Reviewerly/exHarmony huggingface dataset:
A path to DOT: formalizing fully path-dependent types

The Dependent Object Types (DOT) calculus aims to formalize the Scala programming language with a focus on path-dependent types — types such as x . a 1 … a n . T that depend on the runtime value of a path x . a 1 … a n to an object. Unfortunately, existing formulations of DOT can model only types of the form x . A which depend on variables rather than general paths. This restriction makes it impossible to model nested module dependencies. Nesting small components inside larger ones is a necessary ingredient of a modular, scalable language. DOT’s variable restriction thus undermines its ability to fully formalize a variety of programming-language features including Scala’s module system, family polymorphism, and covariant specialization. This paper presents the pDOT calculus, which generalizes DOT to support types that depend on paths of arbitrary length, as well as singleton types to track path equality. We show that naive approaches to add paths to DOT make it inherently unsound, and present necessary conditions for such a calculus to be sound. We discuss the key changes necessary to adapt the techniques of the DOT soundness proofs so that they can be applied to pDOT. Our paper comes with a Coq-mechanized type-safety proof of pDOT. With support for paths of arbitrary length, pDOT can realize DOT’s full potential for formalizing Scala-like calculi.
PPCI: an R Package for Cluster Identification using Projection Pursuit

CITATION: Hofmeyr, D. P. & Pavlidis, N. G. 2019. PPCI: an R Package for Cluster Identification using Projection Pursuit. The R Journal, 11(2):152-170. doi:10.32614/RJ-2019-046
Task scheduling algorithm based on multi criteria decision making method for cloud computing environment: TSABMCDMCCE

Abstract This Paper focuses on multi-criteria decision making techniques (MCDMs), especially analytical networking process (ANP) algorithm to design a model in order to minimize the task scheduling cost during implementation using a queuing model in a cloud environment and also deals with minimization of the waiting time of the task. The simulated results of the algorithm give better outcomes as compared to other existing algorithms by 15 percent.
Performance of an automatic translator in translating medical abstracts

The aim of the present paper was to quantitatively assess the performance of a free online translator in translating medical texts from English into Italian. Effectiveness was evaluated and established by analyzing the number and percent of translation errors with respect to the original medical abstracts. Furthermore, the present study analyzed the total number and the percent of translation errors and their correlation with lexical density, which was assessed using Text analyzer. The mean number of total translation errors was 21. The mean percentage of total translation errors was 15%. The mean lexical density was 66.7%. A direct correlation was found between total translation errors and the lexical density. The findings of the present study indicate a fairly good performance of Google translator in translating words in highly academic writings such as medical abstracts. However, an effective translation is not a matter only of finding a correspondence between words in the source language and the target language, many other aspects are just as important.
Joint-attention Discriminator for Accurate Super-resolution via Adversarial Training

Tremendous progress has been witnessed on single image super-resolution (SR), where existing deep SR models achieve impressive performance in objective criteria, e.g., PSNR and SSIM. However, most of the SR methods are limited in visual perception, for example, they look too smooth. Generative adversarial network (GAN) favors SR visual effects over most of the deep SR models but is poor in objective criteria. In order to trade off the objective and subjective SR performance, we design a joint-attention discriminator with which GAN improves the SR performance in PSNR and SSIM, as well as maintaining the visual effect compared with non-attention GAN based SR models. The joint-attention discriminator contains dense channel-wise attention and cross-layer attention blocks. The former is applied in the shallow layers of the discriminator for channel-wise weighting combination of feature maps. The latter is employed to select feature maps in some middle and deep layers for effective discrimination. Extensive experiments are conducted on six benchmark datasets and the experimental results show that our proposed discriminator combining with different generators can achieve more realistic visual performances.
Size and Shape Filtering of Malignant Cell Clusters within Breast Tumors Identifies Scattered Individual Epithelial Cells as the Most Valuable Histomorphological Clue in the Prognosis of Distant Metastasis Risk

Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy.
Minecraft as AI Playground and Laboratory

