Reviewerly/exHarmony huggingface dataset:
Comparison of Word Embeddings for Extraction from Medical Records

This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from texts available for decision support systems, supervised machine learning algorithms might be successfully applied. In this work, we developed and compared a prototype of a medical data extraction system based on different artificial neural network architectures to process free medical texts in the Russian language. Three classifiers were applied to extract entities from snippets of text. Multi-layer perceptron (MLP) and convolutional neural network (CNN) classifiers showed similar results to all three embedding models. MLP exceeded convolutional network on pipelines that used the embedding model trained on medical records with preliminary lemmatization. Nevertheless, the highest F-score was achieved by CNN. CNN slightly exceeded MLP when the biggest word2vec model was applied (F-score 0.9763).
RSP-QL $$^{\star }\!$$ : Enabling Statement-Level Annotations in RDF Streams

RSP-QL was developed by the W3C RDF Stream Processing (RSP) community group as a common way to express and query RDF streams. However, RSP-QL does not provide any way of annotating data on the statement level, for example, to express the uncertainty that is often associated with streaming information. Instead, the only way to provide such information has been to use RDF reification, which adds additional complexity to query processing, and is syntactically verbose. In this paper, we define an extension of RSP-QL, called RSP-QL $$^{\star }\!$$ , that provides an intuitive way for supporting statement-level annotations in RSP. The approach leverages the concepts previously described for RDF* and SPARQL*. We illustrate the proposed approach based on a scenario from a research project in e-health. An open-source implementation of the proposal is provided and compared to the baseline approach of using RDF reification. The results show that this way of dealing with statement-level annotations offers advantages with respect to both data transfer bandwidth and query execution performance.
Improving MapReduce scheduler for heterogeneous workloads in a heterogeneous environment

Summary Big data is largely influencing business entities and research sectors to be more data‐driven. Hadoop MapReduce is one of the cost‐effective ways to process large scale datasets and offered as a service over the Internet. Even though cloud service providers promise an infinite amount of resources available on‐demand, it is inevitable that some of the hired virtual resources for MapReduce are left unutilized and makespan is limited due to various heterogeneities that exist while offering MapReduce as a service. As MapReduce v2 allows users to define the size of containers for the map and reduce tasks, jobs in a batch become heterogeneous and behave differently. Also, the different capacity of virtual machines in the MapReduce virtual cluster accommodate a varying number of map/reduce tasks. These factors highly affect resource utilization in the virtual cluster and the makespan for a batch of MapReduce jobs. Default MapReduce job schedulers do not consider these heterogeneities that exist in a cloud environment. Moreover, virtual machines in MapReduce virtual cluster process an equal number of blocks regardless of their capacity, which affects the makespan. Therefore, we devised a heuristic‐based MapReduce job scheduler that exploits virtual machine and MapReduce workload level heterogeneities to improve resource utilization and makespan. We proposed two methods to achieve this: (i) roulette wheel scheme based data block placement in heterogeneous virtual machines, and (ii) a constrained 2‐dimensional bin packing to place heterogeneous map/reduce tasks. We compared heuristic‐based MapReduce job scheduler against the classical fair scheduler in MapReduce v2. Experimental results showed that our proposed scheduler improved makespan and resource utilization by 45.6 % and 47.9 % over classical fair scheduler.
A Design of Experiments Comparative Study on Clustering Methods

Cluster analysis is a multivariate data mining technique that is widely used in several areas. It aims to group automatically the n elements of a database into k clusters, using only the information of the variables of each case. However, the accuracy of the final clusters depends on the clustering method used. In this paper, we present an evaluation of the performance of main methods for cluster analysis as Ward, K-means, and Self-Organizing Maps. Differently from many studies published in the area, we generated the datasets using the Design of Experiment (DOE) technique, in order to achieve reliable conclusions about the methods through the generalization of the different possible data structures. We considered the number of variables and clusters, dataset size, sample size, cluster overlapping, and the presence of outliers, as the DOE factors. The datasets were analyzed by each clustering method and the clustering partitions were compared by the Attribute Agreement Analysis, providing invaluable information about the effects of the considered factors individually and about their interactions. The results showed that, the number of clusters, overlapping, and the interaction between sample size and number of variable significantly affect all the studied methods. Moreover, it is possible to state that the methods have similar performances, with a significance level of 5%, and it is not possible to affirm that one outperforms the others.
Classification of Sindhi Headline News Documents based on TF-IDF Text Analysis Scheme

