Miracle-dz/UniFAR-FTUs huggingface dataset:
EMOTIC: Emotions in Context Dataset

Recognizing people's emotions from their frame of reference is very important in our everyday life. This capacity helps us to perceive or predict the subsequent actions of people, interact effectively with them and to be sympathetic and sensitive toward them. Hence, one should expect that a machine needs to have a similar capability of understanding people's feelings in order to correctly interact with humans. Current research on emotion recognition has focused on the analysis of facial expressions. However, recognizing emotions requires also understanding the scene in which a person is immersed. The unavailability of suitable data to study such a problem has made research in emotion recognition in context difficult. In this paper, we present the EMOTIC database (from EMOTions In Context), a database of images with people in real environments, annotated with their apparent emotions. We defined an extended list of 26 emotion categories to annotate the images, and combined these annotations with three common continuous dimensions: Valence, Arousal, and Dominance. Images in the database are annotated using the Amazon Mechanical Turk (AMT) platform. The resulting set contains 18,313 images with 23,788 annotated people. The goal of this paper is to present the EMOTIC database, detailing how it was created and the information available. We expect this dataset can help to open up new horizons on creating systems able of recognizing rich information about people's apparent emotional states.
A Comparative Study of Homomorphic and Searchable Encryption Schemes for Cloud Computing

Cloud computing is a popular distributed network and utility model based technology. Since in cloud the data is outsourced to third parties, the protection of confidentiality and privacy of user data becomes important. Different methods for securing the data in cloud have been proposed by researchers including but not limited to Oblivious RAM, Searchable Encryption, Functional Encryption, Homomorphic Encryption etc. This paper focuses on Searchable and Homomorphic Encryption methods. Finally, a comparative study of these two efficient cloud cryptographic methods has been carried out and given here.
RouteMe: A Mobile Recommender System for Personalized, Multi-Modal Route Planning

Route planner systems support commuters and city visitors in finding the best route between two arbitrary points. More advanced route planners integrate different transportation modes such as private transport, public transport, car- and bicycle sharing or walking and are able combine these to multi-modal routes. Nevertheless, state-of-the-art planner systems usually do not consider the users' personal preferences or the wisdom of the crowd when suggesting multi-modal routes. Including the knowledge and experience of locals who are familiar with local transport allows identification of alternative routes which are, for example, less crowded during peak hours. Collaborative filtering (CF) is a technique that allows recommending items such as multi-modal routes based on the ratings of users with similar preferences. In this paper, we introduce RouteMe, a mobile recommender system for personalized, multi-modal routes which combines CF with knowledge-based recommendations to increase the quality of route recommendations. We present our hybrid algorithm in detail and show how we integrate it in a working prototype. The results of a user study show that our prototype combining CF, knowledge-based and popular route recommendations outperforms state-of-the-art route planners.
Amplified environmental change: Evidence from land‐use and climate change in medieval Minorca

The debate on human environmental impact has often been locked into cause–effect reasoning, aiming at factoring human impact on top of climatic variability. Here we use evidence from Minorca and the Mediterranean region to show the potential for amplified environmental change emerging from complex feedbacks between climatic and historical events. Alluvial sediments collected in a gully reveal a 14‐ to 27‐fold increase in sediment accumulation rates, leading to the rapid aggradation of the valley floor from approximately ad 1300 onwards. These environmental changes coincided with the Feudal conquest of Minorca (ad 1287) and with the climatic shift from the Medieval Climate Anomaly (MCA, c. ad 900–1300) to the Little Ice Age (LIA, c. ad 1300–1850). This evidence of unprecedented sediment mobilisation, in context with climatic and historical events marking the Mediterranean region, highlights the implications for environmental vulnerability emerging from positive feedbacks between climate and land‐use. Understanding such interactions in historical contexts is paramount to increase our capacity for anticipatory learning in the face of rapid climatic, economical, and ecological transformations today.
Dialects matter : the influence of dialects and code-switching on the literacy and numeracy achievements of isiXhosa Grade 1 learners in the Western Cape

