Reviewerly/exHarmony huggingface dataset:
FrameNet-assisted Noun Compound Interpretation

Given a noun compound (NC), we address the problem of predicting the appropriate semantic label linking the constituents of the NC.This problem is called Noun Compound Interpretation (NCI).We use FrameNet as a semantic label repository.For example, given the noun compound (board approval), we predict the frame (DENY OR GRANT PERMISSION, as per FrameNet) as appropriate and the semantic role of the modifier word (AUTHORITY) as the semantic label linking board and approval; the resulting label is DENY OR GRANT PERMISSION:AUTHORITY.Our semantic label repository is very large (≈ 11k labels) compared to the NC data available for training (approx 1900).Thus, learning in this case, especially for unseen semantic labels, is hard.We propose to solve this problem by predicting semantic labels in a continuous label embedding space, which is novel.This embedding space is created by learning label embeddings using the FrameNet data.The embeddings are then used to train two separate models -one for predicting Frames and the other for FEs.As the label embedding space captures the semantics of the labels, using these embeddings enables generalizing well on unseen labels, thus achieving zero-shot learning.Our preliminary investigations show that the proposed approach performs well for unseen labels, achieving 5% and 2% points improvements over baselines for the frame and FE prediction, respectively.The study shows the promise of the use of continuous space embeddings for noun compound interpretation and points to the need for further investigation.
An Empirical Study on the Issues of Traditional Defect Life Cycle in Agile Model

In an Agile workflow, work is divided into sprints and each sprint has a particular activity which needs to be completed. Agile aligns development activity with customer’s requirement. It is an iterative progress methodology, where requirements develop through association between the customer and self-organizing teams. As there is less documentation available in agile methodology, product team, development team and testing team need to work in close association to develop the quality software with minimum defect leakage. In this paper, we analyze empirically the defect life cycle for agile model to determine the correct phases of a defect in the agile workflow. Results obtained may help team to optimize the time and develop the quality software with minimum defect leakage.
Mengkaji Pustaka (Literature Review)

Indonesian Abstract: Penelitian dalam bahasa Inggris (English) adalah” research”. Kata tersebut memiliki kata dasar” search” yang mendapatkan” prefix re”. Kata” search” artinya cari, setelah mendapatkan” prefix re”, maka ”re-search” menjadi bermakna pencarian ulang. Namun pencarian ulang tersebut harus dilakukan dengan cara-cara ilmiah, disusun dengan mengacu pada sejumlah kaidah-kaidah ilmiah sesuai kebutuhannya, serta melibatkan penelitian-penelitian yang sudah pernah dilakukan.English Abstract: Research in English (English) is research. The word has the root word search which gets re prefix. The word search means search, after getting the re-prefix, then re-search becomes a re-search. However, the research must be carried out in scientific ways, compiled by referring to a number of scientific principles according to their needs, and involving studies that have already been carried out.
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP.

Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose UXLA, a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios. In particular, UXLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language. At its core, UXLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on three diverse zero-resource cross-lingual transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to its success.
Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices

Deep learning based image recognition systems have been widely deployed on mobile devices in today's world. In recent studies, however, deep learning models are shown vulnerable to adversarial examples. One variant of adversarial examples, called adversarial patch, draws researchers' attention due to its strong attack abilities. Though adversarial patches achieve high attack success rates, they are easily being detected because of the visual inconsistency between the patches and the original images. Besides, it usually requires a large amount of data for adversarial patch generation in the literature, which is computationally expensive and time-consuming. To tackle these challenges, we propose an approach to generate inconspicuous adversarial patches with one single image. In our approach, we first decide the patch locations basing on the perceptual sensitivity of victim models, then produce adversarial patches in a coarse-to-fine way by utilizing multiple-scale generators and discriminators. The patches are encouraged to be consistent with the background images with adversarial training while preserving strong attack abilities. Our approach shows the strong attack abilities in white-box settings and the excellent transferability in black-box settings through extensive experiments on various models with different architectures and training methods. Compared to other adversarial patches, our adversarial patches hold the most negligible risks to be detected and can evade human observations, which is supported by the illustrations of saliency maps and results of user evaluations. Lastly, we show that our adversarial patches can be applied in the physical world.
Outlier Detection in High Dimensional Data

