Reviewerly/exHarmony huggingface dataset:
Bi-Dimensional Representation of Patients for Diagnosis Prediction

Previous work on learning representation for patients from Electronic Health Records (EHRs) has succeeded in assisting medical diagnosis. Three perspectives of records, patient symptoms, medical treatments and diagnosis codes, are consisted in EHRs. However, existing approaches on patient representation learning take one perspective of patient symptoms and medical treatments into consideration, which miss out the latent correlations between them. Actually, based on the sequence of hospital visits, physical symptoms and associated treatments together affect the diagnosis and recovery of patients. In this paper, we propose Patient2vec, a novel model to learn the bi-dimensional representation for patients by jointly extracting features from physical symptoms and medical treatments. We introduce RNN model into Patient2vec to learn the sequential context-aware features of visits. The learned representations are then fed into a classifier to diagnosis prediction. Experiments on public dataset through multi-classification tasks indicate that Patient2vec achieves up to 76% improvement in area under the ROC curve (AUC) on average, demonstrating that our method significantly outperforms single dimension representation for patients.
Artificial Intelligence and Its Application in Computer Network Technology

Abstract This paper briefly introduces the concept, origin, characteristics and application of artificial intelligence, expounds the application advantages of artificial intelligence in computer network technology, analyzes the problems existing in computer network technology, and launches the application of artificial intelligence in computer network technology. In-depth study, this article can play a certain reference and help for the application of artificial intelligence in computer network technology, gives full play to the value and role of artificial intelligence, promotes the development of computer network technology, and better meets the current social and economic development process.
Clustering Technique to Improve Rural Malaysian Primary School Pupils‟ Writing Skill

From this research, it was found that clustering technique helps these rural primary pupils to generate more ideas for their writing.By clustering what they wanted to put in their writing activity, they were able to remember details, expand and connect ideas for their composition.One of the limitations occurred in this study was the participants" lack of vocabulary.Most of the time these participants had the ideas of what information they wanted to write on their map but they did not have the vocabulary.One way to overcome this problem is by assisting and guiding them.This can be done by providing the needed vocabulary or asking them to look into the dictionary.This research is beneficial for other teachers especially those who teach in the rural areas where the pupils are low proficient in English.They could use this technique in teaching writing for pupils who faced difficulties in this skill.Pupils, on the other hand, will be able to use this technique to generate ideas for their writing.It provides a platform for them to write details or information in order to construct and elaborate more on their composition.Other than that, future researchers could explore this knowledge and use it to conduct research in other places. VII. CONCLUSIONAs a conclusion, clustering technique could act as an alternative for writing especially during the pre-writing session as it allows pupils to explore ideas for their writing.This technique is easy and suitable to conduct for schools with limited access of amenities.
Workflow and tools to track and visualize behavioural data from a Virtual Reality environment using a lightweight GIS

Evaluating user behaviour in Virtual Reality is a challenge for every researcher involved in designing and executing experiments in immersive environments. Behavioural information could lead to relevant findings in presence, engagement or, for example, the mood of the player during a VR experience. Saving this kind of information and exploding it in an appropriate way could lead researchers or even game designers to identify relevant behavioural patterns or correlations. In this article, we are proposing a simple, replicable workflow and a set of scripted tools in order to acquire user’s navigational data and visualize it using the inherent capabilities of a Geographic Information System. Our workflow goes from data acquisition in Unity3D with C# to the final representation in a map using Leaflet, an open-source GIS JavaScript based, passing through the pre-processing of XML files. Using a GIS to visualize navigational data is a flexible, ecological and effective solution that improves productivity and awareness on data storytelling capabilities.
Self-Adversarially Learned Bayesian Sampling

Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models. While tremendous progresses have been achieved via scalable Bayesian sampling such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD), the generated samples are typically highly correlated. Moreover, their sample-generation processes are often criticized to be inefficient. In this paper, we propose a novel self-adversarial learning framework that automatically learns a conditional generator to mimic the behavior of a Markov kernel (transition kernel). High-quality samples can be efficiently generated by direct forward passes though a learned generator. Most importantly, the learning process adopts a self-learning paradigm, requiring no information on existing Markov kernels, e.g., knowledge of how to draw samples from them. Specifically, our framework learns to use current samples, either from the generator or pre-provided training data, to update the generator such that the generated samples progressively approach a target distribution, thus it is called self-learning. Experiments on both synthetic and real datasets verify advantages of our framework, outperforming related methods in terms of both sampling efficiency and sample quality.
Quantum Implementation of Powell’s Conjugate Direction Method

