Reviewerly/exHarmony huggingface dataset:
Metadata filtering for user-friendly centralized biometric authentication

While biometric authentication for commercial use so far mainly has been used for local device unlock use cases, there are great opportunities for using it also for central authentication such as for remote login. However, many current biometric sensors like for instance mobile fingerprint sensors have too large false acceptance rate (FAR) not allowing them, for security reasons, to be used in larger user group for central identification purposes. A straightforward way to avoid this FAR problem is to either request a user unique identifier such as a device identifier or require the user to enter a unique user ID prior to making the biometric matching. Usage of a device identifier does not work when a user desires to authenticate on a previously unused device of a generic type. Furthermore, requiring the user at each login occasion to enter a unique user ID, is not at all user-friendly. To avoid this problem, we in this paper investigate an alternative, most user-friendly approach, for identification in combination with biometric-based authentication using metadata filtering. An evaluation of the adopted approach is carried out using realistic simulations of the Swedish population to assess the feasibility of the proposed system. The results show that metadata filtering in combination with traditional biometric-based matching is indeed a powerful tool for providing reliable, and user-friendly, central authentication services for large user groups.
Efficient Catheter Segmentation in 3D Cardiac Ultrasound using Slice-Based FCN With Deep Supervision and F-Score Loss

Fast and accurate catheter segmentation in 3D ultrasound (US) can improve the outcome and efficiency of cardiac interventions. In this paper, we propose an efficient catheter segmentation method based on a fully convolutional neural network (FCN). The FCN is based on a pre-trained VGG-16 model, which processes the 3D US volumes slice by slice. To enhance its performance, we modify its structure by skipping connections under a deep supervision structure, which is learned with an F-score loss function. Our method can exploit more contextual information and increase the detection of catheter-like voxels. We collected a challenging ex-vivo dataset (92 3D US images) from porcine hearts with an RF-ablation catheter inside. Our experiments on this dataset show that the proposed method achieves a segmentation performance with an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> score of 65.2% with a highly efficient inference around 1.1 sec. per volume.
STATISTICAL SIMULATIONS METHOD IN APPLIED STATISTICS

The new paradigm of mathematical research methods is based on the effective application of information and communication technologies both in calculating the characteristics of the methods of data analysis and in simulation modeling. Pseudo-random number generators underlie many modern data analysis technologies. To solve specific applied problems, researchers permanently develop the new methods for processing statistical data, i.e., measurement results (observations, tests, analyzes, experiments) and expert estimations. The properties of each newly proposed method must be studied. The intellectual tools are limit theorems and method of statistical simulations (Monte-Carlo method). In 2016, our journal opened a discussion on the current state and prospects for the development of statistical modeling, i.e., the theory and practice of applicating the method of the statistical simulations (Monte-Carlo method), and various variants of the simulation. The previous discussion about the properties of such generators was conducted in our journal in 1985 - 1993. This article is devoted to application of the statistical simulations method to the study of the properties of statistical criteria for testing the homogeneity of two independent samples. We consider: the Kramer - Welch criterion, which coincides with Student's criterion when sample sizes are equal; the criteria of Lord, Wilcoxon (Mann - Whitney), Wolfowitz, Van der Waerden, Smirnov, со 2 (Lehmann - Rosenblatt). It is necessary to set the distribution functions of the elements of two samples. We use the normal and Weibull - Gnedenko distributions. It is shown advisable to use the Lehmann - Rosenblatt со 2 test when testing the hypothesis of coincidence of the distribution functions of two samples. If there is a reason to assume that the distributions differ mainly in the shift, then the Wilcoxon and Van der Waerden criteria can be used. However, even in this case, the со 2 test may be more powerful. In the general case, apart from the Lehmann - Rosenblatt criterion, the use of the Smirnov criterion is permissible, taking into account the difference between the real level of significance and the nominal one. The frequency of the discrepancies of statistical findings based on different criteria is studied.
Parallel Multidimensional Lookahead Sorting Algorithm

This paper presents a new parallel structured lookahead multidimensional sorting algorithm. Our algorithm can be based on any sequential sorting algorithm. The amount of parallelism can be controlled using several parameters such as the number of threads, word size, memory/processor communication overhead, and the dimension of the algorithm. The proposed technique is ideally suited for general purpose graphic processing units and shared-memory massively parallel processor systems. It ensures that data being processed exhibits temporal and spatial locality to maximize the utilization of processor cache. The algorithm achieves a speedup even when a single processor is used. A lookahead algorithm is also proposed to achieve even higher speedup. The performance of the proposed algorithm is verified numerically and experimentally.
Factors Influencing Online Consumer Behavior in the Era of IoT

