Reviewerly/exHarmony huggingface dataset:
LINKING MULTIPLE PERSPECTIVES WITH OBJECT-BASED VISUAL CUES FOR SPATIAL VIDEO ANALYSIS

Abstract. The visual analysis of videos in context with mapped information requires support in the challenges of linking different spatial perspectives (e.g., street level and survey perspective), bridging different levels of detail, and relating objects in different visual representation. Uncertainty in the spatial relation between camera views and map complicates these tasks. We implemented visualizations for the visual analysis of street level videos (i.e., video key frames) embedded in their spatial context. As part of this, we developed a design rationale for visual cues that help link the video key frames and a map in cases where the spatial relation between camera view and map is of uncertain accuracy. We implemented three cue types (simplified viewshed, object-based dot cues, street centre line cues) for an image data set with heterogeneous camera localization accuracy and assessed the resulting cue properties. Based on this, we argue in favour of cue designs that minimize uncertain information required for their display at the expense of cues’ spatial explicitness in cases of potentially low camera localization accuracy. When localization accuracy is expected to be at least moderate, particularly, dot cues that refer to unambiguous points of reference within easily recognizable objects of ample size present a promising option to support view co-registration.
Quantum Error Mitigation by Pauli Check Sandwiching

We introduce an error mitigation technique that uses multiple pairs of parity checks to mitigate errors. Each pair of checks uses one ancilla qubit to detect a component of the error operator and represents one layer of the technique. We prove that our technique can recover the noiseless state under the assumption of noise not affecting the checks. Our method is inspired by the parity checks present in stabilizer codes. Our error mitigation scheme does not incur any encoding overhead. Instead, we choose the checks based on the input circuit. We develop an algorithm for obtaining such checks for an arbitrary target circuit. Since our method applies to any circuit and input state, it can be easily combined with other error mitigation techniques. We evaluate the performance of the proposed methods using extensive numerical simulations on 1,850 random input circuits composed of Clifford gates and non-Clifford single-qubit rotations, a class of circuits encompassing most commonly considered variational algorithm circuits. We observe average improvements in fidelity of 34 percentage points with six layers of checks.
Unveiling the successes of religious school leavers in learning English proficiency course via a blended approach

The blended learning approach has achieved great success in the teaching of languages. It has been widely used to teach students at all levels, including university students from religious schools, who continue to grow in number nowadays. Unfortunately, these religious school leavers lacked of guidance in using their own handheld technology for English language learning. Therefore blended learning is the most suitable approach to teach these learners who rely on instructors for guidance. This study, with the qualitative research methods, aims to explore and determine the achievements of religious school leavers in the course of English Proficiency taught via a blended approach. The participants of this study were selected purposefully from six faculties and they are all first-semester students. The researcher conducted a series of semi-structured interviews and focus groups, as well as compiled online reflections and document analysis. The data gathered were analysed using thematic analysis with the assistance of Nvivo software. The findings revealed that (1) the participants perceived the blended learning approach over the traditional method (2) the blended learning approach has successfully contributed to the unexpected successes that include adjustment, positive feelings and remarkable progress in learning the English language.
Easter2.0: Improving convolutional models for handwritten text recognition

Convolutional Neural Networks (CNN) have shown promising results for the task of Handwritten Text Recognition (HTR) but they still fall behind Recurrent Neural Networks (RNNs)/Transformer based models in terms of performance. In this paper, we propose a CNN based architecture that bridges this gap. Our work, Easter2.0, is composed of multiple layers of 1D Convolution, Batch Normalization, ReLU, Dropout, Dense Residual connection, Squeeze-and-Excitation module and make use of Connectionist Temporal Classification (CTC) loss. In addition to the Easter2.0 architecture, we propose a simple and effective data augmentation technique 'Tiling and Corruption (TACO)' relevant for the task of HTR/OCR. Our work achieves state-of-the-art results on IAM handwriting database when trained using only publicly available training data. In our experiments, we also present the impact of TACO augmentations and Squeeze-and-Excitation (SE) on text recognition accuracy. We further show that Easter2.0 is suitable for few-shot learning tasks and outperforms current best methods including Transformers when trained on limited amount of annotated data. Code and model is available at: https://github.com/kartikgill/Easter2
Pelatihan Ketrampilan Jumputan Di Tamanharjo, Singosari Kabupaten Malang

