Reviewerly/exHarmony huggingface dataset:
PENGARUH METODE TEAMS GAMES TOURNAMENT DAN MOTIVASI SECARA BERSAMA-SAMA TERHADAP HASIL BELAJAR FIQH SISWA DI MTS NEGERI GOWA

This study aims to determine and describe the effect of the Teams Games Tournament Method and Motivation Together on Students' Fiqh Learning Outcomes at Mts Negeri Gowa. The approach used in this research is a quantitative approach. The results of this study indicate that there is an effect of the Teams Games Method Application (x1) and learning motivation (x2) on learning outcomes (y) at Mts Negeri Gowa, Bontomarannu District, Gowa Regency.
Fast Class-wise Updating for Online Hashing

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is time-consuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75% storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets.
Learning by Passing Tests, with Application to Neural Architecture Search

Learning through tests is a broadly used methodology in human learning and shows great effectiveness in improving learning outcome: a sequence of tests are made with increasing levels of difficulty; the learner takes these tests to identify his/her weak points in learning and continuously addresses these weak points to successfully pass these tests. We are interested in investigating whether this powerful learning technique can be borrowed from humans to improve the learning abilities of machines. We propose a novel learning approach called learning by passing tests (LPT). In our approach, a tester model creates increasingly more-difficult tests to evaluate a learner model. The learner tries to continuously improve its learning ability so that it can successfully pass however difficult tests created by the tester. We propose a multi-level optimization framework to formulate LPT, where the tester learns to create difficult and meaningful tests and the learner learns to pass these tests. We develop an efficient algorithm to solve the LPT problem. Our method is applied for neural architecture search and achieves significant improvement over state-of-the-art baselines on CIFAR-100, CIFAR-10, and ImageNet.
PENINGKATAN KOMPETENSI PENGGUNA SISTEM INFORMASI AKUNTANSI KEUANGAN PADA SEKOLAH MUHAMMADIYAH DI BUKIT KECIL KOTA PALEMBANG

Partners in the implementation of community service are Muhammadiyah 2 Middle School, Muhammadiyah 1 Aliyah Madrasah School, Muhammadiyah 2 Middle School, Muhammadiyah 1 Tsanawiah Madrasah School and Muhammadiayah Elementary School 1. These schools are under the supervision of the Muhammadiyah Bukit Kecil Branch Palembang. Based on the analysis of the situation, it can be identified several problems faced by partners such as the knowledge possessed by the school treasurer in the use of accounting information systems, and the process of preparing financial statements is also very limited. The service implementation method consists of two stages. The first, step is to arrange material, namely basic accounting and accounting information systems. The second stage conducts training and coaching, to producing financial reports. Results and discussion, community service activities are carried out by means of training and assistance. The community service activities were carried out for the five partners, also involved a treasurer of the Palembang Muhammadiyah Branch Manager. This activity began with conducting a field survey for the stage of preparing the work plan. This activity resulted in an agreement with the principal included in the partner and the Muhammadiyah Palembang Branch Manager, which conducted training on the preparation of financial statements, and trained to develop potential treasurers using the school's financial accounting information system. The conclusions of community service activities are as follows: a) has been running well and smoothly, b) increasing the ability of treasurers of the Muhammadiyah School in Bukit Kecil to use accounting information systems and compile school financial reports..
The NP-hard problem of computing the maximal sample variance over interval data is solvable in almost linear time with high probability

We consider the algorithm by Ferson et al. (Reliable computing 11(3), p. 207-233, 2005) designed for solving the NP-hard problem of computing the maximal sample variance over interval data, motivated by robust statistics (in fact, the formulation can be written as a nonconvex quadratic program with a specific structure). First, we propose a new version of the algorithm improving its original time bound $O(n^2 2^\omega)$ to $O(n \log n+n\cdot 2^\omega)$, where $n$ is number of input data and $\omega$ is the clique number in a certain intersection graph. Then we treat input data as random variables as it is usual in statistics) and introduce a natural probabilistic data generating model. We get $2^\omega = O(n^{1/\log\log n})$ and $\omega = O(\log n / \log\log n)$ on average. This results in average computing time $O(n^{1+\epsilon})$ for $\epsilon > 0$ arbitrarily small, which may be considered as "surprisingly good" average time complexity for solving an NP-hard problem. Moreover, we prove the following tail bound on the distribution of computation time: hard instances, forcing the algorithm to compute in time $2^{\Omega(n)}$, occur rarely, with probability tending to zero at the rate $e^{-n\log\log n}$.
Salient Object Detection in the Deep Learning Era: An In-Depth Survey

