Reviewerly/exHarmony huggingface dataset:
Robustifying deep networks for image segmentation

Purpose: The purpose of this study is to investigate the robustness of a commonly-used convolutional neural network for image segmentation with respect to visually-subtle adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. Materials and Methods: In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas. A three-dimensional UNet model was implemented to segment four different MR series (T1-weighted, post-contrast T1-weighted, T2- weighted, and T2-weighted FLAIR) into four pixelwise labels (Gd-enhancing tumor, peritumoral edema, necrotic and non-enhancing tumor, and background). We developed attack strategies based on the Fast Gradient Sign Method (FGSM), iterative FGSM (i-FGSM), and targeted iterative FGSM (ti-FGSM) to produce effective attacks. Additionally, we explored the effectiveness of distillation and adversarial training via data augmentation to counteract adversarial attacks. Robustness was measured by comparing the Dice coefficient for each attack method using Wilcoxon signed-rank tests. Results: Attacks based on FGSM, i-FGSM, and ti-FGSM were effective in significantly reducing the quality of image segmentation with reductions in Dice coefficient by up to 65%. For attack defenses, distillation performed significantly better than adversarial training approaches. However, all defense approaches performed worse compared to unperturbed test images. Conclusion: Segmentation networks can be adversely affected by targeted attacks that introduce visually minor (and potentially undetectable) modifications to existing images. With an increasing interest in applying deep learning techniques to medical imaging data, it is important to quantify the ramifications of adversarial inputs (either intentional or unintentional).
UPAYA GURU DALAM MENINGKATKAN MINAT MEMBACA DAN MENULIS SISWA SDN PULAU RIMAU KABUPATEN BANYUASIN

This study aims to determine the efforts of teachers in increasing interest in reading and writing at Pulau Rimau Elementary School in Banyuasin Regency. The results of this study are expected to increase students' insight in increasing motivation to read and write. In addition, writing skills can stimulate students to think and reason more critically, and their courage to be active and interactive during the teaching and learning process, (2) for Indonesian language instructors. The results of this study are expected to contribute to teaching Indonesian at elementary school level to obtain knowledge and information about the motivation to read and write students so that teachers can improve the quality of education and teaching Indonesian language, especially reading and writing. The results of the research, findings and discussion of the teacher in increasing interest in reading and writing students at Pulau Rimau Elementary School Banyuasin Regency were obtained from the results of interviews and observations were analyzed using qualitative descriptive analysis techniques, and some material from the informants. Based on the results of the study, it can be concluded: The efforts of Indonesian language teachers in increasing interest in reading and writing students at Pulau Rimau Elementary School Banyuasin Regency have been running quite well from planning to the implementation process in the classroom.
Assisted Suicide in Italy: Constitutional right or wishful Thinking?

The Constitutional Court, in fact, has set for next September 24th the hearing for officially dealing with the constitutional legitimacy of the article of the criminal code that prohibits, in absolute terms, any activity of abetting suicide. However, already in a previous hearing, the Court anticipated that this prohibition is unconstitutional (order no. 207, October 24th, 2018). The case came before the Constitutional Court following the indictment of Mr Marco Cappato for having helped Mr Fabiano Antoniani (known as DJ Fabo) commit suicide.
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets

Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such representations becomes highly challenging in NLP tasks since the nuisance factor is entangled in a raw text. To our knowledge, a major issue is also that only few NLP datasets allow assessing the impact of such factor. In this paper, we introduce two generalization metrics to assess model robustness to a nuisance factor: \textit{generalization under target bias} and \textit{generalization onto unknown}. We combine those metrics with a simple data filtering approach to control the impact of the nuisance factor on the data and thus to build experimental biased datasets. We apply our method to standard datasets of the literature (\textit{Amazon} and \textit{Yelp}). Our work shows that a simple text classification baseline (i.e., sentiment analysis on reviews) may be badly affected by the \textit{product ID} (considered as a nuisance factor) when learning the polarity of a review. The method proposed is generic and applicable as soon as the nuisance variable is annotated in the dataset.
Trends in the Technology Start━Ups Integration in the Educational Process of Future Entrepreneurs

