Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Scale Parallel Convolutional Neural Network Based Intelligent Human Identification Using Face Information

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.
Validation of Tagging Suggestion Models for a Hotel Ticketing Corpus

This paper investigates methods for the prediction of tags on a textual corpus that describes hotel staff inputs in a ticketing system. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry. The paper consists of two parts: (i) exploration of existing sample data, which includes statistical analysis and visualisation of the data to provide an overview, and (ii) evaluation of tag prediction approaches. We have included different approaches from different research fields in order to cover a broad spectrum of possible solutions. As a result, we have tested a machine learning model for multi-label classification (using gradient boosting), a statistical approach (using frequency heuristics), and two simple similarity-based classification approaches (Nearest Centroid and k-Nearest Neighbours). The experiment which compares the approaches uses recall to measure the quality of results. Finally, we provide a recommendation of the modelling approach which produces the best accuracy in terms of tag prediction on the sample data.
Application of machine learning to identify Counterfeit Website

Recent years the prevalence of fraudulent websites has become more severe than before. Fraudulent ecommerce websites that sell counterfeit goods not only cost financial damage to consumers but also have a great impact on Internet industry. Nowadays, there is not an effective way to confront these websites. In this paper, we look forward to achieving three goals: find the characteristics of counterfeit websites, train models for classifying ecommerce websites and provide a service to help consumers distinguish counterfeit websites from legitimate ones.
Path Generation Method of UAV Autopilots using Max-Min Algorithm

In recent times, Natural User Interface/Natural User Experience (NUI/NUX) technology has found widespread application across a diverse range of fields and is also utilized for controlling unmanned aerial vehicles (UAVs). Even if the user controls the UAV by utilizing the NUI/NUX technology, it is difficult for the user to easily control the UAV. The user needs an autopilot to easily control the UAV. The user needs a flight path to use the autopilot. The user sets the flight path based on the waypoints. UAVs normally fly straight from one waypoint to another. However, if flight between two waypoints is in a straight line, UAVs may collide with obstacles. In order to solve collision problems, flight records can be utilized to adjust the generated path taking the locations of the obstacles into consideration. This paper proposes a natural path generation method between waypoints based on flight records collected through UAVs flown by users. Bayesian probability is utilized to select paths most similar to the flight records to connect two waypoints. These paths are generated by selection of the center path corresponding to the highest Bayesian probability. While the K-means algorithm-based straight-line method generated paths that led to UAV collisions, the proposed method generates paths that allow UAVs to avoid obstacles.
A Low-Cost Named Entity Recognition Research Based on Active Learning

Named entity recognition (NER) is an indispensable and very important part of many natural language processing technologies, such as information extraction, information retrieval, and intelligent Q & A. This paper describes the development of the AL-CRF model, which is a NER approach based on active learning (AL). The algorithmic sequence of the processes performed by the AL-CRF model is the following: first, the samples are clustered using the k-means approach. Then, stratified sampling is performed on the produced clusters in order to obtain initial samples, which are used to train the basic conditional random field (CRF) classifier. The next step includes the initiation of the selection process which uses the criterion of entropy. More specifically, samples having the highest entropy values are added to the training set. Afterwards, the learning process is repeated, and the CRF classifier is retrained based on the obtained training set. The learning and the selection process of the AL is running iteratively until the harmonic mean F stabilizes and the final NER model is obtained. Several NER experiments are performed on legislative and medical cases in order to validate the AL-CRF performance. The testing data include Chinese judicial documents and Chinese electronic medical records (EMRs). Testing indicates that our proposed algorithm has better recognition accuracy and recall rate compared to the conventional CRF model. Moreover, the main advantage of our approach is that it requires fewer manually labelled training samples, and at the same time, it is more effective. This can result in a more cost effective and more reliable process.
Systematic mapping study on question answering frameworks over linked data

Employing linked data technologies and semantic endpoints for question answering systems are expanding approaches among the researchers. Therefore, systems that combine syntactic and semantic analysis and enrich input questions by sentence-level recognition are examined. A systematic mapping study is conducted to identify and analyse the studies from major databases, journals and proceedings of conferences or workshops published between 2010 and 2017. With a set of 14 research questions, inclusion and exclusion criteria are specified. 53 studies are selected as primary studies from an initial set of 845 papers. This study provides a mapping while focusing on the methods and identifying the gaps between required and existing approaches. Popular approaches which have gained the most attention among researchers are given as a conclusion. Moreover, a comparison between the authors' study and related work in the literature is given to point out the differences and the contributions of their study. As the result of the comparison, it is concluded that the study is a novel and original topic on question answering frameworks.
A Layered Approach for Summarization and Context Learning from Microblogging Data

