Reviewerly/exHarmony huggingface dataset:
Point Clouds With Color: A Simple Open Library for Matching RGB and Depth Pixels from an Uncalibrated Stereo Pair

Current day robots often rely - for visual perception - on the coupling of two cameras: one for color and one for depth. While for custom RGB-D cameras, the manufacturer takes care of aligning the two images, this is not done when two commercial cameras are coupled (e.g., on the Pepper robot) without having been calibrated beforehand. In this article, we present a simple open library for reconstructing the 3D position of RGB pixels without knowing the parameters of the two cameras. The library requires a simple preliminary calibration step based on pixel-to-pixel matching, and then automatically reconstructs 3D colored point clouds from a given set of pixels in the RGB image. The source code is available at the following link https://github.com/jordan-nowak/OpenHSML.
Personalized Benchmarking with the Ludwig Benchmarking Toolkit

The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g. industry practitioners) which seek to benchmark models across tasks and objectives of personal value. Unfortunately, these users cannot use standard benchmark results to perform such value-driven comparisons as traditional benchmarks evaluate models on a single objective (e.g. average accuracy) and fail to facilitate a standardized training framework that controls for confounding variables (e.g. computational budget), making fair comparisons difficult. To address these challenges, we introduce the open-source Ludwig Benchmarking Toolkit (LBT), a personalized benchmarking toolkit for running end-to-end benchmark studies (from hyperparameter optimization to evaluation) across an easily extensible set of tasks, deep learning models, datasets and evaluation metrics. LBT provides a configurable interface for controlling training and customizing evaluation, a standardized training framework for eliminating confounding variables, and support for multi-objective evaluation. We demonstrate how LBT can be used to create personalized benchmark studies with a large-scale comparative analysis for text classification across 7 models and 9 datasets. We explore the trade-offs between inference latency and performance, relationships between dataset attributes and performance, and the effects of pretraining on convergence and robustness, showing how LBT can be used to satisfy various benchmarking objectives.
Книги и брошюры о библиотечном деле, изданные в постсоветской России (книговедческий обзор 1992–2020 гг.)

The publishing business in post-Soviet Russia differs a lot from that of the Soviet period. The number of books and brochures has significantly increased, though the circulations have drastically decreased, which is evidenced by the national press statistics without interpreting indicators related to the subject. After USSR’s collapsed, many complete and reliable resources have appeared to study librarianship publishing repertoire in various aspects, e. g.: target readership of publications, their target audience, publishing chronology, main publishers; circulations. With these criteria, expert assessment of key publishing repertoire components is made. The Russian Book Chamber databases based on acquired mandatory copies of publications, as well as Rosinforkultura, Institute of scientific Information for Social Sciences of the Russian Academy of Sciences, largest library catalogs and several bibliographic catalogs and indexes on the subject under examination, make the main source of statistical and bibliographic information for the authors. The authors identify the main problems and trends in librarianship publishing so to change situation to the better. The authors also focus on publication identification elements, i. e. their imprint.
LibGuides: LibGuide for LSC 527: Week 13 Activity - Learning Reflection

Learning and teaching Digital Information Literacy Instruction Reflection of learning from this semester.
Learning Debiased and Disentangled Representations for Semantic Segmentation

Deep neural networks are susceptible to learn biased models with entangled feature representations, which may lead to subpar performances on various downstream tasks. This is particularly true for under-represented classes, where a lack of diversity in the data exacerbates the tendency. This limitation has been addressed mostly in classification tasks, but there is little study on additional challenges that may appear in more complex dense prediction problems including semantic segmentation. To this end, we propose a model-agnostic and stochastic training scheme for semantic segmentation, which facilitates the learning of debiased and disentangled representations. For each class, we first extract class-specific information from the highly entangled feature map. Then, information related to a randomly sampled class is suppressed by a feature selection process in the feature space. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes, and the model is able to learn more debiased and disentangled feature representations. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks, with especially notable performance gains on under-represented classes.
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space