As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). To enable in-depth understanding of deep SOD, in this paper, we provide a comprehensive survey covering various aspects, ranging from algorithm taxonomy to unsolved issues. In particular, we first review deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection. Following that, we summarize and analyze existing SOD datasets and evaluation metrics. Then, we benchmark a large group of representative SOD models, and provide detailed analyses of the comparison results. Moreover, we study the performance of SOD algorithms under different attribute settings, which has not been thoroughly explored previously, by constructing a novel SOD dataset with rich attribute annotations covering various salient object types, challenging factors, and scene categories. We further analyze, for the first time in the field, the robustness of SOD models to random input perturbations and adversarial attacks. We also look into the generalization and difficulty of existing SOD datasets. Finally, we discuss several open issues of SOD and outline future research directions.
Predicting Covid-19 Misleading Information Using Sentiment Analysis

Abstract During the go higher of grouping networking time, there has been a be moving as waves of user produced what is in. Millions of people having the same their ideas daily on microblogging site because of its quality of short and simple way of look. To mine the feeling from a having general approval now microblogging support, twitter, we offer and make observation of an example where user post true tweets about covid-19. By using both corpus based and dictionary based method we may clear an offspring of parts coming from different sorts way in, to come to a decision about the connotation adjustment of the emotion statements in tweets. To make clear the example or picture the use and good effects of the offered system an example learning process is presented
The perceptions of students on the application of infographics as instructional media in promoting their writing abilities

The aims to be reached in this article are describing the process of using infographic as the media to teach English writing as well as identifying the perceptions of students toward infographics as the media to teach English writing at the eleventh grade of SMAN 3 Rangkasbitung. This research applied a qualitative descriptive method with two objectives of the research such as: 1) to describe the process of using infographic as the media to teach English writing at the eleventh grade of SMAN 3 Rangkasbitung and (2) to identify the perceptions of students on the use of infographics as the media to teach English writing at the eleventh grade of SMAN 3 Rangkasbitung. This research used four techniques to collect the data; observation, questionnaire, interview, and documentation. The implementation of this research involved one English teacher and 36 students of eleventh grade of IPA 1. The first findings revealed that the process of using infographic in teaching and learning writing which consisted of some processes; making lesson plan and infographics, presenting infographics, assigning the students by using infographic worksheets. The second finding, the students expressed positive responses of infographics; infographics were interesting, understandable, easy to use, and could motivate the students in learning writing. it can be concluded that the process of teaching English writing by using infographics run well. Then, on the use of infographic as the media in learning writing, the students have the positive responses to the infographics. The most students consider that infographic can foster their learning in English class. The students feel that the infographic is indeed effective because infographic do not take much time compared to other media like textbook, infographic do not spend much internet quota, and infographic could be used either in online class or offline class.
Replication Robust Payoff Allocation in Submodular Cooperative Games

&lt;p&gt;Submodular functions have been a powerful mathematical model for a wide range of real-world applications. Recently, submodular functions are becoming increasingly important in machine learning (ML) for modelling notions such as information and redundancy among entities such as data and features. Among these applications, a key question is payoff allocation, i.e., how to evaluate the importance of each entity towards the collective objective? To this end, classic solution concepts from cooperative game theory offer principled approaches to payoff allocation. However, despite the extensive body of game-theoretic literature, payoff allocation in submodular games are relatively under-researched. In particular, an important notion that arises in the emerging submodular applications is redundancy, which may occur from various sources such as abundant data or malicious manipulations where a player replicates its resource and act under multiple identities. Though many game-theoretic solution concepts can be directly used in submodular games, naively applying them for payoff allocation in these settings may incur robustness issues against replication. In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication. Using this metric, we present conditions which theoretically characterise the robustness of semivalues, a wide family of solution concepts including the Shapley and Banzhaf value. Moreover, we empirically validate our theoretical results on an emerging submodular ML application, i.e., the ML data market.&lt;/p&gt;
Weakly-Supervised Camouflaged Object Detection with Scribble Annotations