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.
Semantic Parsing Natural Language into Relational Algebra

Natural interface to database (NLIDB) has been researched a lot during the past decades. In the core of NLIDB, is a semantic parser used to convert natural language into SQL. Solutions from traditional NLP methodology focuses on grammar rule pattern learning and pairing via intermediate logic forms. Although those methods give an acceptable performance on certain specific database and parsing tasks, they are hard to generalize and scale. On the other hand, recent progress in neural deep learning seems to provide a promising direction towards building a general NLIDB system. Unlike the traditional approach, those neural methodologies treat the parsing problem as a sequence-to-sequence learning problem. In this paper, we experimented on several sequence-to-sequence learning models and evaluate their performance on general database parsing task.
Impossibility of composable Oblivious Transfer in relativistic quantum cryptography

We study the cryptographic primitive Oblivious Transfer; a composable construction of this resource would allow arbitrary multi-party computation to be carried out in a secure way, i.e. to compute functions in a distributed way while keeping inputs from different parties private. First we review a framework that allows us to analyze composability of classical and quantum cryptographic protocols in special relativity: Abstract Cryptography implemented with Causal Boxes. We then (1) explore and formalize different versions of oblivious transfer found in the literature, (2) prove that their equivalence holds also in relativistic quantum settings, (3) show that it is impossible to composably construct any of these versions of oblivious transfer from only classical or quantum communication among distrusting agents in relativistic settings, (4) prove that the impossibility also extends to multi-party computation, and (5) provide a mutual construction between oblivious transfer and bit commitment.
Crossref 4.4.2 XML Elements and Attributes

For anyone trying to understand both the basics and the full range of options available when making a DOI metadata submission to Crossref, this linked table of XML element and attribute descriptions gives one small publisher’s best understanding of the most recent version of Crossref’s metadata submission elements and attributes. As of April 2021, the most recent version of Crossref XML files is 4.4.2. This table provides definitions for the six Crossref XML Schema Definition (xsd) files that include the most commonly used description elements of a DOI submission: crossref4.4.2.xsd, common4.4.2.xsd, fundref.xsd, AccessIndicators.xsd, clinicaltrials.xsd, and relations.xsd. The table also includes a brief description of the main features of the externally defined jats:abstract (JATS) element. This table focuses not on XML syntax but on the intent and structure of the elements from a small publisher perspective. This table is one small publisher’s interpretation of Crossref XML and is not authoritative in any way. It will inevitably contain errors, and the author takes no responsibility for its use, which is necessarily and entirely at your own risk. Any submissions created with information from this table should be verified for correctness against the official automated documentation and tools at the Crossref submission site. Note, however, that occasional errors and inconsistencies in those Crossref XML files were uncovered during the creation of this table. Every effort has been made here both to document inconsistencies in the original files and in this interpretation of those files. Important links to Crossref documentation, including comment on the apparent status of Crossref web pages, are provided in the References section after the table on the last document page.
Multi-modal Graph Learning for Disease Prediction

Benefiting from the powerful expressive capability of graphs, graph-based approaches have achieved impressive performance in various biomedical applications. Most existing methods tend to define the adjacency matrix among samples manually based on meta-features, and then obtain the node embeddings for downstream tasks by Graph Representation Learning (GRL). However, it is not easy for these approaches to generalize to unseen samples. Meanwhile, the complex correlation between modalities is also ignored. As a result, these factors inevitably yield the inadequacy of providing valid information about the patient's condition for a reliable diagnosis. In this paper, we propose an end-to-end Multimodal Graph Learning framework (MMGL) for disease prediction. To effectively exploit the rich information across multi-modality associated with diseases, amodal-attentional multi-modal fusion is proposed to integrate the features of each modality by leveraging the correlation and complementarity between the modalities. Furthermore, instead of defining the adjacency matrix manually as existing methods, the latent graph structure can be captured through a novel way of adaptive graph learning. It could be jointly optimized with the prediction model, thus revealing the intrinsic connections among samples. Unlike the previous transductive methods, our model is also applicable to the scenario of inductive learning for those unseen data. An extensive group of experiments on two disease prediction problems is then carefully designed and presented, demonstrating that MMGL obtains more favorable performances. In addition, we also visualize and analyze the learned graph structure to provide more reliable decision support for doctors in real medical applications and inspiration for disease research.
Special Section Guest Editorial: Image and Video Compression Using Deep Neural Networks

