Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion Classification Using EEG Signals

This paper proposes a 3D emotional model for classifying emotions of a user while watching a musical video. A standard dataset DEAP (Database for Emotion Analysis using Physiological Signals) Dataset is used for studying and analyzing the human emotions using EEG signals. Participants are shown various videos across multiple trials and their corresponding EEG signals are recorded. The emotional states are classified on the basis of various parameters such as arousal, valence, dominance, and liking for a particular set of video. After relevant pre-processing and noise removal a 3D Emotional model is constructed. The 3D Emotional Model comprising of 8 octants within a Valence-Arousal-Dominance space gives rises to 8 different emotional states namely relaxed, peaceful, bored, disgust, nervous, sad, surprised and excited. The resultant emotional state obtained provides useful insight into the thinking and behaviour of participants in certain scenarios. The EEG based emotional classification can aid the developers to provide relevant recommendations to the user on the basis of his emotional state. Machine learning algorithms like Naïve Bayes, Support Vector Machine (SVM) when applied to the proposed 3D Emotional model classifies the emotion aptly with an accuracy of 78.06% and 58.90% respectively.
Context-based Arabic Word Sense Disambiguation using Short Text Similarity Measure

Word Sense Disambiguation (WSD) is the process of determining which sense of a word is used in a given context. Most of Arabic WSD systems are based generally on the information extracted from the local context of the word to be disambiguated by computing the number of overlapping words between the two concepts definitions. This information is not usually sufficient for a best disambiguation. Because of the short nature of concept definition, we believe that exploiting semantic short text similarity measure can improve the identification process of which sense of a word is used in a context. In this paper, we propose an efficient method for computing the semantic relatedness between senses. To this end, we reintroduce the Web-based Kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple possible concepts. The proposed method has been tested, evaluated and compared using an Arabic short text categorization system in term of the F1-measure. The obtained results show the interest of our proposition.
An Online Transfer Learning Algorithm with Adaptive Cost

Online transfer learning aims to attack an online learning task on a target domain by transferring knowledge from some source domains, which has received more attentions. And most online transfer learning methods adapt the classifier according to its accuracy on new coming data. However, in real-world applications, such as anomaly detection and credit card fraud detection, the cost may be more important than the accuracy. Moreover, the cost usually changes in these online data, which challenges state-of-art-methods. Therefore, this paper introduces the cost of misclassification into transfer-learning of classifier, and proposes a novel online transfer learning algorithm with adaptive cost (OLAC). Firstly, we introduce the label distribution into traditional Hinge Loss Function to compute the cost of classification adaptively. Secondly, we transfer learn the classifier according to its performance on new coming data including both accuracy and cost. Extensive experimental results show that our method can achieve higher accuracy and less classification lost, especially for the samples with higher costs.
Experimental study on speech enhancement using DNN with perceptual weighting

Based on the phenomenon that auditory system is not easily distinguish quantization noise from high energy region of spectrum, an experimental study on speech enhancement using deep neural network with perceptual weighting is presented in this paper. The error criterion of auditory weighting, which is widely used in low bit rate vo-coder is employed by applying a filter on the error spectrum. The filter has a shape of the inverse spectrum with the original signal. Deep neural network is used to learn the nonlinear mapping form the noisy speech signal to the clean speech by minimizing the weighted error spectrum between the estimated speech and target speech. Experimental study is implemented on the TIMIT database corrupted by unmatched noise during training and test stage. The results demonstrate that the proposed method outperform baseline methods in terms of perceptual evaluation of speech quality, log-spectral distortion in most types of noise.
Hyperlocal Media Architecture: Displaying Societal Narratives in Contested Spaces

Media architecture gains societal relevance as a mass communication medium that is able to strengthen a sense of place. However, little is known about the interplay between the content that media architecture displays and the public perception towards the architecture that supports it. With a global refugee crisis currently unfolding, we took up the challenge to explore how media architecture can raise awareness about urgent societal issues among the nearby population. In this paper, we describe the participatory design and in-the-wild evaluation of a media architecture installation that depicts refugee displacement onto the façade of a refugee shelter. Our analysis provides insight into the qualities of hyperlocal media architecture, in terms of (a) inviting community participation, (b) displaying compelling and socially relevant narratives, and (c) stimulating dialogue between communities.
Barriers To HPV Vaccination Among Male Adolescents