A quantum circuit implementation of Powell’s conjugate direction method (“Powell’s method”) is proposed based on quantum basic transformations in this study. Powell’s method intends to find the minimum of a function, including a sequence of parameters, by changing one parameter at a time. The quantum circuits that implement Powell’s method are logically built by combining quantum computing units and basic quantum gates. The main contributions of this study are the quantum realization of a quadratic equation, the proposal of a quantum one-dimensional search algorithm, the quantum implementation of updating the searching direction array (SDA), and the quantum judgment of stopping the Powell’s iteration. A simulation demonstrates the execution of Powell’s method, and future applications, such as data fitting and image registration, are discussed.
Override and update

Override and update are natural constructions for combining partial functions, which arise in various program specification contexts. We use an unexpected connection with combinatorial geometry to provide a complete finite system of equational axioms for the first order theory of the override and update constructions on partial functions, resolving the main unsolved problem in the area.
The Topic Modeling Analysis of The DMZ Tour Issues Using Text Mining

Among text mining techniques, topic modeling is utilized in diverse fields because it enables drawing major topics from massive unstructured documents and identifying new facts regarding certain issues. Recently, social interest and opinions concerning DMZ have increased due to the rapid changes in inter-Korean relations, but no phenomenological approach to DMZ has been made. Therefore, this study applied topic modeling to draw DMZ issues and keywords from social big data, analyzed the weights of topics by keyword and presented policy implications. First, it was shown that concrete tour places, tour destinations and tour behaviors for ‘DMZ tour’ have been relatively less clearly established compared to other tours. Second, it was shown that DMZ was spatially and highly associated with only some areas such as Paju. Finally, it was shown that tour behaviors were mainly composed of one-time games, festivals and events. This study presented that topic modeling is useful for drawing major topics from certain tourism phenomenon, structural analysis and meaning interpretation.
Restricted Orientation Dubins Path With Application to Sailboats

This letter develops a geometrical construction of the shortest Dubins path in a discontinuous orientation-restricted environment. The method proposed here builds the shortest path from one pose to the other while avoiding a no-go zone in terms of orientation, and being constrained to move forward. Finally, an application to autonomous sailboats is then provided to validate the feasibility of the planned shortest path in a position keeping scenario.
Value-based Search in Execution Space for Mapping Instructions to Programs

Dor Muhlgay, Jonathan Herzig, Jonathan Berant. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Strength properties of coconut fibre reinforced concrete

The fiber obtained from the dry outer covering of the coconut fiber is termed as “coir”. This fiber has the best toughness among all natural fibres. These coir fibres have the tendency to be used as reinforcement in low cost concrete structures particularly in earthquake regions because of their property of toughness and sturdiness. Some advantages of coir fibers include insect proof, resistant to fungi and decay, provide good insulation against temperature and sound. They remain unaffected by external factors like humidity. They act as reinforcement substance by giving strength to the composite. To the brittle material concrete, a standard stresses or impact load is applied; the durability is 1/10th of its compressive strength. Thus, reinforcing bars are used with concrete in order that they face upto tensile stresses and replace the shortage of malleability and strength. For the preparation of fiber concrete solid cube M20 grade cement is employed. The fiber is mixed with cement, fine aggregate (Msand), coarse aggregate (gravel) and needed quantity no f water that forms the concrete mix. A layer of oil is applied evenly within the cube mould to prevent the concrete from sticking to the mould. This concrete mix is then crammed fully within the solid cube mould. This is then left to dry for 24hrs. This is taken out from the mould and immersed curing is to be done for 3-7 days. The comparison of fiber concrete cubes with the conventional concrete cubes was the main aim of this study. Various tests were conducted to determine its compressive strength, density and crushing load.
Object Detection based Deep Unsupervised Hashing

Recently, similarity-preserving hashing methods have been extensively studied for large-scale image retrieval. Compared with unsupervised hashing, supervised hashing methods for labeled data have usually better performance by utilizing semantic label information. Intuitively, for unlabeled data, it will improve the performance of unsupervised hashing methods if we can first mine some supervised semantic 'label information' from unlabeled data and then incorporate the 'label information' into the training process. Thus, in this paper, we propose a novel Object Detection based Deep Unsupervised Hashing method (ODDUH). Specifically, a pre-trained object detection model is utilized to mining supervised 'label information', which is used to guide the learning process to generate high-quality hash codes. Extensive experiments on two public datasets demonstrate that the proposed method outperforms the state-of-the-art unsupervised hashing methods in the image retrieval task.
Self-adaptive scale pedestrian detection algorithm based on deep residual network