Consumers feels the fulminate evolution of internet and technologies in everyday life, in the personal devices interconnected with (mobiles, tables, cars). The IoT (internet of things) changes the houses and institutions activities, or holiday services, etc. This trend is present in every field of the current digital economy, too, bringing new devices and modalities of online marketing, such as virtual reality and augmented reality. This is the reason that each company has to face the challenge by having a constant open communication with the consumers, taking advantage of the new technologies facilities and social media channels. However, the companies can extend their market share if they take into account the consumer nostalgia of old times products and services. The paper makes an analysis of the factors that may affect the behavior of consumers using internet to buy/order online content. For the analysis the Least squares model was implemented. The model shows in how much variables such as: mobile equipment used to search the internet on the move, companies using software, such as CRM to analyse clients’ data, consumers’ level of internet skills, consumers’ level of computer skills, consumers interacting on internet with public authorities, influence the consumer behavior.
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract)

The dynamic behavior of urban mobility patterns makes matching taxi supply with demand as one of the biggest challenges in this industry. Recently, the increasing availability of massive broadcast GPS data has encouraged the exploration of this issue under different perspectives. One possible solution is to build a data-driven real-time taxi-dispatching recommender system. However, existing systems are based on strong assumptions such as stationary demand distributions and finite training sets, which make them inadequate for modeling the dynamic nature of the network. In this paper, we propose BRIGHT: a drift-aware supervised learning framework which aims to provide accurate predictions for short-term horizon taxi demand quantities through a creative ensemble of time series analysis methods that handle distinct types of concept drift. A large experimental set-up which includes three real-world transportation networks and a synthetic test-bed with artificially inserted concept drifts, was employed to illustrate the advantages of BRIGHT when compared to S.o.A methods for this problem.
Human aggressiveness and reactions towards uncertain decisions

Big data is a term that defines data sets are so large or complex that traditional data processing applications are inadequate.Data comes with different formats such as Multilanguage, structured, unstructured and emails.Challenges dealing with a huge amount of data includes analysis, search, sharing, transfer, and visualization.Not all the data collected contains useful information, so there's a need to refine this data in order to filter out the useful information.Tweets posted on Twitter are expressed as opinions.These opinions can be used for different purposes such as to take public views on uncertain decisions.These decisions have a direct impact on the user's life such as violations and aggressiveness are common causes.For this purpose, we have collected opinions on some popular decision taken in the past decade from Twitter.We have divided the tweet text into two classes; Anger (negative) and positive.We have proposed a prediction model to predict public opinions towards such decisions.We used Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) classifier for a text classification task.Furthermore, we have also compared SVM results with NB, LR.The research will help us to predict early behaviors and reactions of people before the big consequences of such decisions.Moreover, the results highlight the feasibility of using social media to predict public opinions.
Naranjo Question Answering using End-to-End Multi-task Learning Model

In the clinical domain, it is important to understand whether an adverse drug reaction (ADR) is caused by a particular medication. Clinical judgement studies help judge the causal relation between a medication and its ADRs. In this study, we present the first attempt to automatically infer the causality between a drug and an ADR from electronic health records (EHRs) by answering the Naranjo questionnaire, the validated clinical question answering set used by domain experts for ADR causality assessment. Using physicians' annotation as the gold standard, our proposed joint model, which uses multi-task learning to predict the answers of a subset of the Naranjo questionnaire, significantly outperforms the baseline pipeline model with a good margin, achieving a macro-weighted f-score between 0.3652-0.5271 and micro-weighted f-score between 0.9523-0.9918.
Blockchain in oil and gas: a collaborative approach

Blockchain technology is a distributed ledger of data, vetted before acceptance, encrypted and shared among parties (depending on the configuration) authorised to view it. Due to the potential cost savings, revenue generation, efficiency gains and security; governments, organisations, companies and consortia across many industries are developing proofs of concept with two in oil and gas reportedly now in production. The value chain in the oil and gas industry is characterised by many remote working locations across different geographical regions; involving multiple stakeholders and regulators each with differing criteria to be met. Many opportunities for improvement across the oil and gas value chain have been identified and some are beginning to be addressed by proofs of concept and live blockchain platforms. However, these are being developed via discrete consortia and not as part of a cohesive industry strategy. Standards to promote understanding and adoption are being developed at the global, national and industry level, but this is an area currently lacking in the oil and gas industry. This paper highlights activity in other industries and suggests that the recently formed OOC Blockchain Consortia of Oil and Gas industry participants, if extended to statutory organisations, regulators, standards organisations and academia could be the forum required to accelerate understanding and adoption within the industry to release the currently untapped value.
INGEOTEC at SemEval-2019 Task 5 and Task 6: A Genetic Programming Approach for Text Classification