Objectives: Sindhi language, historically rich belongs to Indo-Aryan language with diverse background and diverse dialects.Recent drive in globalization, e-commerce and e-literacy have influenced on languages as well.There are lots of magazines, Sindhi books, newspapers and web material available online, but unluckily still proper dataset is not designed for Sindhi information processing.This research study focuses on the Sindhi language news headline texts dataset and automated tool for the online texts' classification based on the predefined label.Methods/Statistical Analysis: For the collection of datasets, the scraping tool is designed for extraction of the headline news from most popular newspapers: Awami Awaz and Daily Jhoongar.The dataset contains 2800 Sindhi headline news with five categories: 0. Entertainment, 1. Sports, 2. Science and Technology, 3. International, 4. National, 5. Sindhi news.The dataset is normalized by removing stop words and cleaning the spaces, punctuations and other unnecessary texts.Furthermore, the language feature is analyzed using TF-IDF and vector model.This paper presents Sindhi headline news classification model with implementation of the machine learning classification algorithms, namely.Multinomial NB, Linear SVC, Logistic Regression, MLP classifier, SGD Classifier, Random Forest Classifier, Ridge Classifier.Findings: The results show that the performance of the Linear SVC and MLP Classifier indicate better results on Sindhi headlines news categorization as compared to other classification techniques.This research study helps in improving the automatic classification of Sindhi text headline news.
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations

This paper presents an exploratory study that aims to evaluate the usefulness of backtranslation in Natural Language Generation (NLG) from semantic representations for non-English languages.Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively.Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context.Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG.Results show that there are still many improvements to be made but this is a promising approach.
Sentiment Commonsense Induced Sequential Neural Networks for Sentiment Classification

Although neural networks achieve promising performance in sentence level sentiment classification, most of them are not aware of sentiment commonsense, such as sentiment polarity tags (Positive or Negative) for words, which explicitly determine the sentiment of the sentence in most cases. In this paper, we propose an auxiliary tagging task to integrate sentiment commonsense into sequential neural networks (such as LSTM). We employ the advantage of multitask learning to achieve two goals simultaneously: 1) the sequential learning task accounts for incorporating the semantic information of the surrounding words; 2) the word tagging task ensures the sequential representation still retains the corresponding word tagging information. Besides, considering the most direct way to introduce sentiment information into models as additional knowledge, we further incorporate the additional knowledge enhancing tagging task model to strengthen the effect of sentiment commonsense. We prove the effectiveness of the sentiment commonsense by extensive experiments. The results show that our models exhibit consistent superiority over competitors on three real-word datasets. Specifically, we obtain an accuracy of 55.2%, which is a new state-of-the-art for SST-fine dataset.
Optimal cervical cancer classification using Gauss-Newton representation based algorithm

Cervical cancer is a cancer that associated with infection with Human Papillomavirus (HPV). It occurs when the cells grow abnormally on the lowest portion of the woman’s uterus. Most precancerous cervical cancer cases are on 20s or 30s women, but most cases diagnosed on 50s women. Cervical cancer has slow progression, it is one of the most preventable cancer and can be prevented by effective treatment. So, timely detection is important to know what effective treatment we can use. Machine learning techniques are being largely used to classify cervical cancer because of its precision and its capability to diagnose efficiently. In this paper, to identify the classification of cervical cancer, we have proposed a Gauss Newton representation-based algorithm (GNRBA) methods. Sparse representation is used with training sample selection, and Euclidean distance measure is used in the subset selection. The proposed method is less complexity, as compared to traditional representation method, more effective and easier to implement. This GNBRA method is examined on the cervical cancer database from Kaggle dataset repository. We used Stratified Shuffle Split to split the data. The experimental results show that the proposed GNBRA method give the 93 % accuracy. So, GNBRA method could be an ideal alternative for helping the clinical experts to classify cervical cancer.
Combining Translation Memory with Neural Machine Translation

In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system’s output.
Rosenthal’s Decoding Algorithm for Certain 1-Dimensional Convolutional Codes

The performance of Rosenthal’s decoding algorithm is measured when it is applied to certain family of 1-dimensional MDS convolutional codes. It is shown that the performance of this algorithm depends on the algebraic structure of the convolutional code we are working with.
Oil Film Detection Under Solar Irradiation and Image Processing

