Reviewerly/exHarmony huggingface dataset:
effect of using learning video media towards students’ study achievement of fiqh subject grade VII (research at state Islamic junior high school model Pasirsukarayat)

This research aims to determine whether studying video media affects students' study accomplishment of Fiqh in grade 7 at State Islamic Junior High School Model Pasirsukarayat. The research is based on the assumption that students' study success is tied to the educational medium employed. Using learning material that engages and motivates students will impact their learning accomplishment. This study used a pretest-posttest two-group design. Observation, documentation, interview, and test are data collection methods. Based on the hypothesis significance test result, employing learning video media (t-count 2,58 t-table = 1,71) at the real level = 0.05 and the degree of freedom (df) = 78 has the lowest score = 7 and the maximum score = 10. The group that does not utilize learning video media scores (t-count 2,45 t-table = 1,71) at the actual level = 0,05 the degree of freedom (df) = 78 with the lowest score = 5 and the maximum score = 7. Ho is rejected, hence it may be concluded that utilizing learning video medium affects pupils' Fiqh grade 7 study success.
Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples

Deep learning classifiers are known to be vulnerable to adversarial examples. A recent paper presented at ICML 2019 proposed a statistical test detection method based on the observation that logits of noisy adversarial examples are biased toward the true class. The method is evaluated on CIFAR-10 dataset and is shown to achieve 99% true positive rate (TPR) at only 1% false positive rate (FPR). In this paper, we first develop a classifier-based adaptation of the statistical test method and show that it improves the detection performance. We then propose Logit Mimicry Attack method to generate adversarial examples such that their logits mimic those of benign images. We show that our attack bypasses both statistical test and classifier-based methods, reducing their TPR to less than 2:2% and 1:6%, respectively, even at 5% FPR. We finally show that a classifier-based detector that is trained with logits of mimicry adversarial examples can be evaded by an adaptive attacker that specifically targets the detector. Furthermore, even a detector that is iteratively trained to defend against adaptive attacker cannot be made robust, indicating that statistics of logits cannot be used to detect adversarial examples.
Quad-cone-rotor: A Novel Tilt Quadrotor with Severe-fault-tolerant Ability

Conventional quadrotors received great attention in trajectory design and fault-tolerant control in these years. The direction of each thrust is perpendicular to the body because of the geometrics in mechanical design. Comparing with the conventional quadrotor, a novel quadrotor named quad-tilt-rotor brings better freedom in manipulating the thrust vector. Quad-tilt-rotor augments the additional degrees of freedom in the thrust, providing the possibility of violating the normal direction of the thrust in the conventional quadrotor. This provides the ability of greater agility in control. This paper presents a novel design of a quad-tilt-rotor (quad-cone-rotor) whose thrust can be assigned along the edge of a cone shape. Besides the inheriting merits in agile from quad-tilt-rotor, the quad-cone-rotor is expected to take fault-tolerant control in severe dynamic failure (total loss in all thrusts). We simulate the control result in a UAV simulator in SIMULINK, MATLAB.
Gaussian belief propagation solvers for nonsymmetric systems of linear equations

In this paper, we argue for the utility of deterministic inference in the classical problem of numerical linear algebra, that of solving a linear system. We show how the Gaussian belief propagation solver, known to work for symmetric matrices can be modified to handle nonsymmetric matrices. Furthermore, we introduce a new algorithm for matrix inversion that corresponds to the generalized belief propagation derived from the cluster variation method (or Kikuchi approximation). We relate these algorithms to LU and block LU decompositions and provide certain guarantees based on theorems from the theory of belief propagation. All proposed algorithms are compared with classical solvers (e.g., Gauss-Seidel, BiCGSTAB) with application to linear elliptic equations. We also show how the Gaussian belief propagation can be used as multigrid smoother, resulting in a substantially more robust solver than the one based on the Gauss-Seidel iterative method.
Challenges University Students Faced in Their Online Paragraph Writing

The study aimed to explore the challenges that faced Ajloun National University EFL students through their online paragraph writing course using Microsoft Teams. 40 second-year EFL students participated in the study. They completed a 10-item questionnaire at the end of an online paragraph writing course. Means and standard deviation for the type and number of challenges were calculated. The Independent Sample t- Test was applied. The results showed that they faced five challenges: inability to focus, lack of interaction, problems getting knowledge, cooperating with classmates, and placing distractions. Based on the results of the study some recommendations for teaching and learning online paragraph writing were suggested.
A Meta-analysis of the Literature on Mobile Assisted Language Learning in Response to COVID-19 in Saudi Arabia