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.
RefactorHub: A Commit Annotator for Refactoring

It is necessary to gather real refactoring instances while conducting empirical studies on refactoring. However, existing refactoring detection approaches are insufficient in terms of their accuracy and coverage. Reducing the manual effort of curating refactoring data is challenging in terms of obtaining various refactoring data accurately. This paper proposes a tool named RefactorHub, which supports users to manually annotate potential refactoring-related commits obtained from existing refactoring detection approaches to make their refactoring information more accurate and complete with rich details. In the proposed approach, the parameters of each refactoring operation are defined as a meaningful set of code elements in the versions before or after refactoring. RefactorHub provides interfaces and supporting features to annotate each parameter, such as the automated filling of dependent parameters, thereby avoiding wrong or uncertain selections. A preliminary user study showed that RefactorHub reduced annotation effort and improved the degree of agreement among users. Source code and demo video are available at https://github.com/salab/RefactorHub
How to Verify Your Python Conversations

In large-scale distributed systems, each application is realised through interactions among distributed components.To guarantee safe communication (no deadlocks and communication mismatches) we need programming languages and tools that structure, manage, and policy-check these interactions.Multiparty session types (MSPT), a typing discipline for structured interactions between communicating processes, offers a promising approach.To date, however, session types applications have been limited to static verification, which is not always feasible and is often restrictive in terms of programming API and specifying policies.This chapter investigates the design and implementation of a runtime verification framework, ensuring conformance between programs and specifications.Specifications are written in Scribble, a protocol description language formally founded on MPST.The central idea of the approach is a runtime monitor, which takes a form of a communicating finite state machine, automatically generated from Scribble specifications, and a communication runtime stipulating a message format.We demonstrate Scribble-based runtime verification in manifold ways.First, we present a Python library, facilitated with session primitives and verification runtime.Second, we show examples from a large cyber-infrastructure project for oceanography, which uses the library as a communication medium.Third, we examine communication patterns, featuring advanced Scribble primitives for verification of exception handling behaviours.
An Application of Machine Learning Strategies to Predict Alzheimer’s Illness Progression in Patients

Alzheimer’s disease (AD) is a terminal neuro-degenerative disorder that's characterized by the build-up of amyloid plaques and neuro-fibrillary tangles within the central nervous system. However, certain individuals remain cognitively intact despite the manifestation of considerable plaques and tangles in keeping with what would be normally related to fully symptomatic AD. Brain imaging via magnetic resonance imaging (MRI), is employed for the evaluation of patients with suspected AD. MRI findings include both local and generalized shrinkage of brain tissue. Mechanisms that allow these subjects to escape dementia remain unresolved and understanding such protective biological processes could reveal novel targets for the development of effective treatments for AD. In this article, we propose to develop a sound model which will help clinicians do this and predict early Alzheimer’s.
Content-Aware Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverses the input images/features using a sliding window scheme to extract features. However, not all the windows contribute equally to the prediction results of CNNs. In practice, the convolutional operation on some of the windows (e.g., smooth windows that contain very similar pixels) can be very redundant and may introduce noises into the computation. Such redundancy may not only deteriorate the performance but also incur the unnecessary computational cost. Thus, it is important to reduce the computational redundancy of convolution to improve the performance. To this end, we propose a Content-aware Convolution (CAC) that automatically detects the smooth windows and applies a 1x1 convolutional kernel to replace the original large kernel. In this sense, we are able to effectively avoid the redundant computation on similar pixels. By replacing the standard convolution in CNNs with our CAC, the resultant models yield significantly better performance and lower computational cost than the baseline models with the standard convolution. More critically, we are able to dynamically allocate suitable computation resources according to the data smoothness of different images, making it possible for content-aware computation. Extensive experiments on various computer vision tasks demonstrate the superiority of our method over existing methods.
Políticas educativas en Latinoamérica ante los retos de la pandemia: Educational policies in Latin America facing the challenges of the pandemic