As a basic component of SE(3)-equivariant deep feature learning, steerable convolution has recently demonstrated its advantages for 3D semantic analysis. The advantages are, however, brought by expensive computations on dense, volumetric data, which prevent its practical use for efficient processing of 3D data that are inherently sparse. In this paper, we propose a novel design of Sparse Steerable Convolution (SS-Conv) to address the shortcoming; SS-Conv greatly accelerates steerable convolution with sparse tensors, while strictly preserving the property of SE(3)-equivariance. Based on SS-Conv, we propose a general pipeline for precise estimation of object poses, wherein a key design is a Feature-Steering module that takes the full advantage of SE(3)-equivariance and is able to conduct an efficient pose refinement. To verify our designs, we conduct thorough experiments on three tasks of 3D object semantic analysis, including instance-level 6D pose estimation, category-level 6D pose and size estimation, and category-level 6D pose tracking. Our proposed pipeline based on SS-Conv outperforms existing methods on almost all the metrics evaluated by the three tasks. Ablation studies also show the superiority of our SS-Conv over alternative convolutions in terms of both accuracy and efficiency. Our code is released publicly at https://github.com/Gorilla-Lab-SCUT/SS-Conv.
Two Constitutional Cultures, Technological Enforcement and User Creativity: the Impending Collapse of the EU Copyright Regime?

In Pelham, the EU Court of Justice and the German Federal Constitutional Court reached diametrically opposing conclusions on the relevance of freedom of art in copyright law. The different stances permit a speculative prediction – they can have immediate consequences for the predictable challenges against the new platform liability regime under Article 17 DSMD, and its associated dangers of wide-spread filtering and blocking. The article discusses the numerous constitutional implications with specific attention given to the respective interests affected by the new regime (authors, exploiters, users, platforms) in light of the divergent approaches from the perspective of what appears to be two rather conflicting constitutional cultures: specific perceptions of fundamental rights and proportionality under German law versus an approach tending to emphasise market integration under the EU legal order. Recent assertions by the German Constitutional Court redevising the division of competences between national and EU law permit the prediction of a disturbing future collision course between the two systems, with potentially massive implications for EU copyright law by and large.
Theorising Corruption with a ‘Click’: Adopting Gioia Methodology

Understanding corruption in the cyber space warranted greater attention. What more, there are relatively limited studies in the literature that provide understanding of the phenomenon. The objective of the study is to provide understanding of corruption in the cyber space and how corruption risk management could assist in mitigating corruption in the cyber space. This study employed a Gioia methodology. Using Gioia methodology process of data collection, i.e. interviews as primary data collection, fourteen experts were interviewed to gain in-depth understanding of the phenomenon. Gioia data analysis process was used to provide understanding on the concept. This study found several important findings. First, cyber corruption is escalating in Malaysia. Second, Corruption Risk Management (CRM) is significant in curbing corruption activities in the cyber space. Third, there are three stages of CRM: 1) recognising corruption by a ‘click,’ 2) analysing the system, and 3) evaluation. The findings advance the knowledge on cybercrime, where it extended previous work on mitigating measures. As this is the first study that developed a CRM model using Gioia methodology, the findings contribute to giving novelty to fraud study, indicating a pathway to mitigate corruption in the digital environment.
BiSECT: Learning to Split and Rephrase Sentences with Bitexts

An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this `split and rephrase' task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations.
Social Networks Impact on Academic Libraries

Social Networking Sits (SNSs) are playing an increasingly important role as information sources for travelers.The goal of this study is to investigate Importance of Social Networking Sits in Libraries.The number of libraries which adopt SNSs is increasing to explore the respondent's attitude towards the use of social media in marketing of library resources and services.To provide possible implication of social media for marketing library and information products and services.To investigate the problems in utilizing social media for marketing purposes in libraries.The paper highlights the Purposes of Social Media Usage in Libraries, Purposes of Social Media Usage in Libraries, examples of Social Media websites, Social Networking Opportunities for Libraries, Impact of use of Social Networking Sits, Problems use of Social Networking Sits.
Introduction

Geology offers a unique perspective on the Earth—past, present, and future. It is the language through which the Earth speaks, by means of rocks, geologic processes, the geologic record, and the biography of the Earth. This book explains how geologists arrived at understanding the story of the planet and discusses the three major tenets of geology: geologic time, plate tectonics, and evolution. The development of theories about how the Earth functions is detailed, along with their relevance. Within geology are the keys to understanding the history of the Earth, how that past informs the present, and ways of going forward. The Earth has interacted with life as long as that life has been extant. Life has shaped the Earth and vice versa with times of flowering and the radiation of life forms and other times of extinctions through profound changes in the climate and positions of the continents. These dynamics can shed light on the current changes in and consequences of perturbing the global climate.
Audience Reliance on Satellite Channels to Listen and Promote jokes Field study in Baghdad 2017: DOI: 10.46988/IJMMC.02.02.2020.04

