESSING AND MONITORING SYSTEM

A large shaking table test of a steel-framed unit house specimen with a partition wall was conducted to obtain the relationship between the rotation angle (Rot) of a partition wall obtained from the gyro sensor and the inter-story drift ratio (IDR) of the frame. Additionally, damage classification using deep learning was performed from the images obtained by the monitoring camera employed to the shaking table test. Combining the IDR and/or Rot information obtained from the monitoring sensors and the visible damage information obtained from the monitoring cameras (as the damage state of DS1, DS2 and DS3), a method of updating the prepared fragility curve by Bayesian estimation is proposed. Furthermore, possibility of optimizing the prepared fragility curve by applying the appropriate IDR-Rot relation is discussed.
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go

Aligning with ACL 2022 special Theme on "Language Diversity: from Low Resource to Endangered Languages", we discuss the major linguistic and sociopolitical challenges facing development of NLP technologies for African languages. Situating African languages in a typological framework, we discuss how the particulars of these languages can be harnessed. To facilitate future research, we also highlight current efforts, communities, venues, datasets, and tools. Our main objective is to motivate and advocate for an Afrocentric approach to technology development. With this in mind, we recommend \textit{what} technologies to build and \textit{how} to build, evaluate, and deploy them based on the needs of local African communities.
Automated Islamic Jurisprudential Legal Opinions Generation Using Artificial Intelligence

Islam is the second-largest and fastest-growing religion. The Islamic Law, Sharia, represents a profound component of the day-to-day lives of Muslims. While sources of Sharia are available for anyone, it often requires a highly qualified person, the Mufti, to provide Fatwa. With Islam followers representing almost 25% of the planet earth population, generating many queries, and the sophistication of the Mufti qualification process, creating a shortage in them, we have a supply-demand problem, calling for Automation solutions. This scenario motivates the application of Artificial Intelligence (AI) to Automated Islamic Fatwa in a scalable way that can adapt to various sources like social media. In this work, the potential of AI, Machine Learning, and Deep Learning, with technologies like Natural Language Processing (NLP), paving the way to help the Automation of Islam Fatwa are explored. The work started by surveying the State-of-The-Art (SoTA) of NLP and exploring the potential use-cases to solve the problems of Question answering and Text Classification in the Islamic Fatwa Automation. The first and major enabler component for AI application for Islamic Fatwa, the data were presented by building the largest dataset for Islamic Fatwa, spanning the widely used websites for Fatwa. Moreover, the baseline systems for Topic Classification, Topic Modeling, and Retrieval-based Question-Answering are presented to set the future research and benchmark on the dataset. Finally, the dataset is released and baselines to the public domain to help advance future research in the area.
Optimality of Finite-Support Parameter Shift Rules for Derivatives of Variational Quantum Circuits

Variational (or, parameterized) quantum circuits are quantum circuits that contain real-number parameters, that need to be optimized/"trained" in order to achieve the desired quantum-computational effect. For that training, analytic derivatives (as opposed to numerical derivation) are useful. Parameter shift rules have received attention as a way to obtain analytic derivatives, via statistical estimators. In this paper, using Fourier Analysis, we characterize the set of all shift rules that realize a given fixed partial derivative of a multi-parameter VQC. Then, using Convex Optimization, we show how the search for the shift rule with smallest standard deviation leads to a primal-dual pair of convex optimization problems. We study these optimization problems theoretically, prove a strong duality theorem, and use it to establish optimal dual solutions for several families of VQCs. This also proves optimality for some known shift rules and answers the odd open question. As a byproduct, we demonstrate how optimal shift rules can be found efficiently computationally, and how the known optimal dual solutions help with that.
Continuous Performance Evaluation for Business Process Outcome Monitoring

Abstract While a few approaches to online predictive monitoring have focused on concept drift model adaptation, none have considered in depth the issue of performance evaluation for online process outcome prediction. Without such a continuous evaluation, users may be unaware of the performance of predictive models, resulting in inaccurate and misleading predictions. This paper fills this gap by proposing a framework for evaluating online process outcome predictions, comprising two different evaluation methods. These methods are partly inspired by the literature on streaming classification with delayed labels and complement each other to provide a comprehensive evaluation of process monitoring techniques: one focuses on real-time performance evaluation, i.e., evaluating the performance of the most recent predictions, whereas the other focuses on progress-based evaluation, i.e., evaluating the ability of a model to output correct predictions at different prefix lengths. We present an evaluation involving three publicly available event logs, including a log characterised by concept drift.
Shadow Distillation: Quantum Error Mitigation with Classical Shadows for Near-Term Quantum Processors