Purpose: To identify barriers to implementing practice recommendations regarding HPV (human papillomavirus) vaccination for male adolescent patients among Family Nurse Practitioners (FNPs). Rationale/Background: HPV infection is a source of numerous cancers. More than one-quarter of the HPV-associated cancers in the United States occur in males. The quadrivalent vaccine is approved in young males and is effective in the prevention of genital warts and reducing HPV related cancers yet vaccination rates are low and expected to remain low. Barriers to vaccination exist even after the 2011 recommendation for routine use. Method: Quantitative, surveys. A 22-item instrument was administered to FNPs working in primary care settings. Participants were surveyed regarding financial, logistic, provider, and parental barriers to vaccination among adolescent males Results: Descriptive analysis at both the item and scale level demonstrated that FNPs report financial barriers as the most significant barrier. The barriers of least concern were provider attitudes. Barriers regarding FNPs’ perception of parental attitudes were seen as moderately concerning. Independent samples t-tests showed that FNPs who did not administer the HPV vaccine to male adolescent patients reported having significantly more financing barriers as compared to FNPs who did. Conclusion: Results suggested that financial issues may constrain FNPs’ implementation of practice recommendations for the HPV vaccine and that FNPs who did not administer the HPV vaccination to adolescent male patients may be unable to do so due to financial reasons. Perceptions of parental attitudes are also seen as playing a role in preventing male adolescent
Control of thrips (Enneothrips flavens Moulton.) with synthetic and biological insecticides in different peanut genotypes

In peanut, pests are controlled by application of insecticides throughout the crop cycle, boosting production costs. This study compared the control of thrips (Enneotrhips flavens Moulton) in 10 peanut genotypes by biological and synthetic insecticides in the field in tropical area in Southeastern Brazil (21°13’29.9’’S 48°54’33.0’’W). The experiment was arranged in a randomized block, 10 x 3 factorial design with three replications. The first factor corresponded to 10 peanut genotypes and the second to different insecticides (synthetic, biological and control). The plants were evaluated 36, 44, 51, 59, 65, 72, and 79 days after sowing to determine the number of thrips individuals and the damage on leaflets caused by E. flavens. The plant height (cm), branch length (cm) and pod yield (kg ha-1) were also determined. The data were subjected to analysis of variance and averages compared by Tukey’s test. In prior evaluation carried out before the start of the applications of insecticides (25 DAS), natural infestation of thrips was observed in all genotypes. The genetic variability of lines L. 386 and L. 314 and cultivar IAC 113 was not significant, whereas yield difference was not observed among treatments of insecticides and control, showing tolerance to E. flavens. The synthetic insecticide Engeo PlenoTM showed of 39% higher efficiency (as average) for controlling thrips in peanut. The insecticides Beauveria bassiana (Bals.) (AUIN®) and Metarhizium anisopliae (Metch.) (GR-INN®) have potential to control E. flavens in peanut, but further studies are needed to better define the application date and products doses.
A system for real-time detecting and recognizing object person

Today, artificial intelligence (AI) has become more and more popular in daily life, such as face recognition, speech recognition, automated driving. In this paper, a chained system based on neural network model is proposed to detect certain person in video, who is in some list. In this system, face detection and face recognition play an important role, especially in face recognition, At the same time, it requires high real-time performance. The Viola-Jones[1] algorithm is employed to do face detection work, and for face recognition, a convolution neural network is proposed. After we use this system to do some tests in real conditions, we get a result of high real-time performance. In most instances, object people can be recognized correctly with great fluency.
Public Health Emergency Risk Communication and Social Media Reactions to an Errant Warning of a Ballistic Missile Threat — Hawaii, January 2018

On January 13, 2018, at 8:07 a.m. Hawaii Standard Time, an errant emergency alert was sent to persons in Hawaii. An employee at the Hawaii Emergency Management Agency (EMA) sent the errant alert via the Wireless Emergency Alert (WEA) system and the Emergency Alert System (EAS) during a ballistic missile preparedness drill, advising persons to seek shelter from an incoming ballistic missile. WEA delivers location-based warnings to wireless carrier systems, and EAS sends alerts via television and radio (1). After 38 minutes, at 8:45 a.m., Hawaii EMA retracted the alert via WEA and EAS (2). To understand the impact of the alert, social media responses to the errant message were analyzed. Data were extracted from Twitter* using a Boolean search for tweets (Twitter postings) posted on January 13 regarding the false alert. Tweets were analyzed during two 38-minute periods: 1) early (8:07-8:45 a.m.), the elapsed time the errant alert circulated until the correction was issued and 2) late (8:46-9:24 a.m.), the same amount of elapsed time after issuance of the correction. A total of 5,880 tweets during the early period and 8,650 tweets during the late period met the search criteria. Four themes emerged during the early period: information processing, information sharing, authentication, and emotional reaction. During the late period, information sharing and emotional reaction themes persisted; denunciation, insufficient knowledge to act, and mistrust of authority also emerged as themes. Understanding public interpretation, sharing, and reaction to social media messages related to emergencies can inform development and dissemination of accurate public health messages to save lives during a crisis.
Genetic Advances in COPD: Insights from COPDGene.

Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors. For many years, knowledge of the genetic basis of COPD was limited to Mendelian syndromes, such as alpha-1 antitrypsin deficiency and cutis laxa, caused by rare genetic variants. Fortunately, over the past decade, the proliferation of genome-wide association studies (GWAS), the accessibility of whole genome sequencing, and the development of novel methods for analyzing genetic variation data have led to a substantial increase in our understanding of genetic variants that play a role in COPD susceptibility and COPD-related phenotypes. COPDGene, a multicenter, longitudinal study of over 10,000 current and former cigarette smokers, has been pivotal to these breakthroughs in understanding the genetic basis of COPD. To date, over 20 genetic loci have been convincingly associated with COPD affection status, with additional loci demonstrating association with COPD-related phenotypes such as emphysema, chronic bronchitis, and hypoxemia. In this review, we discuss the contributions of the COPDGene study to the discovery of these genetic associations as well as the ongoing genetic investigations of COPD subtypes, protein biomarkers, and post-GWAS analysis.
Radioadaptive response in peripheral blood leukocytes of occupationally exposed medical staff with investigation of DNA damage by the use of neutral comet assay

Background: ˝RadioadapƟve Response˝ is well-documented phenomenon appeared in low dose ionizing radiaƟon received in vitro and in vivo. OccupaƟonal exposure has always been a great concern for radiaƟon workers; therefore this study was performed to study radioadapƟve response in terms of residual DNA double strand breaks as an endpoint in peripheral blood leukocytes of occupaƟonally exposed persons. Materials and Methods: Peripheral blood leukocytes of medical staff as well as control subjects were obtained, separate cultures were set up and irradiaƟon was performed at a challenge dose of 4 Gy of gamma rays. AŌer 48-hour incubaƟon, the neutral comet assay was performed according to the standard method. AŌer single cell gel electrophoresis, cells were stained with ethidium bromide and observed by the fluorescence microscope for DNA damage. Results: Although there was no staƟsƟcal difference between baseline DNA damages in two examined exposed and control groups, radiaƟon induced adapƟve response was observed in occupaƟonal exposure significantly (p<0.001). Conclusion: The obtained results indicate that adapƟve response in occupaƟonal exposures could be induced in doses lower than detecƟon limit of personal dosimeters leading to enhanced repair mechanism of DNA double strand breaks aŌer irradiaƟon.
Parliamentary Open Data in Scandinavia

This article presents a case study on the use of open data in the Scandinavian parliaments (Norway, Sweden, and Denmark). While the three countries have all opened the gates and provided access to data—for example, on the voting in parliament, debates, and notes from meetings in committees—the uptake and use of data outside the parliaments is limited. While journalists and academia are users of the open data, hackathons and third-party portals are at an explorative level. Still, there are indicators that hackathons can enhance democracy, and parliamentary data can increase political transparency.
Review of Research on Security Monitoring Index System of Open Source Software

With the development of computer technology and the rapid rise of Internet companies, open source software has matured in operating system, database, Web server, mobile operating system and so on. Open source software not only enjoys our convenience, but brings us great security risks. Scholars at home and abroad have done a lot of research on the security evaluation of open source software, and achieved gratifying results. This paper first introduces the development process of open source software, and then evaluates its security in terms of quality, maturity, vulnerability and reliability. a new and practical index system of open source software security monitoring is proposed.
Using stop-motion video as visual indicator to strength children with ASD's attention focus on specific nonverbal social cues to enhance perception judgments and situational awareness

Autism spectrum disorders (ASD) are characterized by a reduced ability to understand the emotional expressions on other people's faces. Increasing evidence indicates that children with ASD might not recognize or understand crucial nonverbal behaviors, which likely causes them to ignore nonverbal social cues, like facial expressions, that usually aid social interaction. In this study, we create a Stop-Motion Video (SMV) material for children with ASD that will catch their attention by signaling through body gesture movements and facial expressions. The participants will draw these social cues into a storyboard in which the therapist can be able to deduce patterns of judgment and awareness of the situation. This strategy provides a specific elements in a video clip let the autism can find and compare same images occur in the dynamic videos; thus, it can be used to help children with ASD increase and drive their attention toward the meaning and emotional value of facial expressions in specific social situations. The study was carried out according to multiple baseline design across participants. After five weeks of training intervention, all 4 participants' scores rose significantly and dramatically during the intervention phase, and remained significantly higher in the maintenance phase than at baseline. The results showed that SMV strategy moderately effective in teaching the children with ASD to focus on target social signals.
Factors influencing the adoption of education gamification within Abu Dhabi/UAE higher education institutions