This paper describes our participation in HatEval and OffensEval challenges for English and Spanish languages.We used several approaches, B4MSA, FastText, and EvoMSA.Best results were achieved with EvoMSA, which is a multilingual and domainindependent architecture that combines the prediction from different knowledge sources to solve text classification problems.
Sparse Deep Tensor Extreme Learning Machine for Pattern Classification

A novel deep architecture, the sparse deep tensor extreme learning machine (SDT-ELM), is presented as a tool for pattern classification.In extending the original ELM, the proposed SDT-ELM gains the theoretical advantage of effectively reducing the number of hidden-layer parameters by using tensor operations, and using a weight tensor to incorporate higher-order statistics of the hidden feature.In addition, the SDT-ELM gains the implementation advantage of enabling the random hidden nodes to be added block by block, with all blocks having the same hidden layer configuration.Moreover, an SDT-ELM without randomness can also achieve better learning accuracy.Extensive experiments with three widely used classification datasets demonstrate that the proposed algorithm achieves better generalization performance.
Finding Vulnerabilities in IoT Devices : Ethical Hacking of Electronic Locks

Internet of Things (IoT) devices are becoming more ubiquitous than ever before, and while security is not that important for every type of device, it is crucial for some. In this thesis, a widely a ...
Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

Companies have gained important advantages by the development of electronic commerce. Consumer evaluations in electronic environment offer great possibilities for analysis. The fact that the consumer opinions are comprised of textual data, analyzes have complicated and challenging process. In recent years, it is seen that text mining methods are used in analyzes in the literature. However, the evaluations of consumers which are formed by short texts make it necessary to realize the analysis with insufficient data. The weighting methods such as Term Frequency and Term Frequency-Inverse Document Frequency as well as common used classification algorithms such as NaÃ¯ve Bayes and Support Vector Machine have some inadequacies in short text analysis. In this study, a grey relational classification model based on Vector Space Model and Bag of Words has been developed. The model was first applied to the positive-negative categorization of the evaluations, then, applied to the classification of negative evaluations. It was determined that the accuracy level of the model is higher than the classification algorithms commonly used in short text. According to the results of the research, 9637 negative evaluations in 24479 consumer opinion were determined, and 50.4% of the negative evaluations were found to have the most problems related to product.
Enhancement of the entanglement generation via randomly perturbed series of external pulses in a nonlinear Bose–Hubbard dimer

We discuss a model of a short Bose–Hubbard dimer involving two anharmonic quantum oscillators mutually coupled with the use of the linear interaction and additionally driven by a series of ultra-short external pulses. We show that under some conditions the system behaves as a two-qubit one, and contrary to its continuously driven counterpart can be a source of maximally entangled states (MES). We discuss the cases when the system is excited only in one mode and in both ones, and additionally when the cross-Kerr-type interaction is present or omitted. We show that the generation of maximally (or almost maximally) entangled states depends on the time of free evolution of oscillators and hence, the proper steering of the changes of the time between subsequent pulses can increase the effectiveness of MES generation. Moreover, we show the influence of the timing between two series of pulses (corresponding to the two modes) on such a process. What is relevant, thanks to the presence of additional randomness in the timings, the states with stronger entanglement can be achieved than for the situation when the time between consecutive pulses remains ideally fixed.
An improved Kernelized Correlation Filter tracking algorithm based on multi-channel memory model

Aiming at the problems of serious occlusions, deformations, background clutters and so on in the process of target tracking, an improved Kernelized Correlation Filter (KCF) tracking algorithm based on multi-channel memory model is proposed in this paper. Firstly, an updating model based on multi-channel memory is established, in which a control channel is used for memorizing target template, and two executive channels are used for memorizing the parameters and feature of classifier. Then, the established multi-channel memory model is introduced into the updating process of classifier. Our experimental results show that the proposed algorithm can achieve accurate and robust target tracking under the conditions of occlusions, deformations and background clutters.
AN INTEGRATED APPROACH TO GROUNDWATER QUALITY ASSESSMENT IN DETERMINING FACTORS THAT INFLUENCE THE GEOCHEMISTRY AND ORIGIN OF SANDSTONE AQUIFER SOUTHERN NIGER DELTA REGION OF NIGERIA