Modern video games provide exciting challenges and opportunities for pushing the state of the art in machine learning and other research areas, and, in turn, stand to first benefit from research advances. For driving research, games provide rich data that can be used to tackle hard problems, from complex decision making to collaboration. If and when these are successfully tackled, new algorithms and insights have the potential to enable entirely new game experiences.
Smartphone Application in Postgraduate Clinical Psychology Training: Trainees’ Perspectives

M-learning refers to the learning that takes advantage of mobile technologies. Although research shows enhanced educational outcomes from m-learning in some Asian countries, the generalizability to postgraduate clinical psychology training in Singapore remains unclear. Current professional standards in clinical psychology training emphasize the importance of attainment of clinical competencies in trainees. Although learning theories indicated potential for m-learning to be incorporated into the local clinical psychology curriculum, trainees’ perspectives have not been adequately explored on m-learning. The study aimed to address this gap by exploring the use of m-learning via a novel smartphone application in clinical psychology training using mixed-methods design. Eight clinical psychology trainees between the ages of 26 to 43 years old (mean age of 31.75, SD = 5.49) enrolled in a relevant coursework subject were recruited. Participants were randomly allocated to the experimental and control groups. The experimental group accessed the novel application weekly, from week 1 to week 6, and participants in the control group accessed the application after week 6. Participants from both groups completed a brief demographic questionnaire, and the following scales New General Self-Efficacy Scale adapted for Education (NGSES-E) and self-reported scale of learning outcomes (SLO). The qualitative study explored how participants perceived and experienced the novel application. Participants from the experimental group were invited to provide open-ended responses about the novel application. Data were analyzed using thematic analysis. Results from the qualitative analysis yielded four themes of: Convenience, preferred learning style, building confidence, and putting theory into practice. Findings from the qualitative study were consistent with previous studies about advantages of m-learning: That the e-platform was convenient, the learning style was engaging, which helped to build confidence, and facilitate practical learning of skills. The qualitative results were helpful in understanding the users’ perspectives and experience of the novel application, indicating that future research in this innovative area is necessary. However, the quantitative outcomes were not significant, limitations would be discussed, and recommendations made for future research.
Heart Rate Monitoring and Stimulation with the Internet of Thing-Based (IoT) Alquran Recitation

Heart rate or pulse is an important parameter used to determine a person's mental state because when the heartbeat is abnormal, it is necessary to make further efforts to prevent undesirable things from happening. In this study, researchers plan to design the Heart Rate Monitoring and Stimulation with the Internet of Thing-Based (IoT) Qur'an Recitation. The steps taken to design a monitoring system and stimulate the human heartbeat are to design hardware and software. The sensor used is a pulse heart rate sensor as a heart rate detector processed by Arduino-based NodeMCU V3, the test results and measurements will be sampled using smartphone media. Data collection process; polytechnic students as objects taken by their heart rate data, the data collection process was carried out twice, the morning before the lecture began and the afternoon after the lecture process was complete, the testing process was done by taking data using a smartphone and turning on the Quran recitation for 2 minutes. Research is expected to be an stimulus indicator of heart rate, so that the heart rate is more relaxed and calm
Sine-skewed toroidal distributions and their application in protein bioinformatics

Summary In the bioinformatics field, there has been a growing interest in modeling dihedral angles of amino acids by viewing them as data on the torus. This has motivated, over the past years, new proposals of distributions on the torus. The main drawback of most of these models is that the related densities are (pointwise) symmetric, despite the fact that the data usually present asymmetric patterns. This motivates the need to find a new way of constructing asymmetric toroidal distributions starting from a symmetric distribution. We tackle this problem in this article by introducing the sine-skewed toroidal distributions. The general properties of the new models are derived. Based on the initial symmetric model, explicit expressions for the shape and dependence measures are obtained, a simple algorithm for generating random numbers is provided, and asymptotic results for the maximum likelihood estimators are established. An important feature of our construction is that no extra normalizing constant needs to be calculated, leading to more flexible distributions without increasing the complexity of the models. The benefit of employing these new sine-skewed toroidal distributions is shown on the basis of protein data, where, in general, the new models outperform their symmetric antecedents.
Nanoscale Automated Quantitative Mineralogy: A 200-nm Quantitative Mineralogy Assessment of Fault Gouge Using Mineralogic