Existing camouflaged object detection (COD) methods rely heavily on large-scale datasets with pixel-wise annotations. However, due to the ambiguous boundary, annotating camouflage objects pixel-wisely is very time-consuming and labor-intensive, taking ~60mins to label one image. In this paper, we propose the first weakly-supervised COD method, using scribble annotations as supervision. To achieve this, we first relabel 4,040 images in existing camouflaged object datasets with scribbles, which takes ~10s to label one image. As scribble annotations only describe the primary structure of objects without details, for the network to learn to localize the boundaries of camouflaged objects, we propose a novel consistency loss composed of two parts: a cross-view loss to attain reliable consistency over different images, and an inside-view loss to maintain consistency inside a single prediction map. Besides, we observe that humans use semantic information to segment regions near the boundaries of camouflaged objects. Hence, we further propose a feature-guided loss, which includes visual features directly extracted from images and semantically significant features captured by the model. Finally, we propose a novel network for COD via scribble learning on structural information and semantic relations. Our network has two novel modules: the local-context contrasted (LCC) module, which mimics visual inhibition to enhance image contrast/sharpness and expand the scribbles into potential camouflaged regions, and the logical semantic relation (LSR) module, which analyzes the semantic relation to determine the regions representing the camouflaged object. Experimental results show that our model outperforms relevant SOTA methods on three COD benchmarks with an average improvement of 11.0% on MAE, 3.2% on S-measure, 2.5% on E-measure, and 4.4% on weighted F-measure.
Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation

This paper considers a joint multi-graph inference and clustering problem for simultaneous inference of node centrality and association of graph signals with their graphs. We study a mixture model of filtered low pass graph signals with possibly non-white and low-rank excitation. While the mixture model is motivated from practical scenarios, it presents significant challenges to prior graph learning methods. As a remedy, we consider an inference problem focusing on the node centrality of graphs. We design an expectation-maximization (EM) algorithm with a unique low-rank plus sparse prior derived from low pass signal property. We propose a novel online EM algorithm for inference from streaming data. As an example, we extend the online algorithm to detect if the signals are generated from an abnormal graph. We show that the proposed algorithms converge to a stationary point of the maximum-a-posterior (MAP) problem. Numerical experiments support our analysis.
Adaptive Second Order Coresets for Data-efficient Machine Learning

Training machine learning models on massive datasets incurs substantial computational costs. To alleviate such costs, there has been a sustained effort to develop data-efficient training methods that can carefully select subsets of the training examples that generalize on par with the full training data. However, existing methods are limited in providing theoretical guarantees for the quality of the models trained on the extracted subsets, and may perform poorly in practice. We propose AdaCore, a method that leverages the geometry of the data to extract subsets of the training examples for efficient machine learning. The key idea behind our method is to dynamically approximate the curvature of the loss function via an exponentially-averaged estimate of the Hessian to select weighted subsets (coresets) that provide a close approximation of the full gradient preconditioned with the Hessian. We prove rigorous guarantees for the convergence of various first and second-order methods applied to the subsets chosen by AdaCore. Our extensive experiments show that AdaCore extracts coresets with higher quality compared to baselines and speeds up training of convex and non-convex machine learning models, such as logistic regression and neural networks, by over 2.9x over the full data and 4.5x over random subsets.
Certified Everlasting Functional Encryption