This study attempts a meta-analysis of research conducted in Saudi Arabia on Mobile Assisted Language Learning (MALL) related to the teaching and learning of English language in response to COVID-19 that led to the lockdown of education institutions. In this connection, a comprehensive search on Google Chrome and Google Scholar was conducted to collect data to answer the research questions and thus achieves its objectives. Fifty research articles and PhD dissertations were identified, but only seven of them met two selection criteria used in this study: the study should be conducted during or after COVID-19; and it should focus on mobile applications per se. These criteria excluded forty-two articles and PhD dissertations from selection. The studies that were not selected for meta-analysis were either review research articles or data-driven research articles that did not center upon specific mobile applications as in the case of articles that simply focused on “pronunciation applications” without naming one such application. The studies selected for meta-analysis used qualitative, quantitative, and mixed methods to collect their respective data. Positive results emerged from all the studies regarding the use of mobile applications in EFL learning in the Saudi context. This conclusion is equally true for motivation, perception and attitude studies. The results fell roughly into three major categories: the use of mobile applications in informal learning, learners’ motivation, perceptions and attitudes towards mobile phone applications as learning platforms, and the effect of mobile applications on learning style.
(In)Stability for the Blockchain: Deleveraging Spirals and Stablecoin Attacks

We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We derive fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets face deleveraging feedback effects that cause illiquidity during crises and exacerbate collateral drawdown, and characterize stable dynamics of the system under particular conditions. The possibility of such `deleveraging spirals' was first predicted in the initial release of our paper in 2019 and later directly observed during the `Black Thursday' crisis in Dai in 2020. From these insights, we suggest design improvements that aim to improve long-term stability. We also introduce new attacks that exploit arbitrage-like opportunities around stablecoin liquidations. Using our model, we demonstrate that these can be profitable. These attacks may induce volatility in the `stable' asset and cause perverse incentives for miners, posing risks to blockchain consensus. A variant of such attacks also later occurred during Black Thursday, taking the form of mempool manipulation to clear Dai liquidation auctions at near zero prices, costing $8m.
PROBLEMATIKA EVALUASI PEMBELAJARAN DI MIS AS SALMAH DESA KWALA SERAPUH KECAMATAN TANJUNG PURA

Some of the problems seen in the research area include the lack of interest and motivation of students in learning and limited infrastructure. The purpose of this study was to find out the problems of learning evaluation at MIS As-Salmah, Kwala Serapuh village and find out solutions to the problems of learning evaluation. The method of this research is qualitative. The type of this research is descriptive. Data collection techniques in this study were observation, interviews, and documentation. Data analysis techniques in this study were collecting data, reducing data, presenting data, and drawing conclusions. The results of this study are the difficulty of teachers in assessing learning for children with special needs, some teachers have not mastered the RDM application for learning assessment, and the limited learning infrastructure.&#x0D;
Verification of a Synthetic Rainfall Model Based on the ArRR and Satellite Rainfall

Synthetic rainfall is needed as an input in the analysis of design flood. This research intends to conduct the verification of a synthetic rainfall model based on the ArRR and satellite result. The rainfall data is as the main component in obtaining the design, detail design, operation and maintenance of effective water resource infrastructure. Rainfall data is often used for the analysis of flood balance, flood frequency distribution, estimation of river discharge, and design of water structure. However, the main constraint in designing the water resource infrastructure is less available rainfall data temporally and spatially. The limitation of available rainfall station number causes inaccuracy in analyzing the area mean rainfall. Therefore, sometimes the correlation between recorded data from the rainfall station and water estimation station does not show a positive and strong correlation. This is due to less rainfall data that are spatially representative in the watershed upstream. The verification is carried out in the Ciliwung Hulu watershed. The methodology consists of ArRR analysis of the two extreme rainfall events, then the ArRR is analyzed using the synthetic rainfall model equation that is applied for watershed areas &lt; 750 km2; in the end, we spatially compared the satellite and observed rainfall. The analysis of the ArRR value is based on the formulation as follows: ArRR_i=(0,026FB-0,307*Re_i+1,184 Q_(i+lag)/A+0,693)/(C_ArRR.K_ArRR ). The lag time that is used is 3 h based on the analysis of time to peak by using the Nakaysu hydrograph by assuming that the run-off coefficient is 0.35. The spatial comparison between satellite and observed daily rainfall on February 19, 2021, and October 28, 2021, shows that the rainfall distribution pattern is more similar with adding the ArRR synthetic rainfall station than with only using the ARR station.
Spectrogram Data Set for Deep Learning Based RF-Frame Detection