The Covid 19 pandemic that has hit Indonesia since the beginning of 2020 has not only affected people's freedom to gather, but also affected the people's economic life. Many community members lost their jobs so that the community's economic level declined, this also happened in Tamanharjo Village, Singosari, Malang Regency. The problems faced by the community in Tamanharjo Village, Singosari, Malang Regency as partners of this service program are the desire to develop the creativity of mothers in making jumputans, but they are not supported by capital and there is no adequate knowledge and expertise about making jumputans. Partners need knowledge, techniques and skills to make jumputans based on cloth, which will be very helpful in efforts to produce quality jumputan fabric products, so that they will improve the welfare of the local community. To be able to realize the things mentioned above, assistance and training are needed in making jumputans from cloth. The method that will be used in its implementation is by conducting training on the basics of making jumputan fabrics, introducing equipment and also the practice of making jumputans. The implementation of the jumputan skill making program was carried out well with the results in accordance with the output target of this service, namely partners were able to make and produce jumputan batik fabrics with various patterns and colors, so this can be used as capital to develop jumputan businesses.&#x0D; &#x0D; Keywords: Training, Skills, Cloth, Jumputan, Handmade
Perbandingan Nilai K pada Klasifikasi Pneumonia Anak Balita Menggunakan K-Nearest Neighbor

Pneumonia is an infectious disease that attacks the lower respiratory tract and is one of the main causes of death in children under five. Pneumonia is easy to attack toddlers caused by various microorganisms that exist in the environment such as viruses, bacteria, fungi and micro bacteria. This study uses K-Nearest Neighbor (KNN) for the classification of pneumonia in patients based on the symptoms experienced. The KNN classification method is carried out by comparing the object distance between the test data and the overall object on the training data based on the patient's medical history data. The comparison of the percentage of the training data and the test data used is 90:10, 80:20, and 70:30 to calculate the value of the closest distance of the test data to the overall training data with the number of k used. The confusion matrix was used to measure the results of the Pneumonia classification test for toddlers with a combination of the amount of training data and test data on the number of k={1, 3, 5, 7, 9, 11}, the highest accuracy, precision, recall, and F-measure values were obtained. 0.86, 0.89, 1, and 0.91 for 90% training data, 10% test data with a value of k = 3. Keywords: Pneumonia, Toddlers, KNN, Confusion Matrix.
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model

Numerous recent work on unsupervised machine translation (UMT) implies that competent unsupervised translations of low-resource and unrelated languages, such as Nepali or Sinhala, are only possible if the model is trained in a massive multilingual environment, where these low-resource languages are mixed with high-resource counterparts. Nonetheless, while the high-resource languages greatly help kick-start the target low-resource translation tasks, the language discrepancy between them may hinder their further improvement. In this work, we propose a simple refinement procedure to separate languages from a pre-trained multilingual UMT model for it to focus on only the target low-resource task. Our method achieves the state of the art in the fully unsupervised translation tasks of English to Nepali, Sinhala, Gujarati, Latvian, Estonian and Kazakh, with BLEU score gains of 3.5, 3.5, 3.3, 4.1, 4.2, and 3.3, respectively. Our codebase is available at https://github.com/nxphi47/refine_unsup_multilingual_mt
Avoiding Barren Plateaus with Classical Deep Neural Networks

Variational quantum algorithms (VQAs) are among the most promising algorithms in the era of Noisy Intermediate Scale Quantum Devices. The VQAs are applied to a variety of tasks, such as in chemistry simulations, optimization problems, and quantum neural networks. Such algorithms are constructed using a parameterization U($\pmb{\theta}$) with a classical optimizer that updates the parameters $\pmb{\theta}$ in order to minimize a cost function $C$. For this task, in general the gradient descent method, or one of its variants, is used. This is a method where the circuit parameters are updated iteratively using the cost function gradient. However, several works in the literature have shown that this method suffers from a phenomenon known as the Barren Plateaus (BP). This phenomenon is characterized by the exponentially flattening of the cost function landscape, so that the number of times the function must be evaluated to perform the optimization grows exponentially as the number of qubits and parameterization depth increase. In this article, we report on how the use of a classical neural networks in the VQAs input parameters can alleviate the BP phenomenon.
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach

Pre-trained models (PTMs) have lead to great improvements in natural language generation (NLG).However, it is still unclear how much commonsense knowledge they possess.With the goal of evaluating commonsense knowledge of NLG models, recent work has proposed the problem of generative commonsense reasoning, e.g., to compose a logical sentence given a set of unordered concepts.Existing approaches to this problem hypothesize that PTMs lack sufficient parametric knowledge for this task, which can be overcome by introducing external knowledge or task-specific pretraining objectives.Different from this trend, we argue that PTM's inherent ability for generative commonsense reasoning is underestimated due to the order-agnostic property of its input.In particular, we hypothesize that the order of the input concepts can affect the PTM's ability to utilize its commonsense knowledge.To this end, we propose a pre-ordering approach to elaborately manipulate the order of the given concepts before generation.Experiments show that our approach can outperform the more sophisticated models that have access to a lot of external data and resources.
Distinctive Features of Association Measures Applied to Chinese Character Bigram Extraction Tasks