Computational security in cryptography has a risk that computational assumptions underlying the security are broken in the future. One solution is to construct information-theoretically-secure protocols, but many cryptographic primitives are known to be impossible (or unlikely) to have information-theoretical security even in the quantum world. A nice compromise (intrinsic to quantum) is certified everlasting security, which roughly means the following. A receiver with possession of quantum encrypted data can issue a certificate that shows that the receiver has deleted the encrypted data. If the certificate is valid, the security is guaranteed even if the receiver becomes computationally unbounded. Although several cryptographic primitives, such as commitments and zero-knowledge, have been made certified everlasting secure, there are many other important primitives that are not known to be certified everlasting secure. In this paper, we introduce certified everlasting FE. In this primitive, the receiver with the ciphertext of a message m and the functional decryption key of a function f can obtain f(m) and nothing else. The security holds even if the adversary becomes computationally unbounded after issuing a valid certificate. We, first, construct certified everlasting FE for P/poly circuits where only a single key query is allowed for the adversary. We, then, extend it to q-bounded one for NC1 circuits where q-bounded means that q key queries are allowed for the adversary with an a priori bounded polynomial q. For the construction of certified everlasting FE, we introduce and construct certified everlasting versions of secret-key encryption, public-key encryption, receiver non-committing encryption, and a garbling scheme, which are of independent interest.
Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification

Person reidentification (re-ID) has been receiving increasing attention in recent years due to its importance for both science and society. Machine learning and particularly Deep Learning (DL) has become the main re-id tool that allowed researches to achieve unprecedented accuracy levels on benchmark datasets. However, there is a known problem of poor generalization of DL models. That is, models trained to achieve high accuracy on one dataset perform poorly on other ones and require re-training. To address this issue, we present a model without trainable parameters which shows great potential for high generalization. It combines a fully analytical feature extraction and similarity ranking scheme with DL-based human parsing used to obtain the initial subregion classification. We show that such combination to a high extent eliminates the drawbacks of existing analytical methods. We use interpretable color and texture features which have human-readable similarity measures associated with them. To verify the proposed method we conduct experiments on Market1501 and CUHK03 datasets achieving competitive rank-1 accuracy comparable with that of DL-models. Most importantly we show that our method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to transfer learning tasks. It is significantly higher than previously reported 30-50% transfer accuracy. We discuss the potential ways of adding new features to further improve the model. We also show the advantage of interpretable features for constructing human-generated queries from verbal description to conduct search without a query image.
DoRO: Disambiguation of referred object for embodied agents

Robotic task instructions often involve a referred object that the robot must locate (ground) within the environment. While task intent understanding is an essential part of natural language understanding, less effort is made to resolve ambiguity that may arise while grounding the task. Existing works use vision-based task grounding and ambiguity detection, suitable for a fixed view and a static robot. However, the problem magnifies for a mobile robot, where the ideal view is not known beforehand. Moreover, a single view may not be sufficient to locate all the object instances in the given area, which leads to inaccurate ambiguity detection. Human intervention is helpful only if the robot can convey the kind of ambiguity it is facing. In this article, we present DoRO (Disambiguation of Referred Object), a system that can help an embodied agent to disambiguate the referred object by raising a suitable query whenever required. Given an area where the intended object is, DoRO finds all the instances of the object by aggregating observations from multiple views while exploring & scanning the area. It then raises a suitable query using the information from the grounded object instances. Experiments conducted with the AI2Thor simulator show that DoRO not only detects the ambiguity more accurately but also raises verbose queries with more accurate information from the visual-language grounding.
Real Image Restoration via Structure-preserving Complementarity Attention

Since convolutional neural networks perform well in learning generalizable image priors from large-scale data, these models have been widely used in image denoising tasks. However, the computational complexity increases dramatically as well on complex model. In this paper, We propose a novel lightweight Complementary Attention Module, which includes a density module and a sparse module, which can cooperatively mine dense and sparse features for feature complementary learning to build an efficient lightweight architecture. Moreover, to reduce the loss of details caused by denoising, this paper constructs a gradient-based structure-preserving branch. We utilize gradient-based branches to obtain additional structural priors for denoising, and make the model pay more attention to image geometric details through gradient loss optimization.Based on the above, we propose an efficiently Unet structured network with dual branch, the visual results show that can effectively preserve the structural details of the original image, we evaluate benchmarks including SIDD and DND, where SCANet achieves state-of-the-art performance in PSNR and SSIM while significantly reducing computational cost.