Humorous expressions in the societal, cultural repository represent one of the basics of understanding human societies. On the one hand, it can be considered an honest mirror of the values, beliefs, practices, and behaviors it carries. On the other hand, it reveals to us, directly or indirectly, the hidden aspects of society, and jokes are a humorous form transmitted by individuals. In societies by different means, including what is written, what is shared by the media, especially channels, and in public programs, jokes are the main focus of their episodes, which the audience adopts to alleviate their daily troubles and forget their everyday problems. Theoretical, while the third deals with field research procedures.
Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification

Scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Some existing works propose pipeline models on the three tasks of abstract retrieval, rationale selection and stance prediction. Such works have the problems of error propagation among the modules in the pipeline and lack of sharing valuable information among modules. We thus propose an approach, named as ARSJoint, that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information. In addition, we enhance the information exchanges and constraints among tasks by proposing a regularization term between the sentence attention scores of abstract retrieval and the estimated outputs of rational selection. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.
The Use of Monopoly Media in Social Students to Improve Motivation and Learning Outcomes of Elementary School Students

This study used the Classroom Action Research method which was aimed to know and describe the improving of motivation and learning outcomes through the use of monopolistic media in social studies subjects. The research subjects were 28th grade students of Katulampa 1 Public Elementary School, Bogor Timur subdistrict, Bogor City, totaling 28 students. Data collection techniques through observation, interviews and documentation. Data analysis techniques through qualitative descriptives. Based on the results of data analysis that has been done, the results in the first cycle of learning motivation gained 81 with good interpretation increased in cycle II gained 94 with Very Good interpretation, this shows an increase of 13 points. To improve the learning process in Cycle I, it was scored 87 with Very Good Interpretation and in Cycle II to 95 with Very Good Interpretation showed an increase of 8 points, in Cycle I learning outcomes gained 71% mastery learning with Unfinished interpretation and in Cycle II increased to 89% with Completed Interpretation. Whereas when seen from the average value for cycle 1, it obtained a value of 74 with Good criteria and in cycle II it became 86.5 indicating an increase of 12.5 points. From this study, it can be concluded that the use of monopoly media in social studies subjects can improve motivation and learning outcomes Students in elementary schools
Towards Live Video Analytics with On-Drone Deeper-yet-Compatible Compression

In this work, we present DCC(Deeper-yet-Compatible Compression), one enabling technique for real-time drone-sourced edge-assisted video analytics built on top of the existing codec. DCC tackles an important technical problem to compress streamed video from the drone to the edge without scarifying accuracy and timeliness of video analytical tasks performed at the edge. DCC is inspired by the fact that not every bit in streamed video is equally valuable to video analytics, which opens new compression room over the conventional analytics-oblivious video codec technology. We exploit drone-specific context and intermediate hints from object detection to pursue adaptive fidelity needed to retain analytical quality. We have prototyped DCC in one showcase application of vehicle detection and validated its efficiency in representative scenarios. DCC has reduced transmission volume by 9.5-fold over the baseline approach and 19-683% over the state-of-the-art with comparable detection accuracy.
Learning with convolution and pooling operations in kernel methods

Recent empirical work has shown that hierarchical convolutional kernels inspired by convolutional neural networks (CNNs) significantly improve the performance of kernel methods in image classification tasks. A widely accepted explanation for their success is that these architectures encode hypothesis classes that are suitable for natural images. However, understanding the precise interplay between approximation and generalization in convolutional architectures remains a challenge. In this paper, we consider the stylized setting of covariates (image pixels) uniformly distributed on the hypercube, and characterize exactly the RKHS of kernels composed of single layers of convolution, pooling, and downsampling operations. We use this characterization to compute sharp asymptotics of the generalization error for any given function in high-dimension. In particular, we quantify the gain in sample complexity brought by enforcing locality with the convolution operation and approximate translation invariance with average pooling. Notably, these results provide a precise description of how convolution and pooling operations trade off approximation with generalization power in one layer convolutional kernels.
Visual Cues and Error Correction for Translation Robustness