Purpose The conventional pedestrian detection algorithms lack in scale sensitivity. The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection, based on deep residual network (DRN), to address such lacks. Design/methodology/approach First, the “Edge boxes” algorithm is introduced to extract region of interests from pedestrian images. Then, the extracted bounding boxes are incorporated to different DRNs, one is a large-scale DRN and the other one is the small-scale DRN. The height of the bounding boxes is used to classify the results of pedestrians and to regress the bounding boxes to the entity of the pedestrian. At last, a weighted self-adaptive scale function, which combines the large-scale results and small-scale results, is designed for the final pedestrian detection. Findings To validate the effectiveness and feasibility of the proposed algorithm, some comparison experiments have been done on the common pedestrian detection data sets: Caltech, INRIA, ETH and KITTI. Experimental results show that the proposed algorithm is adapted for the various scales of the pedestrians. For the hard detected small-scale pedestrians, the proposed algorithm has improved the accuracy and robustness of detections. Originality/value By applying different models to deal with different scales of pedestrians, the proposed algorithm with the weighted calculation function has improved the accuracy and robustness for different scales of pedestrians.
Named Entity Recognition for Clinical Portuguese Corpus with Conditional Random Fields and Semantic Groups

Considering the difficulties of extracting entities from Electronic Health Records (EHR) texts in Portuguese, we explore the Conditional Random Fields (CRF) algorithm to build a Named Entity Recognition (NER) system based on a corpus of clinical Portuguese data annotated by experts. We acquaint the challenges and methods to classify Abbreviations, Disorders, Procedures and Chemicals within the texts. By selecting a meaningful set of features, and parameters with the best performance the results demonstrate that the method is promising and may support other biomedical tasks, nonetheless, further experiments with more features, different architectures and sophisticated preprocessing steps are needed.
Semi-supervised Turkish Text Categorization with Word2Vec, Doc2Vec and FastText Algorithms

In this study, the performance values of Word2Vec, Doc2Vec and FastText algorithms are compared on the text categorization problem based on a semi-supervised learning technique. The impact of some preprocessing techniques are also analyzed on a corpus that contains approximately 5 million Turkish news documents which are both in labeled and unlabeled manners. Naive Bayes, Support Vector Machines, Artificial Neural Networks, Decision Trees and Logistic Regression classification algorithms are used at the classification phase and the obtained results are shared.
N-qubit system in a pure state: a necessary and sufficient condition for unentanglement

If a pure state of a qubit pair is developed over the four basis states, it is known that an equality between the four coefficients of that development exists if and only if that state is unentangled. This paper considers an arbitrary pure state of an N-qubit system, developed over the 2^N basis states. It is shown that the state is unentangled if and only if a well-chosen collection of (2^N-(N+1)) equalities between the 2^N coefficients of that development is verified. The number of these equalities is large a soon as N = 10, but it is shown that this set of equalities may be classified into (N-1) subsets, which should facilitate their manipulation. This result should be useful e.g. in the contexts of Blind Quantum Source Separation (BQSS) and Blind Quantum Process Tomography (BQPT), with an aim which should not be confused with that found when using the concept of equivalence of pure states through local unitary transformations.
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules

We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems.Recent advances in core components of dialogue systems, such as speech recognition, language understanding, dialogue management, and language generation, harness deep learning to achieve state-ofthe-art performance.The original OpenDial, implemented in Java, provides a plugin architecture to integrate external modules, but lacks Python bindings, making it difficult to interface with popular deep learning frameworks such as Tensorflow or PyTorch.To this end, we re-implemented OpenDial in Python and extended the toolkit with a number of novel functionalities for neural dialogue state tracking and action planning.We describe the overall architecture and its extensions, and illustrate their use on an example where the system response model is implemented with a recurrent neural network.
The First-Break Detection For Real Seismic Data With Use of Convolutional Neural Network

