Reviewerly/exHarmony huggingface dataset:
LEARNING MULTI-MODAL FEATURES FOR DENSE MATCHING-BASED CONFIDENCE ESTIMATION

Abstract. In recent years, the ability to assess the uncertainty of depth estimates in the context of dense stereo matching has received increased attention due to its potential to detect erroneous estimates. Especially, the introduction of deep learning approaches greatly improved general performance, with feature extraction from multiple modalities proving to be highly advantageous due to the unique and different characteristics of each modality. However, most work in the literature focuses on using only mono- or bi- or rarely tri-modal input, not considering the potential effectiveness of modalities, going beyond tri-modality. To further advance the idea of combining different types of features for confidence estimation, in this work, a CNN-based approach is proposed, exploiting uncertainty cues from up to four modalities. For this purpose, a state-of-the-art local-global approach is used as baseline and extended accordingly. Additionally, a novel disparity-based modality named warped difference is presented to support uncertainty estimation at common failure cases of dense stereo matching. The general validity and improved performance of the proposed approach is demonstrated and compared against the bi-modal baseline in an evaluation on three datasets using two common dense stereo matching techniques.
AOT: Anonymization by Oblivious Transfer

We thank David Chaum and Jonathan Katz for helpful comments. Sherman was supported in part by the National Science Foundation under SFS grant DGE-1753681, and by theU.S. Department of Defense under CySP grants H98230-19-1-0308 and H98230-20-1-0384.
3D acoustic recording

In this chapter, methods to capture ambient sound and music performances acoustically in 3D will be discussed. These methods will be classified into the following 5 types of systems: binaural, coincident (including sound field and beam-forming technologies), spaced microphone techniques, spot microphone, and hybrid systems. Up-mixing and down-mixing sound captured using these various methods will be discussed with regard to object based, scene based, binaural and XR application.
BASIC: Blockchain application and smart intelligent contract

As well known, a blockchain is said to be a database of distributed ledger or records in which all transactions or series of events that have been committed are distributed among several entities. The series of transactions in the public ledger is checked by consensus of a several participants in the whole system. The events can never be tampered once it is entered. The blockchain technology creates a system by establishing a consensus in the online world of digital era. In the current research work, emphasis has been laid on the concept of Smart Contract in BlockChain Technology, for which a BASIC: BlockChain Application and Smart Intelligent Contract based Algorithm has been proposed.
Parcel providing and Patrolling Hexacopter with High Payload

The different varieties of drones may be differentiated in terms of the sort (fixed-wing, multirotor, etc.), the degree of autonomy, the dimensions and weight, and also the power supply. These specifications are dynamic, as an example for the drone’s cruising vary, the utmost flight length, and also the loading capability. Apart from the drone itself (i.e., the ‘platform’) varied varieties of payloads may be distinguished, together with freight (e.g., mail parcels, medicines, hearth ending material, flyers, etc.) and differing kinds of sensors (e.g., cameras, sniffers, meteorological sensors, etc.). Applications of various payloads are described. So as to perform a flight, drones have a necessity for (a specific amount of) wireless communication with a pilot on the bottom. During this paper we tend to are introducing drone consists of six blades referred to as hexocoptor that's upraised and propelled by six motor will which may which might carry a load up to ten weight unit or can fly up to thirty minute with payload. It contain twilight vision camera and GPS controller is employed for remote location in night. During this system, brushless DC motors are adjoined with the electronic speed controller.
Contrasting Groups Analysis of TOEFL® iBT Test Cut Scores and the Common European Framework of Reference (CEFR) Proficiency Levels: Kernel Density Estimation of an English Learners’ Corpus

Placing non-native speakers of English into appropriate classes involves mapping placement test scores onto proficiency levels based on predetermined cut scores. However, studies on how to set boundaries for different levels of proficiency have been lacking in the language testing literature. A top-down approach to standard setting in which a panel of experts set cut scores has dominated the typical standard setting procedure. A less utilized approach is to proceed bottom-up by clustering learners based on test scores. The purpose of this study was to fill this gap by examining Education Testing Services (ETS)’s mapping of TOEFL® iBT Test scores to the Common European Framework of Reference (CEFR) levels. The study examined TOEFL® iBT score data from ICNALE (International Corpus Network of Asian Learners of English) and conducted optimal Kernel Density Estimation to find peaks in the distribution of test scores. In addition to the number of peaks, the local minima of the resulting distribution were chosen as boundaries of cut scores for delineating different ability groups. This method of separating scores, also known as contrasting groups, finds clusters of test takers based on maximum differences in scores. The results showed that ETS’ guide for cut scores linking to CEFR levels was comparable to Kernel Density Estimation with some exceptions, namely two out of three cut scores were found to be similar. Implications are discussed in terms of test-centered versus examinee-centered method of standard setting and the need to consider the demographics of the examinee population in determining cut scores.
Avoiding Overlap in Data Augmentation for AMR-to-Text Generation