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to manual spectrum analysis. &#x0D; In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labelling a high variety of frames in different environments is too challenging, an artificial data generation pipeline has been developed. &#x0D; The data set consists of 20 000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enables the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.
Spectral characterisation of hydrothermal alteration associated with sediment-hosted Cu-Ag mineralisation in the Central European Kupferschiefer

Abstract. The analysis of hydrothermal alteration in exploration drill cores allows to trace fluid-rock interaction processes, identify fluid flow paths and thus determine vectors in mineral systems. Hyperspectral imaging techniques are increasingly being employed to fill the scale gap between lab-based petrographic or geochemical analyses and the typical size of exploration targets. Hyperspectral imaging permits the rapid, cost-efficient and continuous characterisation of alteration mineralogy and texture along entire drill cores with a spatial sampling of a few millimetres. In this contribution, we present the results of an exploratory study on three mineralised drill cores from the Spremberg-Graustein Kupferschiefer-type Cu-Ag deposit in the Lusatia region of Germany. We demonstrate that hyperspectral imaging is a tool well-suited to recognize and track the effects of hydrothermal alteration associated with stratabound hydrothermal mineralization. Micro X-ray fluorescence spectrometry was used to validate the alteration mineral assemblages identified in hyperspectral data acquired in the visible, near, shortwave, mid-wave and long-wave infrared. Spectral features associated with the occurrence of iron oxide, kaolinite, sulphate, and carbonates were identified and mapped. We identify intensive hydrothermal alteration of the sandstones immediately below the Kupferschiefer horizon sensu stricto, spatially associated with or stratigraphically adjacent to Cu-Ag mineralisation. Importantly, we can clearly distinguish two mineralogically distinct styles of alteration (hematite and ferroan carbonate) that bracket high-grade Cu-Ag mineralisation. The occurrence of well crystalline kaolinite in the sandstone units is spatially and genetically related to Cu-Ag mineralisation. Proximal Fe-carbonate and kaolinite alteration have not previously been documented for the high-grade Cu-Ag deposits of the Central European Kupferschiefer. Hematite alteration is well known in Kupferschiefer-type ore deposits. It marks the flow path of oxidising, metal-bearing hydrothermal fluids towards the site of hydrothermal sulphide mineralization. Ferroan carbonate alteration in carbonate rocks located above the main mineralized zone, in contrast, is interpreted to mark hydrothermal fluid discharge from the mineralizing system. Although this study is limited to a small number of drill cores, our results suggest that hyperspectral imaging techniques may be used to identify vectors towards high-grade Cu-Ag mineralisation in Kupferschiefer-type mineral systems.
Self-assisted deterministic hyperentangled-Bell-state analysis for polarization and double longitudinal momentum degrees of freedom of photon system

Hyperentangled state analysis is an important module in high-capacity quantum communication. We present a self-assisted deterministic hyperentangled-Bell-state analysis (HBSA) scheme for photon system entangled in three degrees of freedom (DOFs), where 64 polarization-double longitudinal momentum hyperentangled Bell states are completely distinguished. In this HBSA scheme, the four first longitudinal momentum Bell states are distinguished determinately by nondestructive first longitudinal momentum Bell state analyzer, which is constructed with cross-Kerr nonlinearity medium. The 16 second longitudinal momentum-polarization hyperentangled Bell states are distinguished determinately by self-assisted second longitudinal momentum-polarization hyperentangled Bell state analyzer using linear optical elements, where the first longitudinal momentum Bell state and time-bin entangled state are used as auxiliary. Using this self-assisted method, the application of nonlinear optical resource in HBSA scheme has been largely reduced, which makes this self-assisted deterministic HBSA scheme has potential application prospects in high-capacity quantum communication.
Class-independent sequential full image segmentation, using a convolutional net that finds a segment within an attention region, given a pointer pixel within this segment

