Reviewerly/exHarmony huggingface dataset:
Recurrent Registration Neural Networks for Deformable Image Registration

Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain because the transformation of interest is not known in advance. As a consequence, not all basis functions will necessarily contribute to the final transformation which results in a non-compact representation of the transformation. We reformulate the pairwise registration problem as a recursive sequence of successive alignments. For each element in the sequence, a local deformation defined by its position, shape, and weight is computed by our recurrent registration neural network. The sum of all lo- cal deformations yield the final spatial alignment of both images. Formulating the registration problem in this way allows the network to detect non-aligned regions in the images and to learn how to locally refine the registration properly. In contrast to current non-sequence-based registration methods, our approach iteratively applies local spatial deformations to the images until the desired registration accuracy is achieved. We trained our network on 2D magnetic resonance images of the lung and compared our method to a standard parametric B-spline registration. The experiments show, that our method performs on par for the accuracy but yields a more compact representation of the transformation. Furthermore, we achieve a speedup of around 15 compared to the B-spline registration.
Raba tipično govorjenih diskurznih označevalcev na spletu

This article compares the usage of three speech-specific discourse markers in the Gos corpus of spoken Slovenian and the Janes corpus of user-generated content. The results confirm that the online usage of these expressions is much less common than in speech, but nevertheless significant, in particularly in platforms encouraging an interactive or dialogue-like communication. In online discourse, semantic-based functions of the markers prevail, such as relating to the addressee, checking for agreement or alleviating assertion, intertwined with discourse-structuring functions, such as emphasising or turn managing. All three markers also exhibit some new contexts of usage online, such as addressing an unknown/generic addressee, producing a specific stylistic effect and entering into new types of fixed multi-word expressions.
ELECTRONIC EXHIBITIONS OF ARCHIVAL DOCUMENTS AS AN INFORMATION RESOURCE OF THE REGION (EXAMPLE— THE STATE ARCHIVE OF MYKOLAIV REGION)

The purpose of the work. Explore the main types and content of electronic exhibitions of the archive as an information resource and a tool for the representation of historical heritage on the example of the analysis of the activities of the State Archive of Mykolaiv region.The research methodology. In the process of research were used scientific methods of analysis, synthesis, generalization, webometric analysis of the site. The scientific novelty of the work consists in the analysis of documentary online exhibitions of the state archive of the region, their classification, priority topics, features of the presentation of archival information, the use of interface capabilities. The content of the website of the State Archive of Mykolaiv region was analysed, in particular the sections “Documentary Online Exhibitions”, “Archive Activities”, “Publications on the Website”. Conclusions. As a result of the study, the importance and necessity of presenting documentary online exhibitions on archives sites, deciding on digitization of documents necessary for filling documentary exhibitions regarding the representation of archival information for remote customer service was justified. It has been proved that thematic electronic exhibitions of archival documents contribute to the availability of archival information for different segments of the population, self-education, increasing interest in the history and culture of the region.
Generic structure potential of rollingstone magazine cover february 9th, 2006 edition; critical multimodal discourse analysis

Rollingstone; February 9&lt;sup&gt;th&lt;/sup&gt;, 2006 edition was made because there is the meaning intended on it. The researcher uses critical multimodal discourse analysis from O’Halloran (2004), Kress and Van Leeuwen (2006), Halliday and Matthiessen (2014). The objectives of this study are to describe the generic structure potential, representational meaning, and the context of situation of Rollingstone; February 9&lt;sup&gt;th&lt;/sup&gt;, 2006 edition. The approach of this study is qualitative research and the type is document research. The researcher uses document from the picture of Rollingstone; February 9&lt;sup&gt;th&lt;/sup&gt;, 2006 edition which taken from Rollingstone’s official website. Analysis techniques flow model of this study are collecting the data, displaying and concluding. The results of this study are (1) There are some generic structure potential elements found. They are Lead, LoA, Comp. LoA, Display, Emblem, Announcement, Enhancer, Call and Visit Information and missing one element that is Tag. (2) The representational meaning of this magazine cover by applying narrative process for action process and conceptual representation for symbolic process. (3) The context of situation divided into field, tenor and mode. Field consists of The College Dropout Album, Jesus Walks Songs, 47th Grammy Awards, Rollingstone; February 9th, 2006 edition. Tenor consists of Kanye West as Song Writers, Rollingstone as production house and the target market. The mode consists of the word “The Passion of Kanye West”.
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models

