Reviewerly/exHarmony huggingface dataset:
Comparison between Mammography and Breast Ultrasound in the Detection of Breast Cancer in Dense Breast Tissue among a Sample of Iraqi Women

Background: Breast cancer is the most common cancer reported in women worldwide . In Iraq, it is the most common registered malignancy. Mammography plays a major role in the early detection of breast cancers. Dense breast parenchyma has been reported to be the most important inherent factor that limits depiction of breast cancer on mammogram, and often needs supplementary breast ultrasound for complete assessment. &#x0D; Objectives: To evaluate and compare the diagnostic performance of mammography and ultrasound in the&#x0D; detection of breast cancer in dense breast tissue.&#x0D; Patients and methods: A record review study was performed in the Oncology Teaching Hospital/ Medical&#x0D; City from April 2018 to December 2018. The study included forty five females, who attended the Main&#x0D; Referral Center for Early Detection of Breast Tumors during 2017 and 2018 were diagnosed with breast cancer histopathologically . They had dense breast tissues on mammography (either heterogeneously dense&#x0D; breast tissue i.e . category C or extremely dense breast tissue i.e. category D). All patients underwent subsequent breast ultrasound . Their information including the mammogram findings, breast ultrasound,&#x0D; fine needle aspiration (FNA) and biopsy results were reviewed analyzed and compared.&#x0D; Results:Twenty four patients (53.3%) had heterogeneously dense breast tissue (ACR category C) and 21&#x0D; patients (46.6%) had extremely dense breast tissue (ACR category D). The mammogram detected 36 from 45&#x0D; breast cancers (80%) while 9 (20%) were not detected by mammogram, so the mammogram had a detection&#x0D; rate of breast cancer of 80% in mammographically dense breast, while breast ultrasound had higher detection rate of about 97.7% .The sensitivity of mammography in extremely dense breast tissue was about 71% and in heterogeneously dense breast was about 87% while ultrasound had shown a higher sensitivity&#x0D; with increasing tissue density (98% vs. 100%).&#x0D; Conclusion: Breast cancer can be easily obscured and missed in mammographically dense breast tissue due&#x0D; to overlapping surrounding fibroglandular tissue and additional complementary breast ultrasound is highly&#x0D; recommended for a thorough evaluation and to depict mammographically occult breast carcinoma.
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding

Neural machine translation (NMT) is one of the best methods for understanding the differences in semantic rules between two languages. Especially for Indo-European languages, subword-level models have achieved impressive results. However, when the translation task involves Chinese, semantic granularity remains at the word and character level, so there is still need more fine-grained translation model of Chinese. In this paper, we introduce a simple and effective method for Chinese translation at the sub-character level. Our approach uses the Wubi method to translate Chinese into English; byte-pair encoding (BPE) is then applied. Our method for Chinese-English translation eliminates the need for a complicated word segmentation algorithm during preprocessing. Furthermore, our method allows for sub-character-level neural translation based on recurrent neural network (RNN) architecture, without preprocessing. The empirical results show that for Chinese-English translation tasks, our sub-character-level model has a comparable BLEU score to the subword model, despite having a much smaller vocabulary. Additionally, the small vocabulary is highly advantageous for NMT model compression.
Interactive Refinement of Cross-Lingual Word Embeddings

Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. First, CLIME ranks words by their salience to the downstream task. Then, users mark similarity between keywords and their nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We evaluate CLIME on identifying health-related text in four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME often improves accuracy faster than an active learning baseline and can be easily combined with active learning to improve results.
Importance Sampling via Local Sensitivity

Given a loss function $F:\mathcal{X} \rightarrow \R^+$ that can be written as the sum of losses over a large set of inputs $a_1,\ldots, a_n$, it is often desirable to approximate $F$ by subsampling the input points. Strong theoretical guarantees require taking into account the importance of each point, measured by how much its individual loss contributes to $F(x)$. Maximizing this importance over all $x \in \mathcal{X}$ yields the \emph{sensitivity score} of $a_i$. Sampling with probabilities proportional to these scores gives strong guarantees, allowing one to approximately minimize of $F$ using just the subsampled points. Unfortunately, sensitivity sampling is difficult to apply since (1) it is unclear how to efficiently compute the sensitivity scores and (2) the sample size required is often impractically large. To overcome both obstacles we introduce \emph{local sensitivity}, which measures data point importance in a ball around some center $x_0$. We show that the local sensitivity can be efficiently estimated using the \emph{leverage scores} of a quadratic approximation to $F$ and that the sample size required to approximate $F$ around $x_0$ can be bounded. We propose employing local sensitivity sampling in an iterative optimization method and analyze its convergence when $F$ is smooth and convex.
Diagnostics of Metacognitive Involvement in the Activities of Students in the Learning Process at a University