Summary In this study, we appraise a convolutional neural network for the detection of the first breaks on the real 3D seismic data set. The use of convolution as a learning kernel is followed by an assumption that the seismic trace can be considered as a convolution of source signal with the reflectivity function. The investigation area includes mixed elevations, floodplains of the rivers and the regions of strong permafrost, where the shingling effect is observed. We consider the first-break detection for each trace independently to preserve the complicated structure of the arrival times. The proposed approach was apprised on real exploration 3D seismic data set with size over 4.5 million traces. This test showed that the error between the original and predicted first breaks is not more than 3 samples for 95 percents of data set. The final quality control of picking results was established by the calculation of static corrections and computing seismic stacks, which showed that the proposed approach provides better results.
Usefulness of Information for Goal Achievement

This paper focuses on modelling information usefulness. More precisely, it aims at characterizing how useful a piece of information is for a cognitive agent which has some beliefs and goals. The paper presents three different approaches. We take Information Retrieval as a particular application domain and we compare some existing measures with the usefulness measure introduced in the paper.
Wiener Filtering for Passive Linear Quantum Systems

This paper considers a version of the Wiener filtering problem for equalization of passive linear quantum systems. We demonstrate that taking into consideration the quantum nature of the signals involved leads to features typically not encountered in classical equalization problems. Most significantly, finding a mean-square optimal quantum equalizing filter amounts to solving a nonconvex constrained optimization problem. We discuss two approaches to solving this problem, both involving a relaxation of the constraint. In both cases, unlike classical equalization, there is a threshold on the variance of the noise below which an improvement of the mean-square error cannot be guaranteed.
PROBLEMATIKA PEMBELAJARAN MEMBACA DAN MENULIS PERMULAAN DI SEKOLAH DASAR

&lt;p&gt;&lt;em&gt;Language is a communication tool that is a symbolic system of sound produced by a human speech tool. Language as a means of communication can not only talk, but can be manifested by hand signals or other bodies that have their own rules. Language has a central role in the intellectual, social, and emotional development of students. This means every human being is subject to limitations in the language. The development of children's speaking skills in elementary schools will become the language for their effective and efficient communication. Learning to read in primary school is very important. This is caused by learning that can not only be utilized in improving the language skills of children, more than that, that is to improve students' ability in learning other subjects. The essence of writing skills is to increase the vocabulary insight for students, so that students are encouraged to think dynamic, critical, rational and able to live up to the conditions and goals of good and true teaching. However, on various matters relating to the process of reading and writing beginning in elementary school. This should not be allowed to happen continuously will cause an imbalance in the development of students' thinking ability. In addition to teachers, parents also play an important role in helping to solve problems by learning to read and write the beginning. Here the authors formulate some efforts to overcome this, namely: choosing reading materials, making the media as a song, choose innovative learning media, and motivate students by providing feedback.&lt;/em&gt;&lt;/p&gt;
Twin-Net Descriptor: Twin Negative Mining With Quad Loss for Patch-Based Matching

Local keypoint matching is an important step for computer vision based tasks.In recent years, Deep Convolutional Neural Network (CNN) based strategies have been employed to learn descriptor generation to enhance keypoint matching accuracy.Recent state-of-art works in this direction primarily rely upon a triplet based loss function (and its variations) utilizing three samples: an anchor, a positive and a negative.In this work we propose a novel ''Twin Negative Mining'' based sampling strategy coupled with a Quad loss function to train a deep neural network based pipeline (Twin-Net) for generating a robust descriptor that provides an increased discriminatory power to differentiate between patches that do not correspond to each other.Our sampling strategy and choice of loss function is aimed at placing an upper bound that descriptors of two patches representing same location could be at worst no more dissimilar than the descriptors of two similar looking patches that do-not belong to same 3D location.This results in an increase in the generalization capability of the network and outperforms its existing counterparts when trained over the same datasets.Twin-Net outputs a 128-dimensional descriptor and uses L 2 Distance as the similarity metric, and hence conforms to the classical descriptor matching pipelines such as that of SIFT.Our results on Brown and HPatches datasets demonstrate Twin-Net's consistently better performance as well as better discriminatory and generalization capability as compared to the state-of-art.
A Framework for Citing Nanopublications

In this paper we discuss the role of the Nanopublication (nanopub) model for scholarly publications with particular focus on the citation of nanopubs. To this end, we contribute to the state-of-the-art in data citation by proposing: the nanocitation framework that defines the main steps to create a text snippet and a machine-readable citation given a single nanopub; an ad-hoc metadata schema for encoding nanopub citations; and, an open-source and publicly available citation system.