In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preferences. User preferences on items tend to change over time due to a variety of factors such as change in context, the task being performed, or other short-term or long-term external factors. Recommender systems need to be able to capture these dynamics in user preferences in order to remain tuned to the most current interests of users. In this work we present a recommendation framework which takes into account the dynamics of user preferences. We propose an approach based on Hidden Markov Models (HMM) to identify change-points in the sequence of user interactions which reflect significant changes in preference according to the sequential behavior of all the users in the data. The proposed framework leverages the identified change points to generate recommendations using a sequence-aware non-negative matrix factorization model. We empirically demonstrate the effectiveness of the HMM-based change detection method as compared to standard baseline methods. Additionally, we evaluate the performance of the proposed recommendation method and show that it compares favorably to state-of-the-art sequence-aware recommendation models.
AKTIVITAS KOLABORASI DAN KEMAMPUAN MENGEMBANGKAN GAGASAN DALAM MENULIS KALIMAT IKLAN PADA PEMBELAJARAN BAHASA INDONESIA DI SMPN 3 CIBINONG KABUPATEN BOGOR

ABSTRACTThis research is based on student collaboration activities and the ability of students to develop ideas in writing advertising sentences. The formulation of the problem in this study is (1) How is the collaborative activity in learning Indonesian at Cibinong 3 Public Junior High School Bogor? (2) What is the result of the ability of students to develop ideas in writing advertising sentences in learning Indonesian at Cibinong 3 Public Junior High School Bogor? This study aims to describe: (1) collaborative activities on learning Indonesian at Bogor Cibinong 3 Junior High School (2) the results of students' ability to develop ideas in writing advertising sentences in Indonesian language learning at Bogor Cibinong 3 Junior High School. The method used in this research is the qualitative method. The results of this study in the form of collaborative activities on learning Indonesian at SMPN 3 Cibinong Bogor are as follows. a) identifying sentences of facts and opinions on products, b) giving ideas about sentences of facts and opinions, c) giving opportunities (taking turns, giving ideas about sentences of facts and opinions), d) discussing the answers of his group friends, e) utilizing time effectively, f) conveying ideas of facts and opinions in accordance with the product, and g) confirming the ideas of sentence facts and opinions recorded to group friends. The results of students' ability to develop ideas in writing ad sentences on learning Indonesian at Bogor Cibinong 3 Junior High School are good. This is based on data on the results of student skills, there are 32 data facts or correct sentences of 82.05% and 34 correct opinion sentence data or equal to 87.18%.Keywords: collaboration activities, advertising sentences, facts, opinions
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions

Motivated by the need to audit complex and black box models, there has been extensive research on quantifying how data features influence model predictions. Feature influence can be direct (a direct influence on model outcomes) and indirect (model outcomes are influenced via proxy features). Feature influence can also be expressed in aggregate over the training or test data or locally with respect to a single point. Current research has typically focused on one of each of these dimensions. In this paper, we develop disentangled influence audits, a procedure to audit the indirect influence of features. Specifically, we show that disentangled representations provide a mechanism to identify proxy features in the dataset, while allowing an explicit computation of feature influence on either individual outcomes or aggregate-level outcomes. We show through both theory and experiments that disentangled influence audits can both detect proxy features and show, for each individual or in aggregate, which of these proxy features affects the classifier being audited the most. In this respect, our method is more powerful than existing methods for ascertaining feature influence.
One-Shot Neural Architecture Search Through A Posteriori Distribution Guided Sampling.