ResumenExisten muchos retos en el siglo XXI para la formacion del ser humano, en vista que, desde el nivel inicial hasta los estudios universitarios, esta inmerso el uso de tecnologias educativas, donde las politicas educativas juegan un papel preponderante, es por ello que esta investigacion tiene como objetivo general analizar las politicas educativas en Latinoamerica ante los retos de la pandemia. El estudio se aborda bajo el paradigma cuantitativo, tipo de investigacion documental y diseno documental o bibliografico, cuyas conclusiones preliminares implican que se deben elaborar politicas educativas que consideren la desigualdad de la poblacion en variables de edad, genero, ubicacion geografica, capacidad financiera, personas con discapacidad, lengua, nivel educativo y apoyo por parte del gobierno. Palabras claves: formacion, gobierno, pandemia, politicas educativas, retos, tecnologia. AbstractThere are many challenges in the XXI century for the formation of the human being, given that, from the initial level to university studies, the use of educational technologies is immersed, where educational policies play a preponderant role, which is why this research Its general objective is to analyze educational policies in Latin America in the face of the challenges of the pandemic. The study is approached under the quantitative paradigm, type of documentary research and documentary or bibliographic design, whose preliminary conclusions imply that educational policies must be developed that consider the inequality of the population in variables of age, gender, geographic location, financial capacity, people with disabilities, language, educational level and support from the government. Keys words: training, government, pandemic, policies educational, challenges, technology.
Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition

Chinese character recognition has attracted much research interest due to its wide applications. Although it has been studied for many years, some issues in this field have not been completely resolved yet, e.g. the zero-shot problem. Previous character-based and radical-based methods have not fundamentally addressed the zero-shot problem since some characters or radicals in test sets may not appear in training sets under a data-hungry condition. Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters. However, we observe that there is a one-to-many relationship between stroke sequences and Chinese characters. To tackle this challenge, we employ a matching-based strategy to transform the predicted stroke sequence to a specific character. We evaluate the proposed method on handwritten characters, printed artistic characters, and scene characters. The experimental results validate that the proposed method outperforms existing methods on both character zero-shot and radical zero-shot tasks. Moreover, the proposed method can be easily generalized to other languages whose characters can be decomposed into strokes.
Going virtual with the public library “ivan goran kovaČiĆ” karlovac – programs for all ages

Purpose. The paper presents the Public Library “Ivan Goran Kovacic” Karlovac online services, which enabled more efficient work in a pandemic circumstances caused by the COVID-19 virus. Virtual programs that have been held from March 2020 until today are also presented in more detail. The aim of this paper is to show possibility of continuing the production of library programs in exceptional circumstances as well as to present a new way of organizing library operations. Approach. The first part of the paper analyzes the theoretical framework of the library work at the global and national level in the circumstances of the pandemic of the COVID-19 virus. The second part of the paper provides a detailed overview of the Public Library “Ivan Goran Kovacic” Karlovac virtual services and programs for all ages, which include literary meetings, creative workshops, presentation of online library services and user education, quizzes, virtual exhibitions, and reading recommendations. The results of the user satisfaction survey with the mentioned services and programs are also presented. Results. The review of innovative programs and services designed to encourage reading, develop creativity and acquire new skills in the new circumstances shows how the Public Library “Ivan Goran Kovacic” Karlovac in its work continuously seeks to redefine the role of the library in light of upcoming changes and how its approach contributes to local community development. Originality. The examples of good practice in the Public Library “Ivan Goran Kovacic” Karlovac emphasize the importance of changing the way of working as well as the way of thinking in the age of coronavirus and can serve as a model for other libraries. © 2021, Hrvatsko Knjiznicarsko Drustvo. All rights reserved.