Studying professional discourse, a researcher has now an opportunity to create collections of texts and apply linguistic analysis software tools to them. However, when it comes to Chinese discourse there is a problem with the reliability of automatic word segmentation of texts. One of the ways to extract lexical units in Chinese texts is to apply statistical association measures for collocations to Chinese character bigrams. The purpose of this work is to conduct a comparative analysis of seven different statistical measures for collocations as a means of extracting two-syllabic lexical units (binomes) in an unsegmented Chinese character text. The subject of the analysis is the lexical, grammatical and frequency characteristics of bigrams with higher values of the statistical measures. Their comparison makes it possible to draw a conclusion about the features of statistical measures, in particular, about the best correspondence of linguistic tasks to statistical measures. The linguistic material of the study was a collection of 560 military-related news texts in Chinese with more than 720 thousand characters. The results show that the statistical measures considered can be divided into three groups according to the characteristics of bigrams receiving the highest values. The first group includes measures MI, MS and logDice, which give priority to rare bigrams with limited compatibility of components, such as the Chinese two-syllable single morpheme words “lianmianzi”. These measures do not extract terms well, but can be used to search for phraseologically related components. The measures of the second group, t-score and log-likelihood, are frequency-oriented, similar to frequency analysis, but they cope with non-lexical bigrams better, while log-likelihood somewhat lowers the rank of numerals and pronouns, picking out best the typical vocabulary of professional discourse. The third group includes measures MI3 and MI.log-f, which average the opposite approaches of the first two groups. The MI3 measure is considered to be the most universal one; it could be used to compare different corpora or collections of texts. It is concluded that applying statistical association measures to Chinese character bi-grams is possible and appropriate, when taking into account the correspondence of their specifics to a research task.
Basketball Analysis System Using Artificial Intelligence

Abstract: Realtime multi-individuals 2-D posture assessment is a central question in grant machines to have an information on people in pix and recordings. In this work, we gift a realtime strategy to find the 2-D posture of more than one people in an image. The proposed approach utilizes a nonparametric portrayal, which we check with as Part Affinity Fields (PAFs), to figure out how to accessory approach components with individuals withinside the image the makes a speciality of the subject of the product of gadget acquiring information on withinside the subject of b-ball sports exercises, the utilization of studies procedures comprising of writing studies, video assessment, relative investigations, and numerical data to find gadget acquiring information on continuously assessment of b-ball sports exercises information. [1]. We endlessly look at NBA and Euroleague b-ball through box-rating data withinside the term from 2000 to 2017. In general, the quantitative varieties among the NBA and Euroleague have diminished and are in any case diminishing. Contrasts are considerably more modest when we manage for amusement length and keeping in mind that season finisher NBA ball is thought about rather than typical season b-ball. Keywords: 2D human posture assessment, 2D foot keypoint assessment, open posture, part liking fields
Cob: a leaderless protocol for parallel Byzantine agreement in incomplete networks

Abstract In this paper we extend the Multidimensional Byzantine Agreement (MBA) Protocol , a leaderless Byzantine agreement for lists of arbitrary values, into a protocol suitable for wide gossiping networks: Cob . This generalization allows the consensus process to be run by an incomplete network of nodes provided with (non-synchronized) same-speed clocks. Not all nodes are active in every step, so the network size does not hamper the efficiency, as long as the gossiping broadcast delivers the messages to every node in reasonable time. These network assumptions model more closely real-life communication channels, so Cob may be applicable to a variety of practical problems, such as blockchain platforms implementing sharding. Cob has the same Bernoulli-like distribution that upper-bounds the number of steps as the MBA protocol. We prove its correctness and security assuming a supermajority of honest nodes in the network, and compare its performance with Algorand.
Penggunaan Metode Card Sort pada Peningkatan Hasil Belajar Ilmu Pengetahuan Sosial