Cooperation of enterprises and universities in the field of scientific activity leads to an increase in the level of education, as well as the status of the educational institution, and contributes to a better employment of students. However, involvement in this cooperation may not be sufficient. That is why the purpose of this study was to assess the level of this involvement and form possible recommendations for its improvement. To conduct the study, a survey of 650 students from four Russian universities that have connections with various enterprises was organized. The survey showed that, despite the high awareness of the importance of such cooperation, students are not sufficiently involved in the joint research activities of universities and enterprises. To solve this problem, seminars with employees of companies can be organized, a system of encouragement for students, expansion of cooperation with foreign enterprises, as well as organization of financial support for projects of gifted students can be developed. The results and results of this article can be used for further research in this area.
LibGuides: Holiday of a lifetime: Maps

Where are you going? How are you going to get there? Here are some resources you might like to consider on your planning journey
Socio-Demographic Related Difference in Digital Literacy among Undergraduate Students of State universities in Iran

The concept of literacy has undergone a conceptual leap due to the constant growth of technology and the introduction of digital tools. Often-cited as an important component of modern literacy, digital literacy has received significant attention. Since the role of socio-demographic characteristics like gender, place of residence, access to digital tools, fields of study, level of university and previous experience with these tools is very decisive in digital literacy. The current study aimed at filling the gap that exists in the literature in this regard by examining the existence of any relationship between digital literacy level and the mentioned variables in the context of Iran. The study utilized quota sampling to select the respondents. The sample consisted of the undergraduate students of level 1 to level 4 universities. Two public universities from each level 1 to 4 and 8 in total contributed to the sample. The results indicated that all of the selected socio-demographic variables were significantly effective in digital literacy of students. The findings of this study, it is hoped, to provide insights to the education system of our country to recognize the areas of problem and introduce necessary programs in our education system either curricular or extracurricular to meet the required standards.
Word Sense Disambiguation using Diffusion Kernel PCA

One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is the task of computationally identifying the right sense of a polysemous word based on its context. Resolving the WSD problem boosts the accuracy of many NLP focused algorithms such as text classification and machine translation. In this paper, we introduce a new supervised algorithm for WSD, that is based on Kernel PCA and Semantic Diffusion Kernel, which is called Diffusion Kernel PCA (DKPCA). DKPCA grasps the semantic similarities within terms, and it is based on PCA. These properties enable us to perform feature extraction and dimension reduction guided by semantic similarities and within the algorithm. Our empirical results on SensEval data demonstrate that DKPCA achieves higher or very close accuracy results compared to SVM and KPCA with various well-known kernels when the labeled data ratio is meager. Considering the scarcity of labeled data, whereas large quantities of unlabeled textual data are easily accessible, these are highly encouraging first results to develop DKPCA further.
A Novel Virtual Reality Design of Portable Automatic Perimetry

Due to delayed diagnosis and treatment, millions of people, especially senior citizens, suffer from visual field loss even long-term blindness. However, many blindness could be prevented by popularizing the routine inspection of visual field. In this paper, we propose a novel virtual reality design of portable automatic perimetry. The virtual reality (VR) device provides a non-light and full-field environment. Combined with its portability, it makes external light shielded, and full-field examination feasible in normal clinic ward or outpatient department as a kind of regular physical examination. To make it easier to use, we also offered several automatic human-VR interaction modes, including using straightforward eye tracking to replace traditional mouse click or button press. Owing to more friendly interaction and better portability, the novel virtual reality design of portable automatic perimetry facilitates both the subjects and clinicians to improve the efficiency of routine inspection.
GAN Path Finder: Preliminary results

2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being -- to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.
New Approach of Automatic Modulation Classification based on in Phase-Quadrature Diagram Combined with Artificial Neural Network

Automatic Modulation Classification (AMC) with intelligent system is an attracting area of research due to the development of SDR (Software Defined Radio).This paper proposes a new algorithm based on a combination of k-means clustering and Artificial Neural Network (ANN).We use constellation diagram I-Q (In phase, Quadrature) as basic information.K-means algorithm is used to normalize data transmitted and pollute by the Additive White Gaussian Noise (AWGN), then the new diagram obtained is considered as an image and coded in pixel before entering in MLP (Multi-Layer Perceptron) Neural Network.Simulation results show an improvement of recognition rate under low SNR (Signal Noise Rate) compare to some results obtained in the literature.
ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task