Mitigating errors in quantum information processing devices is especially important in the absence of fault tolerance. An effective method in suppressing state-preparation errors is using multiple copies to distill the ideal component from a noisy quantum state. Here, we use classical shadows and randomized measurements to circumvent the need for coherent access to multiple copies at an exponential cost. We study the scaling of resources using numerical simulations and find that the overhead is still favorable compared to full state tomography. We optimize measurement resources under realistic experimental constraints and apply our method to an experiment preparing Greenberger-Horne-Zeilinger (GHZ) state with trapped ions. In addition to improving stabilizer measurements, the analysis of the improved results reveals the nature of errors affecting the experiment. Hence, our results provide a directly applicable method for mitigating errors in near-term quantum computers.
Natural Language Deduction through Search over Statement Compositions

In settings from fact-checking to question answering, we frequently want to know whether a collection of evidence (premises) entails a hypothesis. Existing methods primarily focus on the end-to-end discriminative version of this task, but less work has treated the generative version in which a model searches over the space of statements entailed by the premises to constructively derive the hypothesis. We propose a system for doing this kind of deductive reasoning in natural language by decomposing the task into separate steps coordinated by a search procedure, producing a tree of intermediate conclusions that faithfully reflects the system's reasoning process. Our experiments on the EntailmentBank dataset (Dalvi et al., 2021) demonstrate that the proposed system can successfully prove true statements while rejecting false ones. Moreover, it produces natural language explanations with a 17% absolute higher step validity than those produced by an end-to-end T5 model.
ISP Probing Reduction with Anaximander

Since the early 2000’s, Internet topology discovery has been an active research topic, providing data for various studies such as Internet modeling, network management, or to assist and support network protocol design. Within this research area, ISP mapping at the router level has attracted little interest despite its utility to perform intra-domain routing evaluation. Since Rocketfuel (and, to a smaller extent, mrinfo), no new tool or method has emerged for systematically mapping intra-domain topologies. In this paper, we introduce Anaximander, a new efficient approach for probing and discovering a targeted ISP in particular. Considering a given set of vantage points, we implement and combine several predictive strategies to mitigate the number of probes to be sent without sacrificing the ISP coverage. To assess the ability of our method to efficiently retrieve an ISP map, we rely on a large dataset of ISPs having distinct nature and demonstrate how Anaximander can be tuned with a simple parameter to control the trade-off between coverage and probing budget.
HOW TO CONSTRUCT CSIDH ON QUADRATIC AND TWISTED EDWARDS CURVES

In one of the famous works, an incorrect formulation and an incorrect solution of the implementation problem of the CSIDH algorithm on Edwards curves is discovered. A detailed critique of this work with a proof of the fallacy of its concept is given. Specific properties of three non-isomorphic classes of supersingular curves in the generalized Edwards form is considered: complete, quadratic, and twisted Edwards curves. Conditions for the existence of curves of all classes with the order p+1 of curves over a prime field are determined. The implementation of the CSIDH algorithm on isogenies of odd prime degrees based on the use of quadratic twist pairs of elliptic curves. To this end, the CSIDH algorithm can be construct both on complete Edwards curves with quadratic twist within this class, and on quadratic and twisted Edwards curves forming pairs of quadratic twist. In contrast to this, the authors of a well-known work are trying to prove theorems with statement about existing a solution within one class of curves with a parameter that is a square. The critical analysis of theorems, lemmas, and erroneous statements in this work is given. Theorem 2 on quadratic twist in classes of Edwards curves is proved. A modification of the CSIDH algorithm based on isogenies of quadratic and twisted Edwards curves is presented. To illustrate the correct solution of the problem, an example of Alice and Bob calculations in the secret sharing scheme according to the CSIDH algorithm is considered.
Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments

Dynamic and multimodal features are two important properties and widely existed in many real-world optimization problems. The former illustrates that the objectives and/or constraints of the problems change over time, while the latter means there is more than one optimal solution (sometimes including the accepted local solutions) in each environment. The dynamic multimodal optimization problems (DMMOPs) have both of these characteristics, which have been studied in the field of evolutionary computation and swarm intelligence for years, and attract more and more attention. Solving such problems requires optimization algorithms to simultaneously track multiple optima in the changing environments. So that the decision makers can pick out one optimal solution in each environment according to their experiences and preferences, or quickly turn to other solutions when the current one cannot work well. This is very helpful for the decision makers, especially when facing changing environments. In this competition, a test suit about DMMOPs is given, which models the real-world applications. Specifically, this test suit adopts 8 multimodal functions and 8 change modes to construct 24 typical dynamic multimodal optimization problems. Meanwhile, the metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments. This competition will be very helpful to promote the development of dynamic multimodal optimization algorithms.