Rapid industrialization and oil exploration activities are believed to have influence on groundwater quality globally, and Niger Delta Region of Nigeria is no exception.Hence, this research is conducted to evaluate factors that affect groundwater origin and its geochemistry.For the purpose of this study, 20 groundwater samples were collected (4 from borehole and 16 from hand-dug wells).The parameters used in the assessment include physical parameters; pH, total dissolved solid and electrical conductivity and chemical parameters such as; major cations and anions.From the findings it was observed that pH values fell within the slightly acidic range with the exception of sample location HG/08 with value of 7.01 which can be considered as neutral.The dominant factors that influence groundwater origin and geochemistry within the study area are mainly precipitation and weathering.From Gibb's plot ninety percent (90 %) of groundwater chemistry is influenced by precipitation.Soltan classification showed that 98 % of groundwater belongs to (Na + -SO4 2¯) , hence it can be classified as deep meteoric (precipitation influenced), while the remaining two percent (2%) is of (Na + -HCO3 ¯) and can be classified as shallow meteoric type.Lastly from relationship between Cl¯/HCO3¯ groundwater was slightly moderately affected by saline water intrusion.
Exploring Keystroke Dynamics and Stylometry Features for Gender Prediction on Chat Data

Online anonymity is considered as one of the great gifts of the Internet, but it also brings dangers to society, such as cybercrime, online sexual abuse and bullying, and love scams. Many people are fond of chatting online to make new friends, but how can they be sure that the person sitting behind the other computer is really the person they claim to be? By studying stylometry and keystroke dynamics features from chat data, it is feasible to reveal the actual gender of an online user. In this paper, we examined stylometry and keystroke dynamics features from chat data, and proposed a Random Forest based gender prediction approach by analyzing these features. In order to evaluate the effectiveness of the proposed approach, a data acquisition was conducted to capture the keystroke dynamics and text information when participants were chatting remotely via Skype. All participants were invited to chat freely on any topic they prefered in order to get to know each other. Based on our experimental result, the proposed approach achieved 72% prediction accuracy by analyzing on this free-text data captured only in 15 minutes.
Fractional Fuzzy Clustering and Particle Whale Optimization-Based MapReduce Framework for Big Data Clustering

Abstract The recent advancements in information technology and the web tend to increase the volume of data used in day-to-day life. The result is a big data era, which has become a key issue in research due to the complexity in the analysis of big data. This paper presents a technique called FPWhale-MRF for big data clustering using the MapReduce framework (MRF), by proposing two clustering algorithms. In FPWhale-MRF, the mapper function estimates the cluster centroids using the Fractional Tangential-Spherical Kernel clustering algorithm, which is developed by integrating the fractional theory into a Tangential-Spherical Kernel clustering approach. The reducer combines the mapper outputs to find the optimal centroids using the proposed Particle-Whale (P-Whale) algorithm, for the clustering. The P-Whale algorithm is proposed by combining Whale Optimization Algorithm with Particle Swarm Optimization, for effective clustering such that its performance is improved. Two datasets, namely localization and skin segmentation datasets, are used for the experimentation and the performance is evaluated regarding two performance evaluation metrics: clustering accuracy and DB-index. The maximum accuracy attained by the proposed FPWhale-MRF technique is 87.91% and 90% for the localization and skin segmentation datasets, respectively, thus proving its effectiveness in big data clustering.
Enhancing Content Marketing Article Detection With Graph Analysis

Recently, more and more people have the preference for obtaining the latest news and posting their views relying on social media.In this way, some opinion leaders would ultimately get a large number of followers.Because of the significant influence imposed by their social accounts, some of them start to post native advertisements in their articles, and the articles that fall within the scope of such a category are generally known as content marketing articles.However, the content marketing articles have the tendency of going viral for the lack of supervision.For instance, some of them include misleading information, which, as a result, would do great harm to the benefits of ordinary consumers.In this paper, we take the initiative to deal with this problem and propose a fundamental approach for the purpose of detecting the content marketing articles based on the semantic features.In accordance with the characteristics shown by the content marketing articles, a novel approach is proposed to enhance the detection based on the sentence and word graph analysis.We extract both the graph-related and community-related features from the graphs of the two types, respectively.After that, a supervised classifier is trained based on a manually labeled dataset, and the evaluation is also conducted for its effectiveness by employing extensive experiments.Finally, the results show that the combination of features of different kinds can improve detection accuracy and recall significantly.Apart from that, an algorithm is also developed to extract the advertising content in a detected content marketing article for the aim of helping remove illegal advertisements from social platforms.Finally, relevant analysis is carried out for the writing patterns of content marketing articles on WeChat Subscription, and some interesting findings are discovered.