The relevance of the problem under study is due to the need of the modern educational process in organizing independent cognitive activity of students, and as a result, the main task of the modern education system is to measure the level of metacognitive involvement in students' activities and explore the possibility of explicitly incorporating metacognitive knowledge into the process of learning specific subjects.The purpose of the article is to diagnose metacognitive involvement in the activities of students in the process of learning at university.The leading method for studying this problem is the Metacognitive Awareness Inventory questionnaire (Schraw & Dennison, 1998), which makes it possible to view this problem as a process of specialists' purposeful and conscious mastery of the skills to monitor the quality of education.This questionnaire is part of a fairly small number of methods that currently exist and investigate the metacognitive properties of a person.Its main goal is to trace the patterns of participation of meta-processes in the implementation of activities.The article presents the results of a dissertation research on the problem of metacognitive involvement in activities.The authors provide data on measuring the level of metacognitive involvement in the activities of university students and exploring the possibility of explicitly incorporating metacognitive knowledge into the learning process of specific subjects that affect the development of professional metacognition.Based on the questionnaire used, the article presents the measurements of two components of metacognition: metacognitive knowledge and metacognitive processes.According to the results of the study, metacognitive skills (processes), which are used less often by students, have been established; due to which the issue of applying data about students' metacognition in the practical activities of a teacher is discussed.
Securing Smart Healthcare Systems from Vulnerability Exploitation

The capabilities of the IoT to track entities, measure and analyze vital information captured by sensors, and to transmit data over a fleet of devices, has convincingly placed it best suited toward the realization of the future-ready smart hospitals and healthcare applications. Real time sensing and monitoring of vital signs of patients, efficient scheduling of medical procedures, effective tracking of scarce resources, and optimized supply chain management of drugs and medical devices help not only to significantly improve the quality of health services but also to lower healthcare costs. However, bringing the healthcare systems under the IoT network poses huge security challenges. Once the devices controlling the life supporting equipments are under attack, the damages are beyond imagination. The vulnerabilities in the IoT-based devices can pose serious threats to the IoT healthcare systems. In this work, we propose a graphical modeling of possible attacks through exploitation of such vulnerabilities. The proposed model helps to foresee the possible attack paths exist in a network and to design suitable defense mechanisms. We also propose strategies for improving the security of the IoT-assisted networks.
Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory

Convolutional Recurrent Neural Networks (CRNNs) excel at scene text recognition. Unfortunately, they are likely to suffer from vanishing/exploding gradient problems when processing long text images, which are commonly found in scanned documents. This poses a major challenge to goal of completely solving Optical Character Recognition (OCR) problem. Inspired by recently proposed memory-augmented neural networks (MANNs) for long-term sequential modeling, we present a new architecture dubbed Convolutional Multi-way Associative Memory (CMAM) to tackle the limitation of current CRNNs. By leveraging recent memory accessing mechanisms in MANNs, our architecture demonstrates superior performance against other CRNN counterparts in three real-world long text OCR datasets.
Comprehending Code Fragment in Code Clones: A Literature-Based Perspective

As Code Clones are defined on the notion of similarity in code fragments, it is necessary to first know what a code is meant by in accordance with Code Clones. A Source Code Fragment, which is a sequence of source code lines, is the basic entity that is used to analyze similarity/relation between Code Clones. For analysis, removal, avoidance, and management of Code Clones we have to first detect clones in software systems. There are more than 40 clone detection tools that implement some clone detection techniques to detect clones, but it is not well-defined what could be the appropriate minimum threshold for Clone length and with which unit of estimation. This paper, on the basis of Code Clone literature, presents different Units of Measurement of Clone Size and a comprehensive review of minimum Clone Size based on a particular technique used in Clone Detection and also argues that a unique Unit of Measurement and Minimum Clone Size should be presented.
Predictors of cybercrime victimization