Leveraging additional unlabeled data to boost model performance is common practice in machine learning and natural language processing.For generation tasks, if there is overlap between the additional data and the target text evaluation data, then training on the additional data is training on answers of the test set.This leads to overly-inflated scores with the additional data compared to real-world testing scenarios and problems when comparing models.We study the AMR dataset and Gigaword, which is popularly used for improving AMRto-text generators, and find significant overlap between Gigaword and a subset of the AMR dataset.We propose methods for excluding parts of Gigaword to remove this overlap, and show that our approach leads to a more realistic evaluation of the task of AMR-to-text generation.Going forward, we give simple bestpractice recommendations for leveraging additional data in AMR-to-text generation. 1
SISTEM INFORMASI PROGRAM MAHASISWA WIRAUSAHA UNIVERSITAS NEGERI PADANG

Entrepreneurship plays an important role in Indonesia's development. Entrepreneurship is important because of the magnitude of the role played by entrepreneurs in overcoming various problems of national economic development such as poverty alleviation, high unemployment, the Entrepreneurial Student Program is a program of the Ministry of Education and Culture's Directorate General of Higher Education implemented and developed by universities. Padang State University is one of the universities that has received assistance from the Directorate General of Higher Education. The Entrepreneurial Student Program Information System built has a web-based display, where with this information system students or other users can access anytime and anywhere to get information about the Entrepreneurial Student Program. For proposers, they can input their proposals directly through this information system and facilitate data storage for managers of the Entrepreneurial Student Program. This system is built using the Yii2 Framework, a component-based PHP programming that has high performance for large-scale web application development. Keywords: Information Systems, Entrepreneurial Student Programs, Yii2 Framework
BEyond observation: an approach for ObjectNav

With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics that encompasses embedded systems, control theory, path planning, Simultaneous Localization and Mapping (SLAM), scene reconstruction, and pattern recognition. In this work, we present our exploratory research of how sensor data fusion and state-of-the-art machine learning algorithms can perform the Embodied Artificial Intelligence (E-AI) task called Visual Semantic Navigation. This task, a.k.a Object-Goal Navigation (ObjectNav) consists of autonomous navigation using egocentric visual observations to reach an object belonging to the target semantic class without prior knowledge of the environment. Our method reached fourth place on the Habitat Challenge 2021 ObjectNav on the Minival phase and the Test-Standard Phase.
DEVELOPING MULTIMEDIA-BASED ENGLISH LISTENING MATERIAL FOR ISLAMIC SENIOR HIGH SCHOOL

This study aims at designing and developing multimedia-based English listening material for the first year students of an Islamic Senior High School. The study was a Research and Development (R&amp;D) using ADDIE model. The respondents were 77 students and 4 English teachers of the first-year students of MAN 1 Kerinci. The data of this research were obtained through observation checklist, interviews, questionnaires, validation sheet, and checklist of the product practicality. The findings of product validation and try-out show that the product were interesting and easy to use with minor revisions. Further, it facilitates the students to have self-learning listening. The final product of the study is an executable file (* exe) attached on the CD. The result of the product validation, as well as the try-out, indicates that the material developed is categorized as "valid". The practicality of the material based on teachers and students’ responses indicates that the material developed is "practical" to (Nunan, 1999) use in the classroom.
Recurrent Coupled Topic Modeling over Sequential Documents