Historically, African languages have been in a disadvantaged position and issues related to dialect are not fully understood and acknowledged. In this study, we examined the influence of dialects and code-switching on the literacy and numeracy achievements of isiXhosa Grade 1 learners with Multilevel Modeling (MLM), when controlling for other factors, such as between-school variance, socio-economic status, and sex. The project used stratified random sampling to select Afrikaans, English, and isiXhosa schools in three districts in the Western Cape. A total of 2 497 learners were assessed, of whom 768 were in isiXhosa schools. Dialects and codeswitching had a significantly negative influence on both literacy and numeracy scores of isiXhosa learners. The findings highlight the importance of investigating dialectic issues in isiXhosa and the implications that dialects have for decolonisation. African language decolonisation requires the development of classroom resources for these languages, increased African educational research, and finding ways to address the complexities of dialects in pedagogical frameworks.
A CLASS OF DISCONTINUOUS PETROV-GALERKIN METHODS. PART II: OPTIMAL TEST FUNCTIONS

We lay out a program for constructing discontinuous Petrov-Galerkin (DPG) schemes having test function spaces that are automatically computable to guarantee stability. Given a trial space, a DPG discretization using its optimal test space counterpart inherits stability from the well-posedness of the undiscretized problem. Although the question of stable test space choice had attracted the attention of many previous authors, the novelty in our approach lies in the fact we identify a discontinuous Galerkin (DG) framework wherein test functions, arbitrarily close to the optimal ones, can be locally computed. The idea is presented abstractly and its feasibility illustrated through several theoretical and numerical examples.
Time Interval based Modelling and Classification of Events in Soccer Video

Multimodal indexing of events in video documents poses problems with respect to representation, inclusion of contextual information, and synchronization of the heterogeneous information sources involved. In this paper we propose to model events in multimodal video by means of time interval relations, to tackle aforementioned problems. Our approach exploits the powerful properties of statistical classifiers, like the Maximum Entropy and Support Vector Machine classifiers. To demonstrate the viability of our approach for event classification in multimodal video, an evaluation was performed on the domain of soccer broadcasts. It was found that the amount of video a user has to watch in order to see almost all highlights can be reduced considerably. Furthermore, we found that a Support Vector Machine performs better than a Maximum Entropy classifier.
Opinion Mining of Real Time Twitter Tweets

is a real-time information network and micro- blogging service that allows users to post updates. The service rapidly gained worldwide popularity that connects to the latest stories, ideas, opinions, and news. It is a powerful tool for real-time way of communicating with people by combining messages that are quick to write, easy to read, public and accessible anywhere. On Twitter anyone can read, write and share messages or tweets. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and examine opinions about the product made in blog posts, comments, reviews or tweets. Social media plays an important role in inferring the opinion of the authors. In this paper we focused on tweets that will result in analyzing the view of the public on generally discussed topics. A tweets puller is developed that automatically collects random opinions and classifier tool that performs classifications on that corpus collected from Twitter. Our classification is based on features extracted and classified into POSITIVE, NEGATIVE and NEUTRAL. The results further evaluated and concluded to infer the performance of the classification through SVM.
Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles

In this paper, we study the impact of relational and syntactic representations for an interesting and challenging task: the automatic resolution of crossword puzzles. Automatic solvers are typically based on two answer retrieval modules: (i) a web search engine, e.g., Google, Bing, etc. and (ii) a database (DB) system for accessing previously resolved crossword puzzles. We show that learning to rank models based on relational syntactic structures defined between the clues and the answer can improve both modules above. In particular, our approach accesses the DB using a search engine and reranks its output by modeling paraphrasing. This improves on the MRR of previous system up to 53% in ranking answer candidates and greatly impacts on the resolution accuracy of crossword puzzles up to 15%.
Transcriptome Analysis of Flowering Time Genes under Drought Stress in Maize Leaves

