Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatially Scalable Lossy Coded Caching

We apply the coded caching scheme proposed by Maddah-Ali and Niesen to a multipoint multicasting video paradigm. Partially caching the video files on the wireless devices provides an opportunity to decrease data traffic load in peak hours via sending multicast coded messages to users. In this paper, we propose a two-hop wireless network for video multicasting, where the common coded multicast message is transmitted through different single antenna Edge Nodes (ENs) to multiple antenna users. Each user can decide to decode any EN by using a zero forcing receiver. Motivated by Scalable Video Coding (SVC), we consider successive refinement source coding in order to provide a “softer” tradeoff between the number of decoded ENs and the source distortion at each user receiver. The resulting coding scheme can be seen as the concatenation of Maddah-Ali and Niesen coded caching for each source-coded layer, and multiple description coding. Using stochastic geometry, we investigate the tradeoff between delivery time and peruser average source distortion. The proposed system is spatially scalable in the sense that, for given users' and ENs' spatial density, the achieved distortion-delivery time performance is independent of the coverage area (for in the limit of large area).
Analysis and Improvement of convergence speed in kernel adaptive filters with nonlinear even cost function and pre-tuned dictionary

Kernel algorithms have been proposed to solve nonlinear adaptive filtering problems, as for many machine learning nonlinear issues. However, there is a high cost associated with the calculation of the filter output coming from the kernel approach. This paper revisits the KSIG, which improves the convergence speed in kernel adaptive filtering with an even cost function (Sigmoid Algorithm) and also proposes a new version of the KSIG, with a pre-tuned dictionary (KSIG-PTD), to deal with the high cost in kernel algorithms of the computation of the output. A convergence analysis of the KSIG and the KSIG-PTD is presented, from which there appears a relation between the learning rate and the algorithm’s convergence. Experiments have been conducted, where the KSIG as well as its pre-tuned dictionary version are compared with KLMS both without and with a pre-tuned dictionary. It is shown that there is an increase in the convergence speed in nonlinear adaptive filtering using the kernel approach and a nonlinear even cost function.
LSTM-Based EEG Classification in Motor Imagery Tasks

Classification of motor imagery electroencephalograph signals is a fundamental problem in brain–computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks.
Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA

Neural networks algorithms are commonly used to recognize patterns from different data sources such as audio or vision. In image recognition, Convolutional Neural Networks are one of the most effective techniques due to the high accuracy they achieve. This kind of algorithms require billions of addition and multiplication operations over all pixels of an image. However, it is possible to reduce the number of operations using other computer vision techniques rather than frame-based ones, e.g., neuromorphic frame-free techniques. There exists many neu- romorphic vision sensors that detect pixels that have changed their luminosity. In this study, an event-based convolution engine for FPGA is presented. This engine models an array of leaky integrate and fire neurons. It is able to apply different kernel sizes, from lxl to $7\mathrm {x}7$, which are computed row by row, with a maximum number of 64 different convolution kernels. The design presented is able to process 64 feature maps of $7\mathrm {x}7$ with a latency of $8.98\mu \mathrm {s}. $
Using Fuzzy Fingerprints for Cyberbullying Detection in Social Networks

As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, we study how a recent technique with proven success in similar tasks, Fuzzy Fingerprints, performs when detecting textual cyberbullying in social networks. Despite being commonly treated as binary classification task, we argue that this is in fact a retrieval problem where the only relevant performance is that of retrieving cyberbullying interactions. Experiments show that the Fuzzy Fingerprints slightly outperforms baseline classifiers when tested in a close to real life scenario, where cyberbullying instances are rarer than those without cyberbullying.
A Short Review of Deep Learning Methods for Understanding Group and Crowd Activities

The human behavior analysis in groups and crowds is an important research area in different fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the activities of both, groups and crowds, remains a very challenging problem from sensors to artificial intelligence techniques. Currently, deep learning (DL) methods are achieving great results that are revolutionizing the way to deal with artificial vision problems. The objective of this paper is to briefly review the state of the art in this area of machine learning to solve problems related to image processing and automatic understanding of group and crowd activities detected in video sequences and images. First of all, the review presents the general characteristics of the three main neural networks and their variants: Convolutional Neural Networks (CNNs), Autoencoders (AEs) and Recurrent Neural Network (RNNs). Later, the paper highlights the research work in DL methods for understanding separately group and crowd in sequence of images and the main datasets used for this task. Finally, the different levels of understanding and the number of people considered in the group and crowd is discussed proposing a classification of the analyzed methods using both variables and identifying open challenges in the area.
The Impact of microRNAs in Breast Cancer Angiogenesis and Progression.