Effective energy-dispersive X-ray spectroscopy analysis (EDX) with a scanning electron microscope of fine-grained materials (submicrometer scale) is hampered by the interaction volume of the primary electron beam, whose diameter usually is larger than the size of the grains to be analyzed. Therefore, mixed signals of the chemistry of individual grains are expected, and EDX is commonly not applied to such fine-grained material. However, by applying a low primary beam acceleration voltage, combined with a large aperture, and a dedicated mineral classification in the mineral library employed by the Zeiss Mineralogic software platform, mixed signals could be deconvoluted down to a size of 200 nm. In this way, EDX and automated quantitative mineralogy can be applied to investigations of submicrometer-sized grains. It is shown here that reliable quantitative mineralogy and grain size distribution assessment can be made based on an example of fault gouge with a heterogenous mineralogy collected from Ikkattup nunaa Island, southern West Greenland.
Complaint Analysis and Classification for Economic and Food Safety

João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, Ana Maria Oliveira. Proceedings of the Second Workshop on Economics and Natural Language Processing. 2019.
Detection of Fake Accounts in Instagram Using Machine Learning

With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women.According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016.Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives.While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id.A real life person can create multiple fake IDs and hence impostors can easily be made.Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one's passport or driver's license), in the virtual world of social media, admission does not require any such checks.In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Transformation of UML class diagram into OWL Ontology

UML class diagrams are used in order to demonstrate the model information systems. Most of the former systems were previously framed by applying UML class diagrams. Besides, the current trend has a growing demand of reusing data from older information systems, which is very beneficial for the implementation of semantic knowledge. The upgradation and conversion of UML class diagrams into ontology aim to reuse older systems; therefore, cost reduction is essential. There have been many studies for transformation from UML class diagrams into ontology; however, they have not fully proposed the transformation rules. This paper will suggest the addition of rules for transforming UML class diagrams to ontologies, such as attribute transformations which have data types as classes, the structured attributes, the association with association class, the recursive association, the aggregation with a qualification.
A Gaussian Uniform Mixture Model for Robust Kalman Filtering

This article presents a Kalman-type recursive estimator for discrete-time systems with a measurement noise modeled by a Gaussian-uniform mixture. The objective is to deal with data containing outliers that degrade the performance of the regular Kalman filter. The proposed non-Gaussian noise model takes into account the reliability of the measurement with respect to erroneous data. The Kalman-type estimator is based on Masreliez's formulation which copes with non-Gaussian noise models. Results in different simulated conditions are displayed to evaluate the performance of the newly-presented algorithm and to compare it to state-of-art alternatives.
Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation

In this paper, we report our system submissions to all 6 tracks of the WNGT 2019 shared task on Document-Level Generation and Translation.The objective is to generate a textual document from either structured data: generation task, or a document in a different language: translation task.For the translation task, we focused on adapting a large scale system trained on WMT data by fine tuning it on the RotoWire data.For the generation task, we participated with two systems based on a selection and planning model followed by (a) a simple language model generation, and (b) a GPT-2 pre-trained language model approach.The selection and planning module chooses a subset of table records in order, and the language models produce text given such a subset.
Possible Usages of Smart Contracts (Blockchain) in Healthcare and Why No One Is Using Them.