This work examines the use of a fully convolutional net (FCN) to find an image segment, given a pixel within this segment region. The net receives an image, a point in the image and a region of interest (RoI ) mask. The net output is a binary mask of the segment in which the point is located. The region where the segment can be found is contained within the input RoI mask. Full image segmentation can be achieved by running this net sequentially, region-by-region on the image, and stitching the output segments into a single segmentation map. This simple method addresses two major challenges of image segmentation: 1) Segmentation of unknown categories that were not included in the training set. 2) Segmentation of both individual object instances (things) and non-objects (stuff), such as sky and vegetation. Hence, if the pointer pixel is located within a person in a group, the net will output a mask that covers that individual person; if the pointer point is located within the sky region, the net returns the region of the sky in the image. This is true even if no example for sky or person appeared in the training set. The net was tested and trained on the COCO panoptic dataset and achieved 67% IOU for segmentation of familiar classes (that were part of the net training set) and 53% IOU for segmentation of unfamiliar classes (that were not included in the training).
Transfer Adaptation Learning: A Decade Survey

The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. Conventional machine learning aims to find a model with the minimum expected risk on test data by minimizing the regularized empirical risk on the training data, which, however, supposes that the training and test data share similar joint probability distribution. TAL aims to build models that can perform tasks of target domain by learning knowledge from a semantic related but distribution different source domain. It is an energetic research filed of increasing influence and importance, which is presenting a blowout publication trend. This paper surveys the advances of TAL methodologies in the past decade, and the technical challenges and essential problems of TAL have been observed and discussed with deep insights and new perspectives. Broader solutions of transfer adaptation learning being created by researchers are identified, i.e., instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation and adversarial adaptation, which are beyond the early semi-supervised and unsupervised split. The survey helps researchers rapidly but comprehensively understand and identify the research foundation, research status, theoretical limitations, future challenges and under-studied issues (universality, interpretability, and credibility) to be broken in the field toward universal representation and safe applications in open-world scenarios.
Automatic Generation of Board Game Manuals

In this paper we present a process for automatically generating manuals for board games within the Ludii general game system. This process requires many different sub-tasks to be addressed, such as English translation of Ludii game descriptions, move visualisation, highlighting winning moves, strategy explanation, among others. These aspects are then combined to create a full manual for any given game. This manual is intended to provide a more intuitive explanation of a game's rules and mechanics, particularly for players who are less familiar with the Ludii game description language and grammar.
Summary Refinement through Denoising

We propose a simple method for post-processing the outputs of a text summarization system in order to refine its overall quality. Our approach is to train text-to-text rewriting models to correct information redundancy errors that may arise during summarization. We train on synthetically generated noisy summaries, testing three different types of noise that introduce out-of-context information within each summary. When applied on top of extractive and abstractive summarization baselines, our summary denoising models yield metric improvements while reducing redundancy.
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures

Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to include the time dimension, using modules such as 3D convolutions, or by using two-stream design to capture both appearance and motion in videos. We interpret a video CNN as a collection of multi-stream convolutional blocks connected to each other, and propose the approach of automatically finding neural architectures with better connectivity and spatio-temporal interactions for video understanding. This is done by evolving a population of overly-connected architectures guided by connection weight learning. Architectures combining representations that abstract different input types (i.e., RGB and optical flow) at multiple temporal resolutions are searched for, allowing different types or sources of information to interact with each other. Our method, referred to as AssembleNet, outperforms prior approaches on public video datasets, in some cases by a great margin. We obtain 58.6% mAP on Charades and 34.27% accuracy on Moments-in-Time.
Solving All Regression Models For Learning Gaussian Networks Using Givens Rotations

Score based learning (SBL) is a promising approach for learning Bayesian networks. The initial step in the majority of the SBL algorithms consists of computing the scores of all possible child and parent-set combinations for the variables. For Bayesian networks with continuous variables, a particular score is usually calculated as a function of the regression of the child over the variables in the parent-set. The sheer number of regressions models to be solved necessitates the design of efficient numerical algorithms. In this paper, we propose an algorithm for an efficient and exact calculation of regressions for all child and parent-set combinations. In the proposed algorithm, we use QR decompositions (QRDs) to capture the dependencies between the regressions for different families and Givens rotations to efficiently traverse through the space of QRDs such that all the regression models are accounted for in the shortest path possible. We compare the complexity of the suggested method with different algorithms, mainly those arising in all subset regression problems, and show that our algorithm has the smallest algorithmic complexity. We also explain how to parallelize the proposed method so as to decrease the runtime by a factor proportional to the number of processors utilized.