The use of gamification in education contexts is increasing at a rapid pace, particularly due to its huge potential in motivating and engaging students. This study used an online survey to define the factors that influence the adoption of education gamification in Abu Dhabi. As the results suggest, the need to replace outdated learning methods, the lack of knowledge and skills, and the level of faculty support of education gamification are some of the factors identified by students as having a significant influence on the adoption of education gamification in Abu Dhabi.
Shiga toxin-producing Escherichia coli O26:H11 associated with a cluster of haemolytic uraemic syndrome cases in South Africa, 2017

Introduction Shiga toxin-producing Escherichia coli (STEC) are foodborne pathogens that may cause diarrhoeal outbreaks and occasionally are associated with haemolytic-uraemic syndrome (HUS). We report on STEC O26:H11 associated with a cluster of four HUS cases in South Africa in 2017. Methodology All case-patients were female and aged 5 years and under. Standard microbiological tests were performed for culture and identification of STEC from specimens (human stool and food samples). Further analysis of genomic DNA extracted from bacterial cultures and specimens included PCR for specific virulence genes, whole-genome sequencing and shotgun metagenomic sequencing. Results For 2/4 cases, stool specimens revealed STEC O26:H11 containing eae, stx2a and stx2b virulence genes. All food samples were found to be negative for STEC. No epidemiological links could be established between the HUS cases. Dried meat products were the leading food item suspected to be the vehicle of transmission for these cases, as 3/4 case-patients reported they had eaten this. However, testing of dried meat products could not confirm this. Conclusion Since STEC infection does not always lead to severe symptoms, it is possible that many more cases were associated with this cluster and largely went unrecognized.
Siphonaptera associated with small mammals (Didelphimorphia, Chiroptera, and Rodentia) from northwestern Argentina

Northwestern Argentina (NOA) is one of the least studied areas in Argentina with respect to ectoparasites of the order Siphonaptera; previous investigations, until this study, were scarce and specific. The objective of this study was to contribute to the knowledge of the diversity of siphonaptera that parasitize small mammals from the NOA, emphasizing in their systematics and distribution. Specimens of fleas collected in several localities of NOA, and stored in the “Annexes” of the Coleccion Mamiferos Lillo, Universidad Nacional de Tucuman, Argentina, were studied. Fleas were manually removed from the host pelage, fixed in alcohol 70 % and prepared following conventional techniques for later observation under microscope. For specimen identification, the original descriptions of species and subspecies were reviewed and compared with specimens stored in systematic collections. Nine families, 22 genera, 53 species, and eight subspecies were recorded. A new family, a new genus, and three new species are recorded for the first time in Argentina; five species are new for NOA and nine are new to science. The distribution of 11 species and two subspecies are extended in the NOA, new records are added to different provinces and new flea-hosts associations are reported. The greatest diversity of fleas in the Yungas is the reflection of one of the areas with the highest biodiversity in the Neotropical region, such as the Yungas forests, which also includes mammals, as sigmodontine rodents and bats among them. The similarity analysis among eco-regions showed a major faunistic congruence between the Yungas and the Dry Chaco. The greatest differentiation was given by the High Andes and Puna compared with the other eco-regions, probably because these areas are the least surveyed and with the lowest richness and abundance of small mammal species. From the total of 82 new flea-host associations, 81 belong to sigmodontine rodent hosts and one to a marsupial.
Improvement of low-rank representation denoising model based on nonconvex penalty function

Because nuclear norm tends to underestimate the nonzero singular values in low-rank representation model (LRR), a parameterized nonconvex penalty function is used to estimate non-zero singular value, which can recover the low-rank matrix more accurately. For image denoising, using the inherent non-local self-similarity of the image, the improved model can more effectively detect and remove noise and improve quality of the recovered image. In order to solve the proposed model, an alternative minimisation method has been adopted. Experimental results indicate that the proposed algorithm achieves a competitive denoising performance, especially for high-intensity noise, in comparison with state-of-the-art algorithms in terms of subjective and objective qualities.
Improving multiclass classification and outlier detection method through ensemble technique

Class imbalance problems in multiclass have attracted much research focus due to classification difficulty caused by imbalance class distribution, presence of outliers, and irrelevant features that degrades performance of classifiers. Most of the commonly used classification algorithms are developed for binary problem. Rare classes in the multiclass datasets may be treated as outlier classes. The study proposed development of an ensemble multiclass classification and outlier method using adaboost, random subspace algorithms with random forest as base classifiers and voting combination technique. Experimental results shows that the proposed method outperformed most of the commonly used classification algorithms such as KNN, bagging, Naive Bayes, and random forest. Using rare classes as the outlier classes, the proposed method performed better than outlier detection methods built using Naive Bayes, KNN, decision trees, and random forest algorithms. The study concludes that ensemble techniques leads to an improved multiclass classification and outlier detection method.