Security, privacy, transparency, consent, and data sharing are major challenges that healthcare institutions must address today. The explosion of the Internet of Things (IoT), the enactment of the General Data Protection Regulation (GDPR), the growing trend of patients self-managing their diseases, and the eagerness of patients to share their self-collected health data with primary and secondary health organisations further increase the complexity of these challenges. Smart contracts, based on blockchain technology, can be a legitimate approach for addressing these challenges. Smart contracts define rules and penalties in an agreement, enforce those rules, and render them irrevocable. This paper presents a state-of-the-art review (as of May 2018) of the possible usages of smart contracts in healthcare and focuses on data sharing between patients, doctors, and institutions.
Augmented Reality-Based Procedural Task Training Application for Less Privileged Children and Autistic Individuals

In this work, we evaluate the applicability of using Augmented Reality applications in for enhanced learning experiences for children from less privileged backgrounds, with a focus on autistic population. Such an intervention can prove to be very useful to children with reduced cognitive development. In our evaluation, we compare the AR mode of instruction for a procedural task training, using tangram puzzles, with live demonstration and a desktop-based application. First, we performed a within-subjects user study on neurotypical children in the age group of 9 - 12 years. We asked the children to independently solve a tangram puzzle after being trained through different modes of instruction. Second, we used the same instruction modes to train autistic participants. Our findings indicate that during training, children took the longest time to interact with Desktop-based instruction, and took the shortest time to interact with the live demonstration mode. Children also took the longest time to independently solve the tangram puzzle in the Desktop mode. We also found that autistic participants could use AR-based instructions but required more time to go through the training.
Transductive Zero-Shot Hashing for Multilabel Image Retrieval

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes. While some newly introduced images may contain undefined semantic labels, which we call unseen images, zero-shot hashing (ZSH) techniques have been studied for retrieval. However, existing ZSH methods mainly focus on the retrieval of single-label images and cannot handle multilabel ones. In this article, for the first time, a novel transductive ZSH method is proposed for multilabel unseen image retrieval. In order to predict the labels of the unseen/target data, a visual-semantic bridge is built via instance-concept coherence ranking on the seen/source data. Then, pairwise similarity loss and focal quantization loss are constructed for training a hashing model using both the seen/source and unseen/target data. Extensive evaluations on three popular multilabel data sets demonstrate that the proposed hashing method achieves significantly better results than the comparison methods.
Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content

One of the most persistent characteristics of written user-generated content (UGC) is the use of non-standard words.This characteristic contributes to an increased difficulty to automatically process and analyze UGC.Text normalization is the task of transforming lexical variants to their canonical forms and is often used as a pre-processing step for conventional NLP tasks in order to overcome the performance drop that NLP systems experience when applied to UGC.In this work, we follow a Neural Machine Translation approach to text normalization.To train such an encoderdecoder model, large parallel training corpora of sentence pairs are required.However, obtaining large data sets with UGC and their normalized version is not trivial, especially for languages other than English.In this paper, we explore how to overcome this data bottleneck for Dutch, a low-resource language.We start off with a small publicly available parallel Dutch data set comprising three UGC genres and compare two different approaches.The first is to manually normalize and add training data, a money and time-consuming task.The second approach is a set of data augmentation techniques which increase data size by converting existing resources into synthesized non-standard forms.Our results reveal that, while the different approaches yield similar results regarding the normalization issues in the test set, they also introduce a large amount of over-normalizations.
Multi-Stage Matching Approach for Mobile Platform Visual Imagery

Unpredictable texture structure and motion blur continuously exist in mobile platform visual imagery and seriously reduce the similarity between images. Thus, accurate, stable, and well-distributed matches to follow the accurate pose estimation of the platform are difficult to obtain. To solve such problems, an effective image matching method for mobile platform visual imagery is presented in this study. The proposed method includes three steps, namely, standard initial matching, transformation matrices evaluation and matching propagation. Firstly, an oriented FAST and rotated BRIEF (ORB) method was used to obtain the number of matches and the initial projective transformation relationship between an image pair. Secondly, an evaluation function was set to choose the suitable rotation matrix for the image scene. Finally, geometric correspondence matching was utilized to propagate matches and produce additional reliable matching results. The geometric correspondence matching used the geometric relationship between the image pair and found more suitable matches than the standard ORB matching. Comprehensive experiments on TUM and ICL-NUIM dataset images showed that the proposed algorithm performs better in terms of correct matches, satisfactory matching rate, and higher matching accuracy than the standard ORB and ORB-slam2 initial match methods.