Card Sort is a collaborative activity that can be used to teach concepts, classification characteristics, facts about objects or review previously given knowledge. This method invites students to learn actively and aims to make students have an independent spirit while studying and foster creativity. Through this method, it is hoped that social studies learning that is less interesting and boring can become more interesting so that it can improve student learning outcomes according to the purpose of this study, namely to improve student learning outcomes using the card sort method. This research uses classroom action research method. The subjects of this study were students of class VIII A SMPN 1 Saptosari totaling 32 students, consisting of 16 boys and 16 girls. Data collection uses a learning outcomes test technique with 10 multiple choice questions. The results showed an increase in student learning outcomes as seen from the increase in the average score from 69.2 (pre-cycle) to 72.0 (cycle I), increasing to 74.5 (cycle II). Therefore, it can be said that the use of the card sort method can slightly add to the picture of the research problem.
Intensifying the spatial resolution of 3D thermal models from aerial imagery using deep learning-based image super-resolution

Nowadays, 3D thermal models can play an important role in buildings' energy management while acquiring multisource data to generate a high-resolution 3D thermal model. Consequently, in this article, a method for intensifying 3D thermal model using deep learning-based image super-resolution is presented. In the proposed method, first, the enhanced deep residual super-resolution (EDSR) deep network is re-trained based on thermal aerial images. Second, the resolution of low-resolution thermal images is enhanced using the newly trained network. Finally, the state-of-the-art structures from motion (SfM), semi global matching (SGM) and space intersection are utilized to generate intensified 3D thermal model from the resolution enhanced thermal images. Spatial evaluations indicate a 5% increase in edge-based image fusion metric (EFM) for the intensified 3D model. Besides, the evaluations show that the modulation transfer function (MTF) curves of the intensified 3D thermal model are closer to a reference model against the original 3D thermal model. HighlightsA 3D thermal model intensification solution using EDSR is proposed which is independent of hardware techniques and multisource data.Considering the importance of edge sharpness in the intensified 3D thermal model, the quality of edges is assessed using MTF curves and the EFM metric.In comparison to the original 3D thermal model, the MTF curves of the intensified 3D thermal model are closer to the MTF curve of the high-resolution 3D model.The EFM metric shows higher values for MTF curves of the intensified 3D thermal model against MTF curves of the original 3D thermal model.
Statistical model of adaptive system compensation of noise interference in the radar when detecting hydrometeorological formations in the atmosphere

A simulation model of an adaptive combined spatial-polarization system for detecting hydrometeorolog-ical formations in the atmosphere are created. The creation of this model is based on statistical modeling of both the electromagnetic field created by a useful signal and interference vibrations and the configu-rable parameters of the system under study. The laws of distribution of input random processes are de-scribed by Gaussian statistics. The adaptation of the weighting coefficients to the interference situation is carried out using a gradient algorithm. The dependences of the relative dispersion of the process at the output of the system under study on the number of the adaptation step are constructed, which indicate high efficiency of detecting the reflected signal from hydrometeors under the influence of noise interfer-ence arbitrarily oriented in space.
HOUDINI: Escaping from Moderately Constrained Saddles

We give the first polynomial time algorithms for escaping from high-dimensional saddle points under a moderate number of constraints. Given gradient access to a smooth function $f \colon \mathbb R^d \to \mathbb R$ we show that (noisy) gradient descent methods can escape from saddle points under a logarithmic number of inequality constraints. This constitutes the first tangible progress (without reliance on NP-oracles or altering the definitions to only account for certain constraints) on the main open question of the breakthrough work of Ge et al. who showed an analogous result for unconstrained and equality-constrained problems. Our results hold for both regular and stochastic gradient descent.
KOLEKSI REPOSITORI SEBAGAI SARANA DISEMINASI INFORMASI DI MASA PANDEMI

This study aims to answer questions about the media used in information dissemination during the pandemic, the types of collections contained in the repository, and the level of utilization of repository collections during the pandemic at the Library of UIN Sunan Ampel. The method used in this research is descriptive qualitative, namely conducting an in-depth study of symptoms that occur in a certain place and time using observation and documentation techniques on the object of research, that is the library repository on the website http://digilib.uinsby.ac.id. The research findings are as follows: 1) the media used as a means of disseminating information at the UIN Sunan Ampel Library are subscription e-books, subscription journals, UINSA books, and repositories. 2) There are 4 types of collections available in the repository, namely final and non-final assignments. The final project consists of a thesis, a thesis, and a dissertation. Meanwhile, non-final digital collections include scientific papers, journal articles, mass media articles, articles in books, books, textbooks, conferences, KKN/PKL reports, research reports, service reports, papers, scientific orations, guidelines, and proceedings. 3) The utilization rate of repository collections has increased during the pandemic compared to before. This can be seen from the number of page loads which increased by 86%, unique visits increased by 34%, first time visits increased by 60%, and returning visits increased by 6%.