The emergence of one-shot approaches has greatly advanced the research on neural architecture search (NAS). Recent approaches train an over-parameterized super-network (one-shot model) and then sample and evaluate a number of sub-networks, which inherit weights from the one-shot model. The overall searching cost is significantly reduced as training is avoided for sub-networks. However, the network sampling process is casually treated and the inherited weights from an independently trained super-network perform sub-optimally for sub-networks. In this paper, we propose a novel one-shot NAS scheme to address the above issues. The key innovation is to explicitly estimate the joint a posteriori distribution over network architecture and weights, and sample networks for evaluation according to it. This brings two benefits. First, network sampling under the guidance of a posteriori probability is more efficient than conventional random or uniform sampling. Second, the network architecture and its weights are sampled as a pair to alleviate the sub-optimal weights problem. Note that estimating the joint a posteriori distribution is not a trivial problem. By adopting variational methods and introducing a hybrid network representation, we convert the distribution approximation problem into an end-to-end neural network training problem which is neatly approached by variational dropout. As a result, the proposed method reduces the number of sampled sub-networks by orders of magnitude. We validate our method on the fundamental image classification task. Results on Cifar-10, Cifar-100 and ImageNet show that our method strikes the best trade-off between precision and speed among NAS methods. On Cifar-10, we speed up the searching process by 20x and achieve a higher precision than the best network found by existing NAS methods.
Determinants of Accounting Students’ Competency: Kuwait University

Kuwait University is the only state university with accounting education that has commenced in 1966. So far, accounting education is offered by the College of Business Administration – CBA- as a full degree of 4 years span, in addition to some other private universities that started recently. CBA periodically reviews the accounting program particularly after earning AACSB accreditation in the late 90’s, which has pressed for more academic excellence. CBA has introduced an exit exam process in 2009 since inception. Results revealed some areas of concern such as the shallow technical background of graduates, ill-use of advanced accounting systems and software, and inadequate research ability. One would add to these, student's tendency toward the classical type of learning or “spoon feeding”, which questions their ability in the judgmental type of cases as well as their self-expression.The purpose of this research is to explore factors associated with accounting student’s competencies at Kuwait University and subsequently to suggest remedy policies. A survey has been conducted through out the period from April 2018 till August 2018 exploring factors affecting accounting student’s competency. Multiple factors that influence student's competencies were entrenched within a statistical model which later resulted in comparable results. Moreover, stepwise multiple regression analysis was used to analyse the relationships between students' competency and the independent variables. Results expose that students lean heavily on in- class materials and do less reading in advance. Also, learning techniques used and practices by students are well classical as it depends primarily on lecturer delivering the materials. Finally, this study provides evidence that GPA earned by accounting students at CBA is the most important variable associated with their overall performance upon graduation. Other variables such as MGPA, external assistance (EA), in-class assignments (ICA), students' personal and technical abilities (PFT), and other financial and social factors (FT) have lesser degree of significance on their performance.
One approach in the time domain in detecting copy-move of speech recordings with the similar magnitude

In this study, we focused on copy-move detection in the content of audio files.We have many ways to identify a forged audio, in this article we focus on comparing the pitch of samples, compare the similarity of sound intensity in the time domain together.The goal of this method is to focus detecting copy-move speech in an audio file, which it has archived high speed processing and improved accuracy.
A Public Key Cryptosystem Using Cyclotomic Matrices

Confidentiality and Integrity are two paramount objectives in the evaluation of information and communication technology. In this chapter, we propose an arithmetic approach for designing asymmetric key cryptography. Our method is based on the formulation of cyclotomic matrices correspond to a diophantine system. The strategy uses in cyclotomic matrices to design a one-way function. The result of a one-way function that is efficient to compute, however, is hard to process its inverse except if privileged information about the hidden entry is known. Also, we demonstrate that encryption and decryption can be efficiently performed with the asymptotic complexity of Oe2.373. Finally, we study the computational complexity of the cryptosystem.
Learning robust visual representations using data augmentation invariance

Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream. Yet, artificial and biological networks still exhibit important differences. Here we investigate one such property: increasing invariance to identity-preserving image transformations found along the ventral stream. Despite theoretical evidence that invariance should emerge naturally from the optimization process, we present empirical evidence that the activations of convolutional neural networks trained for object categorization are not robust to identity-preserving image transformations commonly used in data augmentation. As a solution, we propose data augmentation invariance, an unsupervised learning objective which improves the robustness of the learned representations by promoting the similarity between the activations of augmented image samples. Our results show that this approach is a simple, yet effective and efficient (10 % increase in training time) way of increasing the invariance of the models while obtaining similar categorization performance.
An assessment of performance appraisal satisfaction levels among physicians: Investigation from the healthcare sector in Qatar

Performance appraisal is an ongoing process between managers and employees.In fact, the fairer the process in designing the performance appraisal, the better the employee satisfaction.However, realizing fairness in performance appraisal process is a tedious task.The main objective of this study is to investigate the relationship between how employees may perceive fairness of performance appraisal system and how this would affect work performance and intention to leave.This investigation is likely to be executed among physicians working in the health sector in Qatar.In order to achieve this objective, a model is framed and investigated where about one hundred physicians respond to a questionnaire which was designed in order to assess the performance appraisal satisfaction.Statistical results show a partial positive relationship between organizational justice (interview, and outcome) and performance appraisal satisfaction.Moreover, partial positive relationship between performance appraisal satisfaction and work performance is statistically proven.Differently, a weak relationship is noticed between intention to leave and perceiving fairness in performance appraisal.
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction

While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.
Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learningbased image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time.
Automated Measuring Unit for Analysis of Thin Magnetic Film Ferroresonance Spectrum

This paper considers hardware and software for automated analysis of thin magnetic film ferroresonance spectrum to determine the rate of growth and decrease in ferroresonance spectrum (conversion factor parameter). The measuring element of automated system is the strip line which is short-circuited. The measuring unit is used to study Ni 80 Fe 20 permalloy films, with thickness of more than 300 A. Conversion factor, magnetic permeability μ, uniaxial magnetic anisotropy, ferromagnetic spectrum resonance line width, saturation magnetization are analyzed from 50 MHz to 2 GHz frequency range. The magnetic films are used to design frequency selective filters, phase shifters and weak-field magnetic sensors. The thin magnetic film sensors are high sensitive magnetometers, with wide frequency range, low dimensions and weight [1].
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection

With the rapid development in deep learning, deep neural networks have been widely adopted in many real-life natural language applications. Under deep neural networks, a pre-defined vocabulary is required to vectorize text inputs. The canonical approach to select pre-defined vocabulary is based on the word frequency, where a threshold is selected to cut off the long tail distribution. However, we observed that such simple approach could easily lead to under-sized vocabulary or over-sized vocabulary issues. Therefore, we are interested in understanding how the end-task classification accuracy is related to the vocabulary size and what is the minimum required vocabulary size to achieve a specific performance. In this paper, we provide a more sophisticated variational vocabulary dropout (VVD) based on variational dropout to perform vocabulary selection, which can intelligently select the subset of the vocabulary to achieve the required performance. To evaluate different algorithms on the newly proposed vocabulary selection problem, we propose two new metrics: Area Under Accuracy-Vocab Curve and Vocab Size under X\% Accuracy Drop. Through extensive experiments on various NLP classification tasks, our variational framework is shown to significantly outperform the frequency-based and other selection baselines on these metrics.
Micropayments for Trusted Vehicular Services using MOTIVE (video)

The connected and autonomous vehicles are expected to rely heavily on connectivity to exchange data and computation services with other vehicles and remote infrastructure including roadside units and other edge infrastructure to increase their immediate view, which leads to greater safety, coordination and more comfortable experience for their human occupants. In order for vehicles to obtain data, compute and other services from other vehicles or road-side infrastructure, it is important to be able to make micropayments for those services and for the services to run seamlessly despite the challenges posed by mobility and ephemeral interactions with a dynamic set of neighboring devices. We present MOTIVE, a trusted and decentralized framework that allows vehicles to make peer-to-peer micropayments for data, compute and other services obtained from other vehicles or road-side infrastructure within radio range. The framework utilizes distributed ledger technologies including smart contracts to enable autonomous operation and trusted interactions between vehicles and nearby entities.