OBJECTIVE The study aims to review the recent data considering the expression profile and the role of microRNAs in breast tumorigenesis, and their impact on -the vital for breast cancer progression- angiogenesis. METHODS PubMed was searched for studies focused on data that associate microRNA with breast cancer, using the terms ''breast", "mammary gland", "neoplasia'', "angiogenesis" and ''microRNA'' between 1997-2018. RESULTS Aberrant expression of several circulating and tissue miRNAs is observed in human breast neoplasms with the deregulation of several miRNAs having a major participation in breast cancer progression. Angiogenesis seems to be directly affected by either overexpression or down regulation of many miRNAs, defining the overall prognostic rates. Many miRNAs differentially expressed in breast cancer that reveal a key role in suppression - progression and metastasis of breast cancer along with the contribution of the EGF, TNF-a and EGF cytokines. Conclusion Angiogenesis has proven to be vital for tumor development and metastasis while microRNAs are proposed to have multiple biological roles, including participation in immunosuppressive, immunomodulatory and recent studies reveal their implication in angiogenesis and its possible use as prognostic factors in cancer Even though larger studies are needed in order to reach safe conclusions, important steps are made that reveal the connection of serum microRNA expression to the angiogenic course of breast cancer, while miRNAs could be potential prognostic factors for the different breast cancer types.
Using the Analytic Network Process Method for Prioritizing and Weighing Shift Work Disorders Among the Personnel of Hospitals of Kerman University of Medical Sciences

Introduction: Increasing population, the need for services, and industrialization of societies have led to a growing demand for shift work. Shiftwork causes several disorders, and determining the weight of each disorders is important for their prevention and treatment. Therefore, the purpose of the present study was to use Analytic Network Process (ANP) to prioritize and weigh shift work disorders among the personnel of hospitals of Kerman University of Medical Sciences. Methods: This cross-sectional, descriptive-analytical study was conducted in 2017 among 300 shift work personnel of 10 public hospitals affiliated with Kerman University of Medical Sciences. ANP was used to prioritize and weigh shift work disorders. To this end, the criteria, sub-criteria, and alternatives were initially identified. Then, shift work disorders were categorized into 7 general criteria, 20 sub-criteria, and 3 alternatives. After designing the ANP and determining the effect of each criterion on the sub-criteria, the ANP questionnaire was developed and administered among the shift work personnel, who filled it out based on ANP. Super Decisions was subsequently used to weigh and prioritize shift work disorders. Results: The results indicated that shift work disorders among the nurses included sleep disorders (0.297), psychological disorders (0.275), digestive disorders (0.137), personal life disorders (0.122), etc., in that order of weighing. With respect to the support staff, the major shift work disorders involved sleep disorders (0.252), digestive disorders (0.198), personal life disorders (0.168), and psychological disorders (0.164). Considering security personnel, the top four shift work disorders were sleep disorders (0.201), digestive disorders (0.186), psychological disorders (0.174), and personal life disorders (0.145). Conclusion: According to the findings, sleep disorders had the highest weight in the three studied groups. Moreover, the night shift had the most profound effect on shift work disorders among the personnel in the three groups. It was followed by the evening shift. Morning shift had the lowest influence on shift work disorders. Therefore, the schedules should be taken to prevent these complications in the shift workers. It is suggested that work shift complications be included in the periodic examination program and, in case of discovery of any rhythmic disorder in each shift workers, the person should not remain in the shiftwork group for some time.
The Evolutionary Race: Improving the Process of Evaluating Car Controllers in Racing Simulators