Given many recent advanced embedding models, selecting pre-trained word embedding (a.k.a., word representation) models best fit for a specific downstream task is non-trivial. In this paper, we propose a systematic approach, called ETNLP, for extracting, evaluating, and visualizing multiple sets of pre-trained word embeddings to determine which embeddings should be used in a downstream task. For extraction, we provide a method to extract subsets of the embeddings to be used in the downstream task. For evaluation, we analyse the quality of pre-trained embeddings using an input word analogy list. Finally, we visualize the word representations in the embedding space to explore the embedded words interactively. We demonstrate the effectiveness of the proposed approach on our pre-trained word embedding models in Vietnamese to select which models are suitable for a named entity recognition (NER) task. Specifically, we create a large Vietnamese word analogy list to evaluate and select the pre-trained embedding models for the task. We then utilize the selected embeddings for the NER task and achieve the new state-of-the-art results on the task benchmark dataset. We also apply the approach to another downstream task of privacy-guaranteed embedding selection, and show that it helps users quickly select the most suitable embeddings. In addition, we create an open-source system using the proposed systematic approach to facilitate similar studies on other NLP tasks. The source code and data are available at https://github.com/vietnlp/etnlp.
Ielab at the open-source IR replicability challenge 2019

We present our participating Docker image in the SIGIR Open-Source IR Replicability Challenge 2019. Our participation includes an Elasticsearch-based Docker image conforming to the specifications laid out by the challenge, a collection parser, and a topic parser. Our Docker image is capable of indexing and searching several commonly used Information Retrieval test collections including robust04 (TREC 2004 Robust Track Topics - disks 4/5), core17 (TREC NewYork Times Annotated Corpus), and core18 (TRECWashington Post Corpus). The Docker image and associated tools we present in this paper provide a solid foundation for replicable Information Retrieval experiments using Elasticsearch.
La traducción del dialecto: La viuda descalza, de Salvatore Niffoi

Abstract – In recent years, the presence of dialect within the contemporary Italian novel has acquired an even greater role, thus becoming a characterizing element of its narrative fabric. This has revived the debate on the possibility of translating the dialect, on how to translate it and on the translation strategies that can be employed. The aim of the work is to analyze the possible strategies to translate from Italian into Spanish literary texts in which linguistic variation plays a fundamental role. The theorists describe various alternatives, which will be presented through some examples taken from the novel La Vedova Scalza (The barefoot widow) by Niffoi, a text in which it is possible to find a wide range of translation strategies. After a brief description of the novel, we will analyze the strategies adopted with the aim of making a modest contribution to the debate on the translatability of the dialect. It is a descriptive study of possible dialect translation solutions, in which empirical data and the trends followed to translate are analyzed. Resumen – En los ultimos anos, la presencia del dialecto en la novela italiana contemporanea ha ido adquiriendo un papel cada vez mayor, llegando a convertirse en un elemento que caracteriza su tejido narrativo. Esto ha avivado el debate sobre la posibilidad de traducir el dialecto, sobre como traducirlo y, en concreto, sobre las estrategias traductivas que pueden emplearse. El objetivo de este trabajo es analizar las posibles estrategias para traducir del italiano al espanol textos literarios en los que la variacion linguistica juega un papel fundamental. Los teoricos describen varias alternativas, que se presentaran mediante algunos ejemplos tomados de la novela La viuda descalza de Niffoi, texto en el que aparece un abanico amplio de estrategias traductivas. Tras una breve descripcion de la novela, se analizaran las estrategias de traduccion empleadas para aportar una modesta contribucion al debate sobre la traducibilidad del dialecto. Se trata de un estudio descriptivo sobre las posibles soluciones de traduccion del dialecto en el que se analizan datos empiricos y las pautas seguidas para traducir.
Analyzing Linguistic Complexity and Scientific Impact

The number of publications and the number of citations received have become the most common indicators of scholarly success. In this context, scientific writing increasingly plays an important role in scholars' scientific careers. To understand the relationship between scientific writing and scientific impact, this paper selected 12 variables of linguistic complexity as a proxy for depicting scientific writing. We then analyzed these features from 36,400 full-text Biology articles and 1,797 full-text Psychology articles. These features were compared to the scientific impact of articles, grouped into high, medium, and low categories. The results suggested no practical significant relationship between linguistic complexity and citation strata in either discipline. This suggests that textual complexity plays little role in scientific impact in our data sets.
On Minimum Elementary-Triplet Bases for Independence Relations

A semi-graphoid independence relation is a set of independence statements, called triplets, and is typically exponentially large in the number of variables involved. For concise representation of such a relation, a subset of its triplets is listed in a so-called basis; its other triplets are defined implicitly through a set of axioms. An elementary-triplet basis for this purpose consists of all elementary triplets of a relation. Such a basis however, may include redundant information. In this paper we provide two lower bounds on the size of an elementary-triplet basis in general and an upper bound on the size of a minimum elementary-triplet basis. We further specify the construction of an elementary-triplet basis of minimum size for restricted relations.
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping.