Neural Machine Translation models are sensitive to noise in the input texts, such as misspelled words and ungrammatical constructions. Existing robustness techniques generally fail when faced with unseen types of noise and their performance degrades on clean texts. In this paper, we focus on three types of realistic noise that are commonly generated by humans and introduce the idea of visual context to improve translation robustness for noisy texts. In addition, we describe a novel error correction training regime that can be used as an auxiliary task to further improve translation robustness. Experiments on English-French and English-German translation show that both multimodal and error correction components improve model robustness to noisy texts, while still retaining translation quality on clean texts.
The Witten index for one-dimensional split-step quantum walks under the non-Fredholm condition

It is recently shown that a split-step quantum walk possesses a chiral symmetry, and that a certain well-defined index can be naturally assigned to it. The index is a well-defined Fredholm index if and only if the associated unitary time-evolution operator has spectral gaps at both $+1$ and $-1.$ In this paper we extend the existing index formula for the Fredholm case to encompass the non-Fredholm case (i.e., gapless case). We make use of a natural extension of the Fredholm index to the non-Fredholm case, known as the Witten index. The aim of this paper is to fully classify the Witten index of the split-step quantum walk by employing the spectral shift function for a rank one perturbation of a fourth order difference operator. It is also shown in this paper that the Witten index can take half-integer values in the non-Fredholm case.
Do We Know What We Don't Know? Studying Unanswerable Questions beyond SQuAD 2.0.

Understanding when a text snippet does not provide a sought after information is an essential part of natural language utnderstanding. Recent work (SQuAD 2.0; Rajpurkar et al., 2018) has attempted to make some progress in this direction by enriching the SQuAD dataset for the Extractive QA task with unanswerable questions. However, as we show, the performance of a top system trained on SQuAD 2.0 drops considerably in out-of-domain scenarios, limiting its use in practical situations. In order to study this we build an out-of-domain corpus, focusing on simple event-based questions and distinguish between two types of IDK questions: competitive questions, where the context includes an entity of the same type as the expected answer, and simpler, non-competitive questions where there is no entity of the same type in the context. We find that SQuAD 2.0-based models fail even in the case of the simpler questions. We then analyze the similarities and differences between the IDK phenomenon in Extractive QA and the Recognizing Textual Entailments task (RTE; Dagan et al., 2013) and investigate the extent to which the latter can be used to improve the performance.
ConQuest: Contextual Question Paraphrasing through Answer-Aware Synthetic Question Generation

Despite excellent performance on tasks such as question answering, Transformer-based architectures remain sensitive to syntactic and contextual ambiguities.Question Paraphrasing (QP) offers a promising solution as a means to augment existing datasets.The main challenges of current QP models include lack of training data and difficulty in generating diverse and natural questions.In this paper, we present ConQuest, a framework for generating synthetic datasets for contextual question paraphrasing.To this end, ConQuest first employs an answer-aware question generation (QG) model to create a question-pair dataset and then uses this data to train a contextualized question paraphrasing model.We extensively evaluate ConQuest and show its ability to produce more diverse and fluent question pairs than existing approaches.Our contextual paraphrase model also establishes a strong baseline for end-to-end contextual paraphrasing.Further, We find that context can improve BLEU-1 score on contextual compression and expansion by 4.3 and 11.2 respectively, compared to a non-contextual model.
Rethinking the Objectives of Extractive Question Answering

This work demonstrates that using the objective with independence assumption for modelling the span probability P (a_s , a_e ) = P (a_s )P (a_e) of span starting at position a_s and ending at position a_e has adverse effects. Therefore we propose multiple approaches to modelling joint probability P (a_s , a_e) directly. Among those, we propose a compound objective, composed from the joint probability while still keeping the objective with independence assumption as an auxiliary objective. We find that the compound objective is consistently superior or equal to other assumptions in exact match. Additionally, we identified common errors caused by the assumption of independence and manually checked the counterpart predictions, demonstrating the impact of the compound objective on the real examples. Our findings are supported via experiments with three extractive QA models (BIDAF, BERT, ALBERT) over six datasets and our code, individual results and manual analysis are available online.