The abundant sequential documents such as online archival, social media and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the multi-topic-thread evolution. Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward-forward filter algorithm efficiently learns latent timeevolving parameters in a closed-form. In addition, the latent Indian Buffet Process (IBP) compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction.
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent research on few-shot WSD using meta-learning. While such work has successfully applied meta-learning to learn new word senses from very few examples, its performance still lags behind its fully supervised counterpart. Aiming to further close this gap, we propose a model of semantic memory for WSD in a meta-learning setting. Semantic memory encapsulates prior experiences seen throughout the lifetime of the model, which aids better generalization in limited data settings. Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork. We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce (e.g. one-shot) scenarios and produces meaning prototypes that capture similar senses of distinct words.
A Deep Learning Model to Predict Congressional Roll Call Votes From Legislative Texts

Developments in natural language processing (NLP) techniques, convolutional neural networks (CNNs), and long-short- term memory networks (LSTMs) allow for a state-of-the-art automated system capable of predicting the status (pass/fail) of congressional roll call votes. The paper introduces a custom hybrid model labeled Predict Text Classification Network (PTCN), which inputs legislation and outputs a prediction of the document's classification (pass/fail). The convolutional layers and the LSTM layers automatically recognize features from the input data's latent space. The PTCN's custom architecture provides elements enabling adaptation to the input's variance from adjustment to the kernel weights over time. On the document level, the model reported an average evaluation of 67.32% using 10-fold cross- validation. The results suggest that the model can recognize congressional voting behaviors from the associated legislation's language. Overall, the PTCN provides a solution with competitive performance to related systems targeting congressional roll call votes.
Language-Agnostic Visual-Semantic Embeddings

We propose a framework for training language-invariant cross-modal retrieval models. We introduce four novel text encoding approaches, as well as a character-based word-embedding approach, allowing the model to project similar words across languages into the same word-embedding space. In addition, by performing cross-modal retrieval at the character level, the storage requirements for a text encoder decrease substantially, allowing for lighter and more scalable retrieval architectures. The proposed language-invariant textual encoder based on characters is virtually unaffected in terms of storage requirements when novel languages are added to the system. Contributions include new methods for building character-level-based word-embeddings, an improved loss function, and a novel cross-language alignment module that not only makes the architecture language-invariant, but also presents better predictive performance. Moreover, we introduce a module called \adapt, which is responsible for providing query-aware visual representations that generate large improvements in terms of recall for four widely-used large-scale image-text datasets. We show that our models outperform the current state-of-the-art all scenarios. This thesis can serve as a new path on retrieval research, now allowing for the effective use of captions in multiple-language scenarios.
SYNTHESIS: A Platform of Platforms for Integrated Management, Curation, and Visualization of Digital Cultural Experiences through VR and AR Technologies

SYNTHESIS is a platform of platforms (PoP) for the management, curation, and creation of digital cultural experiences related to cultural heritage material (artifacts), and their visualization through mobile applications and virtual and augmented reality (VR/AR) technologies. The digital cultural experiences may relate to specific cultural heritage monuments or exhibits of museum collections and may reside either on site or remotely. The platform provides a comprehensive supervisory tool that allows curators to select digital artifacts from different databases around the topic of interest, associate them through narratives that translate them into a sequence of semantic correlations that can be visualized using VR/AR technologies. The SYNTHESIS of platforms contributes to the creation of a new condition for the promotion of cultural heritage sites, museums, and exhibits, and facilitates the management and interaction of the user-visitor with them. The platform contributes to the creation of an innovative system for the management and curation of large volume of digital material related to an exhibit and consequently its promotion by unifying the stages of: (a) production of digital content; (b) connection of additional digital multimedia material related to the digital content; (c) curation of the digital content and transformation to a digital experience in accordance to a desired narrative; and (d) visualization of the digital experience in-situ or remotely with the use of mobile apps and VR/AR technologies.
Sistem Tracer Study dan Monitoring Alumni Universitas Pamulang