Twitter, a microblogging online social network, is one of the most popular information sharing and communication platform. The large user-base and users mutual interactions generate massive amount of data that are rich source of information for predictive modeling, sentiment analysis, opinion mining, and other text information processing tasks. Understanding context embedded within text corpus and generating a contextual summary of the corpus is one of the promising research directions in the field of data analytics. In this paper, we present a layered graph-based approach using both content and structural data to analyze and summarize tweets at different levels of granularity. The proposed approach models tweets as a multi-dimensional graph and applies random walk to identify most informative tweets, which are further processed using a graph-theoretic approach, LexRank, to identify most informative sentences for summary generation. Finally, the summary texts are analyzed using TextRank algorithm to identify prominent keywords conceptualizing the context of the underlying corpus. The proposed summary generation and context learning approach is evaluated over four different real-world Twitter datasets using standard information retrieval metrics.
Human Language Technologies: Key Issues for Representing Knowledge from Textual Information

This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government, Ministerio de Educacion, Cultura y Deporte and Ayudas Fundacion BBVA a equipos de investigacion cientifica 2016 through the projects TIN2015-65100-R, TIN2015-65136-C2-2-R, PROMETEU/2018/089,“Plataforma inteligente para recuperacion, analisis y representacion de la informacion generada por usuarios en Internet” (GRE16-01) and “Analisis de Sentimientos Aplicado a la Prevencion del Suicidio en las Redes Sociales” (ASAP).
Evaluation of Japanese Text Characteristics with Simplified Corpora using Base Selection

On the Internet, contents with different expressions of difficulty are mixed even though they have almost the same contents. In this research, we propose a method to compare two types of documents with different difficulty, and select a characteristic related to difficulty of expression from various characteristics related to text. The characteristics of relevant simplified corpora are critical to people of all ages. In our proposed method, thousands of text characteristics are compressed and converted by Non-negative Matrix Factorization(NMF), and a basis for characterizing the simplified document is selected. As a result of applying the proposed method to two kinds of environment white papers, it became clear that an effective basis can be selected. We evaluated the text characteristics in the NMF Base of the results using a classifier and a Bayesian Network. The classifier yielded good results. The Bayesian Network result described the characteristics of causal relationships.
Community Detection Applied on Big Linked Data

The Linked Open Data (LOD) Cloud has more than tripled its sources in just six years (from 295 sources in 2011 to 1163 datasets in 2017). The actual Web of Data contains more then 150 Billions of triples. We are assisting at a staggering growth in the production and consumption of LOD and the generation of increasingly large datasets. In this scenario, providing researchers, domain experts, but also businessmen and citizens with visual representations and intuitive interactions can significantly aid the exploration and understanding of the domains and knowledge represented by Linked Data. Various tools and web applications have been developed to enable the navigation, and browsing of the Web of Data. However, these tools lack in producing high level representations for large datasets, and in supporting users in the exploration and querying of these big sources. Following this trend, we devised a new method and a tool called H-BOLD (High level visualizations on Big Open Linked Data). H-BOLD enables the exploratory search and multilevel analysis of Linked Open Data. It offers different levels of abstraction on Big Linked Data. Through the user interaction and the dynamic adaptation of the graph representing the dataset, it will be possible to perform an effective exploration of the dataset, starting from a set of few classes and adding new ones. Performance and portability of H-BOLD have been evaluated on the SPARQL endpoint listed on SPARQL ENDPOINT STATUS. The effectiveness of H-BOLD as a visualization tool is described through a user study.
Chasing Similarity: Distribution-aware Aggregation Scheduling

Parallel aggregation is a ubiquitous operation in data analytics that is expressed as GROUP BY in SQL, reduce in Hadoop, or segment in TensorFlow. Parallel aggregation starts with an optional local pre-aggregation step and then repartitions the intermediate result across the network. While local pre-aggregation works well for low-cardinality aggregations, the network communication cost remains significant for high-cardinality aggregations even after local pre-aggregation. The problem is that the repartition-based algorithm for high-cardinality aggregation does not fully utilize the network. In this work, we first formulate a mathematical model that captures the performance of parallel aggregation. We prove that finding optimal aggregation plans from a known data distribution is NP-hard, assuming the Small Set Expansion conjecture. We propose GRASP, a GReedy Aggregation Scheduling Protocol that decomposes parallel aggregation into phases. GRASP is distribution-aware as it aggregates the most similar partitions in each phase to reduce the transmitted data size in subsequent phases. In addition, GRASP takes the available network bandwidth into account when scheduling aggregations in each phase to maximize network utilization. The experimental evaluation on real data shows that GRASP outperforms repartition-based aggregation by 3.5x and LOOM by 2.0x.
Robust Proximal Support Vector Regression Based on Maximum Correntropy Criterion