To detect oil leakage from a power transformer at its initial stage to prevent environmental pollution, we aimed to detect and localize leaked oil film that had adhered to the surface of a power transformer. Since the inspection for oil leakage is generally carried out in the daytime, in this research, a method to localize the oil film under solar irradiation was developed to support the inspection. To detect oil leakages, observation of the emitted fluorescence when the oil is irradiated with ultraviolet (UV) light has been commonly adopted. However, the fluorescence of an oil film adhered to a surface is too weak to recognize under solar irradiation. Therefore, the detection of strong specular reflection light of the oil film when it was irradiated with a light containing a visible component was used to additionally detect leaked oil film. Moreover, image processing using deep learning was applied on the obtained images to automatically recognize the oil film. By using the proposed oil-localization method, the oil-adhered area on the surface was recognized with a high accuracy of 99.2%. The proposed method is expected to be used to detect oil leakages for various types of oil filled equipment.
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks. To obtain the large number of networks needed for this study, we adopt one-shot neural architecture search, training a large network for once and then finetuning the sub-networks sampled therefrom. The sampled architectures together with the accuracies they achieve provide a rich basis for our study. Our "robust architecture Odyssey" reveals several valuable observations: 1) densely connected patterns result in improved robustness; 2) under computational budget, adding convolution operations to direct connection edge is effective; 3) flow of solution procedure (FSP) matrix is a good indicator of network robustness. Based on these observations, we discover a family of robust architectures (RobNets). On various datasets, including CIFAR, SVHN, Tiny-ImageNet, and ImageNet, RobNets exhibit superior robustness performance to other widely used architectures. Notably, RobNets substantially improve the robust accuracy (~5% absolute gains) under both white-box and black-box attacks, even with fewer parameter numbers. Code is available at https://github.com/gmh14/RobNets.
Improving Arabic Lemmatization Through a Lemmas Database and a Machine-Learning Technique

Lemmatization is a key preprocessing step and an important component for many natural language applications. For Arabic language, lemmatization is a complex task due to Arabic morphology richness. In this paper, we present a new lemmatizer that combines a lexicon-based approach with a machine-learning-based approach to get the lemma solution. The lexicon-based step provides a context-free lemmatization and the most appropriate lemma according to the sentence context is detected using the Hidden Markov Model. The developed lemmatizer evaluations yield to over than 91% of accuracy. This achievement outperforms the state of the art Arabic lemmatizers.
Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks

Vision-Language Navigation (VLN) is a task where agents learn to navigate following natural language instructions. The key to this task is to perceive both the visual scene and natural language sequentially. Conventional approaches exploit the vision and language features in cross-modal grounding. However, the VLN task remains challenging, since previous works have neglected the rich semantic information contained in the environment (such as implicit navigation graphs or sub-trajectory semantics). In this paper, we introduce Auxiliary Reasoning Navigation (AuxRN), a framework with four self-supervised auxiliary reasoning tasks to take advantage of the additional training signals derived from the semantic information. The auxiliary tasks have four reasoning objectives: explaining the previous actions, estimating the navigation progress, predicting the next orientation, and evaluating the trajectory consistency. As a result, these additional training signals help the agent to acquire knowledge of semantic representations in order to reason about its activity and build a thorough perception of the environment. Our experiments indicate that auxiliary reasoning tasks improve both the performance of the main task and the model generalizability by a large margin. Empirically, we demonstrate that an agent trained with self-supervised auxiliary reasoning tasks substantially outperforms the previous state-of-the-art method, being the best existing approach on the standard benchmark.
Comparison of Beginning R Students’ Perceptions of Peer-Made Plots Created in Two Plotting Systems: A Randomized Experiment

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Sustained Self-Supervised Pretraining for Temporal Order Verification

Self-Supervised Pretraining (SSP) has been shown to boost performance for video related tasks such as action recognition and pose estimation. It captures important spatiotemporal constraints which act as an implicit regularizer. This work seeks to leverage upon temporal derivatives and a novel sampling algorithm for sustained (long term) SSP. Main limitations of our baseline approach are – its inadequacy to capture sustained temporal information, weaker sampling algorithm, and the need for parameter tuning. This work analyzes the Temporal Order Verification (TOV) problem in detail, by incorporating multiple temporal derivatives for temporal information amplification and using a novel sampling algorithm that does not need manual parameter adjustment. The key idea is that image-only tuples contain less information and become virtually indiscriminating in case of cyclic events, this can be attenuated by fusing temporal derivatives with the image-only tuples. We explore a few simple yet powerful variants for TOV. One variant uses Motion History Images (MHI), others use optical flow. The proposed TOV algorithm has been compared with previous works along with validation on challenging benchmarks – HMDB51 and UCF101.
Self-adaptive Executors for Big Data Processing