Flowering time is an important factor determining yield and seed quality in maize. A change in flowering time is a strategy used to survive abiotic stresses. Among abiotic stresses, drought can increase anthesis-silking intervals (ASI), resulting in negative effects on maize yield. We have analyzed the correlation between flowering time and drought stress using RNA-seq and bioinformatics tools. Our results identified a total of 619 genes and 126 transcripts whose expression was altered by drought stress in the maize B73 leaves under short-day condition. Among drought responsive genes, we also identified 20 genes involved in flowering times. Gene Ontology (GO) enrichment analysis was used to predict the functions of the drought-responsive genes and transcripts. GO categories related to flowering time included reproduction, flower development, pollen–pistil interaction, and post-embryonic development. Transcript levels of several genes that have previously been shown to affect flowering time, such as PRR37, transcription factor HY5, and CONSTANS, were significantly altered by drought conditions. Furthermore, we also identified several drought-responsive transcripts containing C2H2 zinc finger, CCCH, and NAC domains, which are frequently involved in transcriptional regulation and may thus have potential to alter gene expression programs to change maize flowering time. Overall, our results provide a genome-wide analysis of differentially expressed genes (DEGs), novel transcripts, and isoform variants expressed during the reproductive stage of maize plants subjected to drought stress and short-day condition. Further characterization of the drought-responsive transcripts identified in this study has the potential to advance our understanding of the mechanisms that regulate flowering time under drought stress.
Sensitivity to stress-induced reproductive dysfunction linked to activity of the serotonin system.

OBJECTIVE To use a nonhuman primate model and determine whether individuals sensitive to stress-induced reproductive dysfunction have lower activity of central serotonergic neurons under nonstressed conditions. DESIGN The activity of the central serotonergic system was assessed by measuring responsiveness to a fenfluramine challenge (5 mg/kg, IV) in sedated monkeys previously categorized as highly stress resistant (HSR; n = 4; normal menstrual cyclicity through two stress cycles), medium stress resistant (MSR; n = 5; ovulatory in the first stress cycle but anovulatory in the second stress cycle), or low stress resistant (i.e., stress sensitive, SS; n = 4; anovulatory as soon as stress is initiated). To control for differences in pituitary stores of prolactin or ACTH, the animals were subsequently administered a bolus of thyrotropin-releasing hormone (3 microg/kg) plus corticotropin releasing factor (CRF), (3 microg/kg). SETTING Oregon National Primate Research Center, Animal Services Building. PATIENT(S) Female cynomolgus macaques exhibiting normal menstrual cycles. INTERVENTION(S) Administration of fenfluramine, a serotonin-releasing drug. MAIN OUTCOMES MEASURE(S) Serum concentrations of prolactin (PRL) and cortisol (F). RESULT(S) Prolactin release in response to fenfluramine was significantly greater in the HSR group compared with the MSR or SS groups. In contrast, cortisol was higher in the SS group compared with the other two groups. Similar responses were not evident after thyrotropin-releasing hormone + CRF stimulation. CONCLUSION(S) The lower PRL response to fenfluramine in the stress-sensitive animals suggests that stress-sensitive individuals have decreased activity in central serotonergic neurons. However, the F data suggest that the hypothalamic-pituitary-adrenal axis in stress-sensitive individuals is highly responsive to even small increases in serotonin.
Structural Characterization of the Extracellular Domain of CASPR2 and Insights into Its Association with the Novel Ligand Contactin1*