The robustness problem of the classical proximal support vector machine for regression estimation (PSVR) when confronting with samples in the presence of outliers is addressed in this paper. Correntropy is a local similarity measure between two arbitrary variables and has been proven the insensitivity to noises and outliers. Based on the maximum correntropy criterion (MCC), a correntropy-based robust PSVR framework is proposed, named as RPSVR-MCC. The half-quadratic optimization method is employed to solve the resultant optimization, and an iterative algorithm is developed to solve RPSVR-MCC. In each iteration, the complex optimization can be converted to a linear system of equations which can be easily solved by the widely popular optimization techniques. The experimental results on synthetic datasets and real-world benchmark datasets demonstrate that the effectiveness of the proposed method. Moreover, the superiority of the proposed algorithm is more evident in noisy environment, especially in the presence of outliers.
Towards a Learning Optimizer for Shared Clouds

Query optimizers are notorious for inaccurate cost estimates, leading to poor performance. The root of the problem lies in inaccurate cardinality estimates, i.e., the size of intermediate (and final) results in a query plan. These estimates also determine the resources consumed in modern shared cloud infrastructures. In this paper, we present C ARD L EARNER , a machine learning based approach to learn cardinality models from previous job executions and use them to predict the cardinalities in future jobs. The key intuition in our approach is that shared cloud workloads are often recurring and overlapping in nature, and so we could learn cardinality models for overlapping subgraph templates. We discuss various learning approaches and show how learning a large number of smaller models results in high accuracy and explainability. We further present an exploration technique to avoid learning bias by considering alternate join orders and learning cardinality models over them. We describe the feedback loop to apply the learned models back to future job executions. Finally, we show a detailed evaluation of our models (up to 5 orders of magnitude less error), query plans (60% applicability), performance (up to 100% faster, 3x fewer resources), and exploration (optimal in few 10s of executions).
A Framework for Processing Skyline Queries for a Group of Mobile Users

Skyline processing, an established preference evaluation technique, aims at discovering the best objects, i.e. those that are not dominated by any other objects, in satisfying the user's preferences. Most of the skyline approaches are limited to a single user query. However, in real world scenario, due to the advancement of technology, adhoc meetings are becoming more and more common. Thus, it is necessary to consider the preferences of a group of users, when they intend to meet while they are on the move. While there are studies that consider the preferences of a group of users, the skyline objects derived by their solutions are noncontinuous as they did not take into consideration the movements of the users, i.e. the current locations. Therefore, in this study we present our proposed framework which aims at deriving skylines for a group of users while they are moving (i.e. mobile users) towards an undecided meeting point. The skylines which are the objects recommended to be visited by the group of mobile users are derived by analysing the locations of the mobile users, i.e. spatial attributes, as well as the spatial and non-spatial attributes of the objects. Since these users are moving, thus the skylines are continuously derived. Several experiments have been conducted and the results show that our proposed framework outperforms the previous work with respect to CPU time.
Feature extraction based on information gain and sequential pattern for English question classification

The purpose of question classification (QC) is to assign a question to an appropriate category from the set of predefined categories that constitute a question taxonomy. Selected question features are able to significantly improve the performance of QC. However, feature extraction, particularly syntax feature extraction, has a high computational cost. To maintain or enhance performance without syntax features, this study presents a hybrid approach to semantic feature extraction and lexical feature extraction. These features are generated by improved information gain and sequential pattern mining methods, respectively. Selected features are then fed into classifiers for questions classification. Benchmark testing is performed using the public UIUC data set. The results reveal that the proposed approach achieves a coarse accuracy of 96% and fine accuracy of 90.4%, which is superior to existing methods.
Pilomotor seizures marked by infraslow activity and acetazolamide responsiveness