The demand for additional performance due to the rapid increase in the size and importance of data-intensive applications has considerably elevated the complexity of computer architecture. In response, systems offer pre-determined behaviors based on heuristics and then expose a large number of configuration parameters for operators to adjust them to their particular infrastructure. Unfortunately, in practice this leads to a substantial manual tuning effort. In this work, we focus on one of the most impactful tuning decisions in big data systems: the number of executor threads. We first show the impact of I/O contention on the runtime of workloads and a simple static solution to reduce the number of threads for I/O-bound phases. We then present a more elaborate solution in the form of self-adaptive executors which are able to continuously monitor the underlying system resources and detect contentions. This enables the executors to tune their thread pool size dynamically at runtime in order to achieve the best performance. Our experimental results show that being adaptive can significantly reduce the execution time especially in I/O intensive applications such as Terasort and PageRank which see a 34% and 54% reduction in runtime.
An Aerial Robot Path Follower Based on the ‘Carrot Chasing’ Algorithm

This paper presents a three-dimensional path follower implementation for an aerial robot based on the carrot-chasing algorithm. The main objective was to improve the performance of the position controller of the PX4 autopilot when following a list of waypoints. This autopilot is widely used in the aerial robotics community, but we needed to improve its performance for navigation in cluttered environments. Different simulations have been carried out under the ROS (Robotic Operating System) environment for the comparison between the position controller of the PX4 and the proposed path follower. In addition, we have implemented different modes to generate the path from the input list of waypoints that are also analyzed in our simulation environment.
Peel the Onion: Recognition of Android Apps Behind the Tor Network

According to Freedom on the Net 2017 report [15] more than 60% of World’s Internet users are not completely free from censorship. Solutions like Tor allow users to gain more freedom, bypassing these restrictions. For this reason they are continuously under deep observation to detect vulnerabilities that would compromise users anonymity. The aim of this work is showing that Tor is vulnerable to app deanonymization attacks on Android devices through network traffic analysis. While attacks against Tor anonymity have already gained considerable attention in the context of website fingerprinting in desktop environments, to the best of our knowledge this is the first work that addresses a similar problem on Android devices. For this purpose, we describe a general methodology for performing an attack that allows to deanonymize the apps running on a target smartphone using Tor. Then, we discuss a Proof-of-Concept, implementing the methodology, that shows how the attack can be performed in practice and allows to assess the deanonymization accuracy that it is possible to achieve. Moreover, we made the software of the Proof-of-Concept available, as well as the datasets used to evaluate it. In our extensive experimental evaluation, we achieved an accuracy of $$97\%$$ .
From Key Encapsulation to Authenticated Group Key Establishment—A Compiler for Post-Quantum Primitives †

Assuming the availability of an existentially unforgeable signature scheme and an (IND- CCA secure) key encapsulation mechanism, we present a generic construction for group key establishment. The construction is designed with existing proposals for post-quantum cryptography in mind. Applied with such existing proposals and assuming their security, we obtain a quantum-safe three-round protocol for authenticated group key establishment that requires only one signature per protocol participant.
CAT: CRF-based ASR Toolkit

In this paper, we present a new open source toolkit for automatic speech recognition (ASR), named CAT (CRF-based ASR Toolkit). A key feature of CAT is discriminative training in the framework of conditional random field (CRF), particularly with connectionist temporal classification (CTC) inspired state topology. CAT contains a full-fledged implementation of CTC-CRF and provides a complete workflow for CRF-based end-to-end speech recognition. Evaluation results on Chinese and English benchmarks such as Switchboard and Aishell show that CAT obtains the state-of-the-art results among existing end-to-end models with less parameters, and is competitive compared with the hybrid DNN-HMM models. Towards flexibility, we show that i-vector based speaker-adapted recognition and latency control mechanism can be explored easily and effectively in CAT. We hope CAT, especially the CRF-based framework and software, will be of broad interest to the community, and can be further explored and improved.