In line with self-control theory and routine activities theory, several previous studies have shown that low self-control and involvement in certain online activities are associated with cybercrime victimization. However, these studies were based on cross-sectional, observational data and therefore it remains unknown whether these findings indicate causal effects on cybercrime victimization or simply reflect spurious associations. In this study, a longitudinal dataset (2008-2016) drawn from a large representative sample of Dutch households was used to estimate the effect of low self-control and fifteen online activities on five types of cybercrime victimization: online harassment, online fraud, hacking, virus infection, and malware infection. In addition to standard cross-sectional analyses, quasi-experimental research designs (i.e., fixed effects panel models and discordant sibling models) were used to control for hidden bias. Results from the cross-sectional analyses show that low self-control and time spent on certain online activities were significantly related to cybercrime victimization. However, after applying the quasi-experimental research designs most significant associations disappeared, suggesting that these associations do not reflect causal effects but are rather the consequence of unobserved confounders. It is recommended that future studies on cybercrime victimization and offending use quasi-experimental research designs that control for hidden bias.
Studies on the effect of MegaPixel sensor resolution on displayed image quality and relevant metrics

This paper investigates camera phone image quality, namely the effect of sensor megapixel (MP) resolution on the perceived quality of images displayed at full size on high-quality desktop displays. For the purpose, we use images from simulated cameras with different sensor MP resolutions. We employ methods recommended in the IEEE 1858 Camera Phone Image Quality (CPIQ) standard, as well as other established psychophysical paradigms, to obtain subjective image quality ratings for systems with varying MP resolution from large numbers of observers. These are subsequently used to validate image quality metrics (IQMs) relating to sharpness and resolution, including those from the CPIQ standard. Further, we define acceptable levels of quality - when changing MP resolution - for mobile phone images in Subjective Quality Scale (SQS) units. Finally, we map SQS levels to categories obtained from star-rating experiments (commonly used to rate consumer experience). Our findings draw a relationship between the MP resolution of the camera sensor and the LCD device. The chosen metrics predict quality accurately, but only the metrics proposed by CPIQ return results in calibrated JNDs in quality. We close by discussing the appropriateness of star-rating experiments for the purpose of measuring subjective image quality and metric validation.
A Multigrid Method for Efficiently Training Video Models

Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training assumes a fixed mini-batch shape: a specific number of clips, frames, and spatial size. However, what is the optimal shape? High resolution models perform well, but train slowly. Low resolution models train faster, but they are inaccurate. Inspired by multigrid methods in numerical optimization, we propose to use variable mini-batch shapes with different spatial-temporal resolutions that are varied according to a schedule. The different shapes arise from resampling the training data on multiple sampling grids. Training is accelerated by scaling up the mini-batch size and learning rate when shrinking the other dimensions. We empirically demonstrate a general and robust grid schedule that yields a significant out-of-the-box training speedup without a loss in accuracy for different models (I3D, non-local, SlowFast), datasets (Kinetics, Something-Something, Charades), and training settings (with and without pre-training, 128 GPUs or 1 GPU). As an illustrative example, the proposed multigrid method trains a ResNet-50 SlowFast network 4.5x faster (wall-clock time, same hardware) while also improving accuracy (+0.8% absolute) on Kinetics-400 compared to the baseline training method. Code is available online.
Latent space conditioning for improved classification and anomaly detection

We propose a new type of variational autoencoder to perform improved pre-processing for clustering and anomaly detection on data with a given label. Anomalies however are not known or labeled. We call our method conditional latent space variational autonencoder since it separates the latent space by conditioning on information within the data. The method fits one prior distribution to each class in the dataset, effectively expanding the prior distribution to include a Gaussian mixture model. Our approach is compared against the capabilities of a typical variational autoencoder by measuring their V-score during cluster formation with respect to the k-means and EM algorithms. For anomaly detection, we use a new metric composed of the mass-volume and excess-mass curves which can work in an unsupervised setting. We compare the results between established methods such as as isolation forest, local outlier factor and one-class support vector machine.
The human side of geoscientists: comparing geoscientists' and non-geoscientists' cognitive and affective responses to geology