Simulated car races have been used for a long time as an environment where car controlling algorithms can be tested; they are an interesting testbed for all kinds of algorithms, including metaheuristics such as evolutionary algorithms. However, the challenge in the evolutionary algorithms is to design a reliable and effective evaluation process for the individuals that eventually translates into good solutions to the car racing problem: finding a controller that is able to win in a wide range of tracks and with a good quantity of opponents. Evaluating individual car controllers involves not only the design of a proper fitness function representing how good the car controller would be in a competitive race, but also the selection of the best solution for the optimization problem being solved; this decision might not be easy when uncertainty is present in the problem environment; in this case, weather and track conditions as well as unpredictable behavior of other drivers. Creating a methodology for the automatic design of the controller of an autonomous driver for a car racing simulator such as TORCS is an optimization problem which offers all these challenges. Thus, in this paper we describe an analysis and some proposals to improve the evaluation of optimized fuzzy drivers for TORCS over previous attempts to do so. It builds on preliminary results obtained in previous papers as a baseline and aims to obtain a more competitive autonomous driver via redesign of the fitness evaluation procedure; to this end, two different fitness functions are studied in several experiments, along with a novel race-based approach for the selection of the best individual in the evolution.
Targeting Optimization for Internet Advertising by Learning from Logged Bandit Feedback

In the last two decades, online advertising has become the most effective way to sponsor a product or an event. The success of this advertising format is mainly due to the capability of the Internet channels to reach a broad audience and to target different groups of users with specific sponsored announces. This is of paramount importance for media agencies, companies whose primary goal is to design ad campaigns that target only those users who are interested in the sponsored product, thus avoiding unnecessary costs due to the display of ads to uninterested users. In the present work, we develop an automatic method to find the best user targets (a.k.a. contexts) that a media agency can use in a given Internet advertising campaign. More specifically, we formulate the problem of target optimization as a Learning from Logged Bandit Feedback (LLBF) problem, and we propose the TargOpt algorithm, which uses a tree expansion of the target space to learn the partition that efficiently maximizes the campaign revenue. Furthermore, since the problem of finding the optimal target is intrinsically exponential in the number of the features, we propose a tree-search method, called A-TargOpt, and two heuristics to drive the tree expansion, aiming at providing an anytime solution. Finally, we present empirical evidence, on both synthetically generated and real-world data, that our algorithms provide a practical solution to find effective targets for Internet advertising.
Integrating Requirements Specification and Model-Based Testing in Agile Development

In agile development, Requirements Engineering (RE) and testing have to cope with a number of challenges such as continuous requirement changes and the need for minimal and manageable documentation. In this sense, extensive research has been conducted to automatically generate test cases from (structured) natural language documents using Model-Based Testing (MBT). However, the imposed structure may impair agile practices or test case generation. In this paper, inspired by cooperation with industry partners, we propose CLARET, a notation that allows the creation of use case specifications using natural language to be used as central artifacts for both RE and MBT practices. A tool set supports CLARET specification by checking syntax of use cases structure as well as providing visualization of flows for use case revisions. We also present exploratory studies on the use of CLARET to create RE documents as well as on their use as part of a system testing process based on MBT. Results show that, with CLARET, we can document use cases in a cost-effective way. Moreover, a survey with professional developers shows that CLARET use cases are easy to read and write. Furthermore, CLARET has been successfully applied during specification, development and testing of industrial applications.
Cross-modal Metric Learning with Graph Embedding

Metric learning with neural networks has exhibited promising improvements in representation learning. Yet cross-modal retrieval poses a unique challenge to metric learning: how to compute the distance across different modalities such as image and text. Existing neural network based methods tend to establish two branches for images and texts respectively to bridge the modal gap. Also, most of them cannot fully exploit the structure embedded in the multimodal data. This paper introduces embedding layer to provide cross-modal shared representation with non-linearity and reformulates the cross-modal retrieval problem as a graph embedding problem by constructing a multimodal graph. To learn the graph embedding, training pairs and triplets are uniformly generated from random walk sequences on the graph. Then graph pair and triplet constraints are imposed on the embedding layer for structure preservation. Meanwhile, a classifier is trained with labeled data to ensure the learned embedding is coupled with semantic information. For optimization, graph pair and triplet constraints are integrated into a unified multi-task learning with the supervised classifier. Experimental results on the Wiki and NUS-WIDE datasets demonstrate the effectiveness and superiority of the learned embedding for cross-modal retrieval.
Pediatric motor activity during sleep as measured by actigraphy