Deep neural networks (DNNs) have a high accuracy on image classification tasks. However, DNNs trained by such dataset with co-occurrence bias may rely on wrong features while making decisions for classification. It will greatly affect the transferability of pre-trained DNNs. In this paper, we propose an interactive method to direct classifiers paying attentions to the regions that are manually specified by the users, in order to mitigate the influence of co-occurrence bias. We test on CelebA dataset, the pre-trained AlexNet is fine-tuned to focus on the specific facial attributes based on the results of Grad-CAM.
Air

An interactive presentation of AR research on the augmentation of the interfaces between computing systems, leveraging physical space as the medium to interact, transfer, and actuate digital content.
Privately Answering Classification Queries in the Agnostic PAC Model

We revisit the problem of differentially private release of classification queries. In this problem, the goal is to design an algorithm that can accurately answer a sequence of classification queries based on a private training set while ensuring differential privacy. We formally study this problem in the agnostic PAC model and derive a new upper bound on the private sample complexity. Our results improve over those obtained in a recent work [BTT18] for the agnostic PAC setting. In particular, we give an improved construction that yields a tighter upper bound on the sample complexity. Moreover, unlike [BTT18], our accuracy guarantee does not involve any blow-up in the approximation error associated with the given hypothesis class. Given any hypothesis class with VC-dimension $d$, we show that our construction can privately answer up to $m$ classification queries with average excess error $α$ using a private sample of size $\approx \frac{d}{α^2}\,\max\left(1, \sqrt{m}\,α^{3/2}\right)$. Using recent results on private learning with auxiliary public data, we extend our construction to show that one can privately answer any number of classification queries with average excess error $α$ using a private sample of size $\approx \frac{d}{α^2}\,\max\left(1, \sqrt{d}\,α\right)$. When $α=O\left(\frac{1}{\sqrt{d}}\right)$, our private sample complexity bound is essentially optimal.
Protagonist’s Struggle in Andrea Hirata’s Novel Dwilogi Padang Bulan

This research is aimed at describing protagonist’s struggle in protecting her dignity&#x0D; in economic and educational aspects in the novel Dwilogi Padang Bulan written by&#x0D; Andre Hirata. The novel told about sosial issues related with gender injustice which&#x0D; created a struggle for equality performed by the protagonist, Enong. This research&#x0D; was conducted by applying feminist theory pioneered by Virginia Woolf (1929) since&#x0D; fenimism prioritizes about struggling value of women. This was a library research.&#x0D; Methods used in collecting the data were heuristics and hermeneutics by Palmer. The&#x0D; results showed that protagonist’s struggle in protecting her dignity and herself safe&#x0D; from death, and also being able to show her true identity. The struggle in economic&#x0D; aspect showed that the protagonist was able to fulfil the needs of her family as tin&#x0D; miner. The protagonist afforded to complete the study of her young sisters and to&#x0D; marry her sister to good man. In education aspect, the protagonist’s struggle was that&#x0D; she became the fifth runner up of English course competition.
Extraction and Analysis of Information in News Domain Using Semantic Web

News-papers, blogs, and web-pages are a rich and diverse source of textual information. However, the information contained in these sources cannot be manually extracted, recorded, and indexed, mainly because it comes in a massive size. Moreover, the extraction of some information sometimes requires specific knowledge or technical background. This is the case in the news domain where we need to extract the relevant news from a lot of available information. In order to scale knowledge extraction to the large size of available textual information, and build extractors specific to a certain field various techniques are applied over the unstructured data so that it can be made available to the users. This could help the researchers and the news readers or users to find relevant information in less time and with great ease. This study aims to review all the approaches and techniques done so far, for the information retrieval, search ability and its analysis and it also proposed an idea for better searching that reduces the time complexity to extract the data and also reduces human intervention. This is a better idea to put forward which also helps in filtering of irrelevant data and thus integrates only the relevant data to create a better space for the news data.
Computing quadratic subfields of number fields

Given a number field, it is an important question in algorithmic number theory to determine all its subfields. If the search is restricted to abelian subfields, one can try to determine them by using class field theory. For this, it is necessary to know the ramified primes. We show that the ramified primes of the subfield can be computed efficiently. Using this information we give algorithms to determine all the quadratic and the cyclic cubic subfields of the initial field. The approach generalises to cyclic subfields of prime degree. In the case of quadratic subfields, our approach is much faster than other methods.