Contactin-associated protein-like 2 (CNTNAP2) encodes for CASPR2, a multidomain single transmembrane protein belonging to the neurexin superfamily that has been implicated in a broad range of human phenotypes including autism and language impairment. Using a combination of biophysical techniques, including small angle x-ray scattering, single particle electron microscopy, analytical ultracentrifugation, and bio-layer interferometry, we present novel structural and functional data that relate the architecture of the extracellular domain of CASPR2 to a previously unknown ligand, Contactin1 (CNTN1). Structurally, CASPR2 is highly glycosylated and has an overall compact architecture. Functionally, we show that CASPR2 associates with micromolar affinity with CNTN1 but, under the same conditions, it does not interact with any of the other members of the contactin family. Moreover, by using dissociated hippocampal neurons we show that microbeads loaded with CASPR2, but not with a deletion mutant, co-localize with transfected CNTN1, suggesting that CNTN1 is an endogenous ligand for CASPR2. These data provide novel insights into the structure and function of CASPR2, suggesting a complex role of CASPR2 in the nervous system.
Detecting Weakly Simple Polygons

A closed curve in the plane is weakly simple if it is the limit (in the Frechet metric) of a sequence of simple closed curves. We describe an algorithm to determine whether a closed walk of length n in a simple plane graph is weakly simple in O(n log n) time, improving an earlier O(n3)-time algorithm of Cortese et al. [Discrete Math. 2009]. As an immediate corollary, we obtain the first efficient algorithm to determine whether an arbitrary n-vertex polygon is weakly simple; our algorithm runs in O(n2 log n) time. We also describe algorithms that detect weak simplicity in O(n log n) time for two interesting classes of polygons. Finally, we discuss subtle errors in several previously published definitions of weak simplicity.
A novel approach for fast mining frequent itemsets use N-list structure based on MapReduce

Frequent Pattern Mining is a one field of the most significant topics in data mining. In recent years, many algorithms have been proposed for mining frequent itemsets. A new algorithm has been presented for mining frequent itemsets based on N-list data structure called Prepost algorithm. The Prepost algorithm is enhanced by implementing compact PPC-tree with the general tree. Prepost algorithm can only find a frequent itemsets with required (pre-order and post-order) for each node. In this chapter, we improved prepost algorithm based on Hadoop platform (HPrepost), proposed using the Mapreduce programming model. The main goals of proposed method are efficient mining frequent itemsets requiring less running time and memory usage. We have conduct experiments for the proposed scheme to compare with another algorithms. With dense datasets, which have a large average length of transactions, HPrepost is more effective than frequent itemsets algorithms in terms of execution time and memory usage for all min-sup. Generally, our algorithm outperforms algorithms in terms of runtime and memory usage with small thresholds and large datasets.
Whole-grain and fiber intakes and periodontitis risk in men.

BACKGROUND Diabetes and hyperglycemia increase periodontitis risk, severity, and extent. Increased whole-grain and fiber intakes are associated with improved insulin sensitivity and may therefore affect periodontitis risk. OBJECTIVE The objective was to examine the associations between whole-grain and fiber intakes and periodontitis risk. DESIGN We prospectively followed 34,160 male US health professionals aged 40-75 y at the outset. We updated medical and lifestyle information biennially with questionnaires and diet every 4 y by using a validated food-frequency questionnaire. We excluded men reporting periodontitis, myocardial infarction, stroke, and hypercholesterolemia before 1986 and those with incomplete dietary data. All diabetics were excluded. Periodontitis was determined by a report of professionally diagnosed disease and validated by a diagnosis of periodontitis by a periodontist from a blinded review of radiographs. RESULTS Men in the highest quintile of whole-grain intake were 23% less likely to get periodontitis than were those in the lowest quintile (multivariate RR: 0.77; 95% CI: 0.66, 0.89; P for trend < 0.001) after adjustment for age, smoking, body mass index, alcohol intake, physical activity, and total energy intake. Periodontitis was not associated with refined-grain intake (multivariate RR comparing extreme quintiles of intake: 1.04; 95% CI: 0.89, 1.23; P for trend = 0.37). Cereal fiber was inversely related to periodontitis risk (multivariate RR comparing extreme quintiles of intake: 0.85; 95% CI: 0.73, 0.99; P for trend = 0.03), but the association was not significant after adjustment for whole-grain intake. CONCLUSION Increasing whole grain in the diet without increasing total energy intake may reduce periodontitis risk.
Prevalence of arterial hypertension in obese high school population in Zenica, Bosnia and Herzegovina.