Study Objectives Provide actigraphic reference values for motor activity during sleep for children and adolescents ages 8-17 years. Methods Participants were 671 healthy community-dwelling children and adolescents (52% female, mean age 13.5 + 2.4 years) from the United States (64%) and Australia (36%). All participants wore an Ambulatory-Monitoring Inc. (AMI, Ardsley, NY) actigraph on their nondominant wrist for ≥5 nights and completed daily sleep diaries. Actigraphy data were scored with standard methods and a validated algorithm. Reference values were calculated for three outcome variables: percent sleep (sleep minutes/sleep period), mean activity count (average activity count over the sleep period), and restlessness measured by the activity index (% of epochs in sleep period > 0). Between-group differences were examined for sex and age group. In addition, changes to activity level across the sleep period were explored. Results All participants had a minimum of three scorable nights of data, with 95% having at least five scorable nights. Reference values are presented by age group and sex, and reference percentiles are provided. Boys were found to have more activity in sleep across the three outcome variables. Age differences were also found for the three outcomes, but a consistent pattern was not detected across variables. Conclusions This study is the first to examine motor activity from actigraphy in a large sample of healthy community-dwelling children and adolescents. Reference tables and percentiles, as well as sample actigrams highlighting different outcomes, are provided for clinicians and researchers who utilize actigraphy in pediatric populations.
Medicinal Materials DNA Barcode Database (MMDBD) version 1.5—one-stop solution for storage, BLAST, alignment and primer design

Abstract Authentication of medicinal materials by deoxyribonucleic acid (DNA) technology is gaining popularity. In 2010, our team has created Medicinal Materials DNA Barcode Database (MMDBD) version 1.0 to provide an interactive database for documenting DNA barcode sequences of medicinal materials. This database now contains DNA barcode sequences of medicinal materials listed in the Chinese Pharmacopoeia, Dietary Supplements Compendium and Herbal Medicine Compendium of the US Pharmacopoeia and selected adulterants. The data archive is regularly updated and currently it stores 62 011 DNA sequences of 2111 medicinal materials. Our team has recently completed the major improvement on the interfaces and incorporated essential bioinformatics tools to facilitate the authentication work. MMDBD version 1.5 contains detailed information of each medicinal material including their material names, medical part, pharmacopeia information, biological classification in rank of family and status on the Convention on International Trade in Endangered Species of Wild Fauna and Flora and the International Union for Conservation of Nature’s Red List of Threatened Species, if any. DNA sequences can be retrieved by search in Latin scientific name, Chinese name, family name, material name, medical part and simplified Chinese character stroke. A `BLAST’-based engine for searching DNA sequences is included in the MMDBD version 1.5. Since primer design is a key step in DNA barcoding authentication, we have integrated the `Clustal Omega alignment tool’ and `Primer3’ in the form of web interface. These new tools facilitate multiple sequence comparison and the design of primers for amplification of a target DNA barcode region, allowing DNA barcoding authentication.
An ANEW based Fuzzy Sentiment Analysis Model

Within the framework of the Intelligent Data Suite (IDS) that is being developed by the company Prometeus Global Solutions, there is a Sentiment Analysis and Opinion Mining module focused on detecting ‘dangerous’ (to the tool user company) messages on Social Media. This can be useful for sending ‘early warnings’ to alert tool user company analysts to take preventive measures against potentially harmful messages. In this paper, a brief description of IDS features, regarding tweets filtering and classification is firstly presented. Affective Norms for English Words (ANEW) provides a set of normative emotional ratings for a large number of words in English and three emotions (valence, arousal and dominance) measures for each term. It is used as a basis for describing a fuzzy model containing five categories for representing the opinion of a microblogging text (very negative, negative, neutral, positive and very positive). The proposal is implemented and tested on the IDS framework.