A patient with pilomotor seizures post anti‐LGI1 limbic encephalitis, refractory to immunotherapy and anti‐epileptic drugs, was investigated with electroencephalography and magnetoencephalography. Seizures occurred daily (14.9 ± 4.9/day), with catamenial exacerbation, inducible by hyperventilation. Anterior temporal ictal onsets were heralded (by ~15 sec) by high amplitude ipsilateral electromagnetic infraslow activity. The catamenial/ventilatory sensitivity and the infraslow activity (reflecting glial depolarization) suggested an ionic, CO2/pH‐related glioneuronal mechanism. Furosemide decreased seizure frequency by ~33%. Acetazolamide led to immediate seizure freedom, but lost efficacy with daily treatment. A cycling acetazolamide regimen (2 days on, 4 days off) plus low‐dose topiramate maintained >95% reduction (0.5 ± 0.9/day) in seizures.
Application of built-in adjuvants for epitope-based vaccines

Several studies have shown that epitope vaccines exhibit substantial advantages over conventional vaccines. However, epitope vaccines are associated with limited immunity, which can be overcome by conjugating antigenic epitopes with built-in adjuvants (e.g., some carrier proteins or new biomaterials) with special properties, including immunologic specificity, good biosecurity and biocompatibility, and the ability to vastly improve the immune response of epitope vaccines. When designing epitope vaccines, the following types of built-in adjuvants are typically considered: (1) pattern recognition receptor ligands (i.e., toll-like receptors); (2) virus-like particle carrier platforms; (3) bacterial toxin proteins; and (4) novel potential delivery systems (e.g., self-assembled peptide nanoparticles, lipid core peptides, and polymeric or inorganic nanoparticles). This review primarily discusses the current and prospective applications of these built-in adjuvants (i.e., biological carriers) to provide some references for the future design of epitope-based vaccines.
Difference of neuromuscular responses by additional loads during plyometric jump

Plyometric training is a training method to increase the motor output, stretch-shortening cycle which could be associated with power output. To increase the neuromuscular output, various training variables have been incorporated in training programs. Weight vest is one of the variables to develop it. However, how much load can effectively develop the neural response is still not clearly understood. The aim of this study was to identify the effects of additional external loads on neuromuscular response of lower body during plyometric jump. Total 19 men performed jump tests with weight vest (two jumps in each additional load; 0%, 10%, 15% and 20% of bodyweight [BW]). During the tests, neuromuscular responses of lower extremity were measured. In vertical jump, 0%BW group was higher than the other heavier loads. In rate of force development (RFD), 10%BW was higher than 15%BW and 20%BW. In 0–30 msec of interval RFD, the heavier load groups were greater than 0%BW and in 0–50 msec, 15%BW and 20%BW were higher than 0%BW. In neuromuscular efficiency (NME), 15%BW and 20%BW were greater than 0%BW in ankle joint. This research indicated that plyometric jump with additional load causes greater RFD and NME of lower extremity compared with jump training without additional load. During weight vest plyometric jump, 10%–20% of BW load is advantageous to NME of lower body and 10% of BW load is effective to develop RFD of lower extremity.
The impact of short and long sleep duration on instrumental activities of daily living among stroke survivors

Objective Stroke survivors generally have problems completing instrumental activities of daily living (IADL; eg, preparing meals, chores, taking a bath, and managing finances). However, it is unclear how stroke survivors might stave off IADL issues. Studies indicating that sleep has restorative neurological effects provide potential mechanisms to address issues with IADL. The aim of this study was to ascertain the association between sleep duration (short or long sleep duration) and IADL among stroke survivors and those without a stroke history. Methods Data of 486,619 participants were analyzed from the 2000 to 2015 National Health Interview Survey (NHIS), a nationally representative sample. Measures of self-reported stroke, sociodemographic variables, sleep duration, and IADL problems were collected. Binary logistic regression was utilized to analyze the relationship of short (≤6 hours) and long (≥9 hours) sleep duration with limitations to IADL. Results Of the sample, 3% reported a physician-diagnosed stroke event. The mean age was 45.73 years; 52.7% were female; 77.4% were White; 14.2% were Black; 41.3% were married, 62.7% were employed; 31.1% reported that annual family income was less than $35,000; 87% reported good-to-excellent health; and 29.7% reported short sleep (≤6 hours). Approximately 30% of stroke survivors reported IADL problems, and 34.4% who reported IADL problems were short sleepers. Among stroke survivors, long sleepers were 97% more likely than average sleepers to report IADL problems (OR =1.97, 95% CI =1.71–2.26, P<0.001) adjusting for age, sex, race, marital status, poverty, and health. Conclusion Findings from our study indicate that, among stroke survivors, long sleepers were more likely to report IADL problems compared to average sleepers (7–8 hours). Future studies should investigate other potential mediators such as severity of stroke, medication, comorbidities, level of impairment, and whether improving sleep among stroke survivors may improve IADL.