Abstract. Geoscientists and non-geoscientists often struggle to communicate with each other. In this paper we aim to understand how geoscientists and non-geoscientists perceive geological concepts and processes, that is, how they think (cognitive responses) and feel (affective responses) about them. To this effect, using a mixed-methods approach, we compare mental models – people's representation of a phenomenon – of the subsurface, mining/quarrying, drilling, and flooding between geoscientists (n = 24) and non-geoscientists (n = 38). We identify four dominant themes which underlie their mental models: (1) degree of knowledge and familiarity, (2) beliefs about human interactions, (3) affective beliefs, and (4) beliefs about perceived impact of the processes. While the mental models of non-geoscientists focus more on the perceived negative environmental and economic impacts of geoscience, those of geoscientists focus more on human interactions. We argue that mental models are the result of beliefs, including both cognitive and affective components, and that both need to be acknowledged for effective dialogue between the two groups to take place.
Visualising and evaluating the effects of combining active learning with word embedding features.

A tool that enables the use of active learning, as well as the incorporation of word embeddings, was evaluated for its ability to decrease the training data set size required for a named entity rec ...
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations

Variational Autoencoders (VAEs) are powerful in data representation inference, but it cannot learn relations between features with its vanilla form and common variations. The ability to capture relations within data can provide the much needed inductive bias necessary for building more robust Machine Learning algorithms with more interpretable results. In this paper, inspired by recent advances in relational learning using Graph Neural Networks, we propose the Self-Attention Graph Variational AutoEncoder (SAG-VAE) network which can simultaneously learn feature relations and data representations in an end-to-end manner. SAG-VAE is trained by jointly inferring the posterior distribution of two types of latent variables, which denote the data representation and a shared graph structure, respectively. Furthermore, we introduce a novel self-attention graph network that improves the generative capabilities of SAG-VAE by parameterizing the generative distribution allowing SAG-VAE to generate new data via graph convolution, while still trainable via backpropagation. A learnable relational graph representation enhances SAG-VAE's robustness to perturbation and noise, while also providing deeper intuition into model performance. Experiments based on graphs show that SAG-VAE is capable of approximately retrieving edges and links between nodes based entirely on feature observations. Finally, results on image data illustrate that SAG-VAE is fairly robust against perturbations in image reconstruction and sampling.
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder

Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset of the object segmentation problem and less handled in the semantic segmentation field. Obviously, portrait segmentation has its unique requirements. First, because the portrait segmentation is performed in the middle of a whole process of many real-world applications, it requires extremely lightweight models. Second, there has not been any public datasets in this domain that contain a sufficient number of images with unbiased statistics. To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules. The information blocking decoder uses confidence estimates to recover local spatial information without spoiling global consistency. The spatial squeeze module uses multiple receptive fields to cope with various sizes of consistency in the image. To tackle the second problem, we propose a simple method to create additional portrait segmentation data which can improve accuracy on the EG1800 dataset. In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight segmentation models. Our method reduces the number of parameters from 2.1M to 86.9K (around 95.9% reduction), while maintaining the accuracy under an 1% margin from the state-of-the-art portrait segmentation method. We also show our model is successfully executed on a real mobile device with 100.6 FPS. In addition, we demonstrate that our method can be used for general semantic segmentation on the Cityscapes dataset. The code and dataset are available in https://github.com/HYOJINPARK/ExtPortraitSeg .
PENGELOMPOKKAN JUMLAH DESA/KELURAHAN YANG MEMILIKI SARANA KESEHATAN MENURUT PROVINSI DENGAN MENGGUNAKAN METODE K-MEANS CLUSTER

Health problems that exist in the community, especially in developing countries such as Indonesia are influenced by two factors, namely physical aspects and non-physical aspects. Physical aspects such as health facilities and treatment of diseases, while the second is non-physical aspects that involve health problems. The construction of health facilities is an effort to fulfill one of the basic rights of the people carried out by the government to provide health facilities that will be used in helping the community to be healthy. So the purpose of this study is to group villages / kelurahan that have health facilities quickly and effectively. Describe the number of villages / kelurahan that have health facilities using the K-Means method, in order to find out whether the constraints faced by the government in grouping villages / kelurahan that have inadequate health facilities based on the provinces in Indonesia, require a long time to group them. It is expected that using this method can produce clustering which is proven to be accurate in the case of the number of villages / kelurahan that have inadequate health facilities based on the provinces in Indonesia.Keywords: Health Facilities, Datamining, K-Means