AIM To determine the significance of obesity as a risk factor in high school population with hypertension. METHODS Testing was conducted during the school year 2006-2007 on a sample of 1121 students, 15-18 years of age (306 boys and 815 girls) from three high schools in Zenica (Grammar School, Teacher Training High School and Secondary School for Nurses). Increased relative risk factor with reliability coefficient that points to the significance of the correlation of risk factors and development of a disease was examined using analytical epidemiological research methods and statistical analysis (the linear correlation coefficient, χ2 test, and the relative risk ) . RESULTS Obesity, e.g. Body Mass Index (BMI) more than 95 was found in 15 (5%) boys and 57 (7%) girls. Increased relative risk of developing essential arterial hypertension was found in obese boys (RR= 9.00 ) and girls (RR= 2.66) with the coefficient of reliability which pointed to the significance of the relationship between obesity and arterial hypertension morbidity. CONCLUSION Obesity is a significant risk factor for the occurrence of essential arterial hypertension in boys and girls. Monitoring the nutritional status of youth, regular systematic medical examinations with measuring tension and implementing measures to control nutrition, are important preventive public health tasks.
Unpacking Virtualization Obfuscators

Nearly every malware sample is sheathed in an executable protection which must be removed before static analyses can proceed. Existing research has studied automatically unpacking certain protections, but has not yet caught up with many modern techniques. Contrary to prior assumptions, protected programs do not always have the property that they are reverted to a fully unprotected state at some point during the course of their execution. This work provides a novel technique for circumventing one of the most problematic features of modern software protections, so-called virtualization obfuscation. The technique enables analysis of heretofore impenetrable malware.
Towards individual aerosol Particle identification using advanced laser spectroscopic techniques

This thesis presents the development and use of an optical device designed for aerosol analysis in different environments. We expose and analyze the results of an organized expedition to better understand the role of marine aerosols on the global climate. This device also enabled us to carry out a campaign of real-time measurements of individual particles from urban aerosols. By integrating advanced techniques of fluorescence depletion spectroscopy (pump-probe) into a portable device, we were able to distinguish, with high selectivity, the aerosols containing amino acids compared to those containing polycyclic hydrocarbons. Finally, we tested this device under real conditions of use. In addition, a comparative test using particle impaction on a filter was performed to confirm the existence of biological material by traditional biochemical analysis. This first test in real conditions of use has allowed us to demonstrate the benefits and limitations of this type of device and builds a foundation for future applications.
User interfaces and data entry with real time inverse arithmetic coding

This paper introduces real time inverse arithmetic coding and user interfaces based thereupon. The main idea is that information-efficient data entry can be achieved by ensuring that each input's associated display space and ease of selection are at all times related to the input's probability of being selected. As with data entry based on inverse arithmetic coding, the layout initially depends on the probabilities of the possible inputs; however, real time inverse arithmetic coding differs in that the user's actions are interpreted not to navigate this probability distribution but rather to modify it according to a learned update rule, which approximates the conditioning of the probability distribution on the user's actions. Potential applications of real time inverse arithmetic coding include text entry, file browsing, integrated multi-program user interfaces, assistive technologies for users with movement disabilities, and ergonomic input methods.
Analysis of the airflow in the TA-2 subsonic wind tunnel

Tests are being carried out at the TA-2 wind tunnel to obtain the velocity and pressure distributions at the test section. The airflow conditions must be known to fulfil the required accuracy of tests. A rake composed of Pitot tubes was used to measure local static and total pressures of the flow. The test section region considered here is related to the transverse central area. The estimated parameters are velocity and pressure coefficient. Uncertainties associated with these parameters are evaluated according to metrological standardisation. Calibration curves are supplied using the least squares fitting. Results revealed a gradient in airflow parameters.