In the implementation of higher education, of course, the relevance of the curriculum is needed in improving the quality of learning. One of the efforts to fulfill this is through alumni tracking (tracer study). Tracer study is a strategy that allows higher education institutions to obtain input on adjusting the curriculum to current industry needs so that it can become the basis for future improvements. Lack of distribution and collection of questionnaires again became a problem for the tracer study team, because during its implementation there was little response from alumni and stakeholders. Therefore we need a system that can be used to collect and manage and present data accurately from the measurement results of the tracer study in the form of parameters such as work status, job relevance, level of importance of educational services, level of satisfaction of educational services, and the influence of thesis quality. As well as measuring the level of stakeholder satisfaction with alumni with parameters in the form of ethics and morals, expertise based on scientific fields, skills in communication, and teamwork. The results showed that the process of collecting and processing tracer study instruments according to the parameters required in the accreditation process was easier to do because it was website-based. Besides, it made it easier for alumni and stakeholders to fill out the questionnaire, so that more feedback was obtained by the tracer study team.
THE CORRELATION BETWEEN THE STUDENTSâ€™ VOCABULARY MASTERY AND THEIR WRITING SKILL OF THE TENTH GRADE STUDENTS OF SMA NEGERI 1 KERAJAAN IN THE ACADEMIC YEAR OF 2020/2021

Writing is one of the important skills in learningÂ English that should be acquired by the students. And also it is one of the most difficult skills to the students to b acquired in the learning English. One of the reasons is the lack of studentsâ€™ in vocabulary mastery. That is why this research was conducted in order to find out the correlation between studentsâ€™ vocabulary mastery and their writing skill at the tenth grade students of SMA Negeri 1 Kerajaan in the Academic Year of 2020/2021 which is consist of four classes. The writer took 53 students as the sample of this research by whose students numbers are odd randomly. The instrument in collecting data is the test. The tests were used to collect the data of the studentsâ€™ vocabulary mastery and their writing skill. The techniques which were used to analyze the data are normality test, Linear Regression Analysis Statistic and Pearson Product Moment by using Statistical Product and Service Solution (SPSS). The result of hypothesis test showed that the coefficient of correlation is 0,638 at the significant level 0,5%. Since the rcount is higher than rtable (0,638&gt;0,279) it means that Ho is rejected. It can be concludedthat there is a significant correlation between studentsâ€™ vocabulary mastery and their writing skill at the tenth grade students of SMA Negeri 1 Kerajaan in the Academic Year of 2020//2021.
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild

Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data. As an alternative approach, recent methods rely on limited supervision to disentangle the factors of variation and allow their identifiability. While annotating the true generative factors is only required for a limited number of observations, we argue that it is infeasible to enumerate all the factors of variation that describe a real-world image distribution. To this end, we propose a method for disentangling a set of factors which are only partially labeled, as well as separating the complementary set of residual factors that are never explicitly specified. Our success in this challenging setting, demonstrated on synthetic benchmarks, gives rise to leveraging off-the-shelf image descriptors to partially annotate a subset of attributes in real image domains (e.g. of human faces) with minimal manual effort. Specifically, we use a recent language-image embedding model (CLIP) to annotate a set of attributes of interest in a zero-shot manner and demonstrate state-of-the-art disentangled image manipulation results.
THE CONTENT OF PEDAGOGICAL DISCIPLINES AIMED AT CONTINUOUS DIDACTICAL TRAINING OF UNIVERSITY STUDENTS

The content of pedagogical disciplines aimed at intensifying didactic training is analyzed. The methodology for intensifying the didactic training of future bachelor teachers has been substantiated.
Use of Mobile Devices for Accessing E-books by Medical College Students in Karnataka: A Study

The present paper assessed the usage of technical tools offering updates in medical information through various medical devices, database, apps, etc. The advancement in learning process aided by newly designed tools, viz. various devices, applications and databases are now within the reach of students. The new trend of dealing with e-books, audio books, etc., is an asset to the spectrum of education. Especially, students pursuing professional courses like medicine, etc., should make the maximum use of these new peripherals in technology. Nowadays, a change in reading pattern has been observed – a shift from traditional books to e-books and audio books. This paper significantly brings out the usage pattern of various mobile devices, database meant for medical education and various apps for e-book reader and audio books. The data from 1150 students belonging to 16 medical colleges in Karnataka were obtained through questionnaire, interview and observation techniques and the findings were elaborated with the help of suitable statistical techniques.
A note on $\overline{2}$-separable codes and $B_2$ codes

We derive a simple proof, based on information theoretic inequalities, of an upper bound on the largest rates of $q$-ary $\overline{2}$-separable codes that improves recent results of Wang for any $q\geq 13$. For the case $q=2$, we recover a result of Lindström, but with a much simpler derivation. The method easily extends to give bounds on $B_2$ codes which, although not improving on Wang's results, use much simpler tools and might be useful for future applications.