Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Music Tagging With Time Series Models

State-of-the-art systems for automatic music tagging model music based on bag-of-feature representations which give little or no account of temporal dynamics, a key characteristic of the audio signal. We describe a novel approach to automatic music annotation and retrieval that captures temporal (e.g., rhythmical) aspects as well as tim-bral content. The proposed approach leverages a recently proposed song model that is based on a generative time series model of the musical content — the dynamic texture mixture (DTM) model — that treats fragments of audio as the output of a linear dynamical system. To model characteristic temporal dynamics and timbral content at the tag level, a novel, efﬁcient hierarchical EM algorithm for DTM (HEM-DTM) is used to summarize the common information shared by DTMs modeling individual songs associated with a tag. Experiments show learning the semantics of music beneﬁts from modeling temporal dynamics.
UAV perimeter patrol operations optimization using efficient Dynamic Programming

A reduced order Dynamic Programming (DP) method that efficiently computes the optimal policy and value function for a class of controlled Markov chains is developed. We assume that the Markov chains exhibit the property that a subset of the states have a single (default) control action asso-ciated with them. Furthermore, we assume that the transition probabilities between the remaining (decision) states can be derived from the original Markov chain specification. Under these assumptions, the suggested reduced order DP method yields significant savings in computation time and also leads to faster convergence to the optimal solution. Most importantly, the reduced order DP has been shown analytically to give the exact same solution that one would obtain via performing DP on the original full state space Markov chain. The method is illustrated via a multi UAV perimeter patrol stochastic optimal control problem.
Quantitative Detection of Corynebacterium casei in Cheese by Real-Time PCR

ABSTRACT The flora on the surface of smear-ripened cheeses is composed of numerous species of bacteria and yeasts that contribute to the production of the desired organoleptic properties. Due to the absence of selective media, it is very difficult to quantify cheese surface bacteria, and, consequently, the ecology of the cheese surface microflora has not been extensively investigated. We developed a SYBR green I real-time PCR method to quantify Corynebacterium casei, a major species of smear-ripened cheeses, using primers designed to target the 16S rRNA gene. It was possible to recover C. casei genomic DNA from the cheese matrix with nearly the same yield that C. casei genomic DNA is recovered from cells recovered by centrifugation from liquid cultures. Quantification was linear over a range from 105 to 1010 CFU per g of cheese. The specificity of the assay was demonstrated with DNA from species related to C. casei and from other bacteria and yeasts belonging to the cheese flora. Nine commercial cheeses were analyzed by real-time PCR, and six of them were found to contain more than 105 CFU equivalents of C. casei per g. In two of them, the proportion of C. casei in the total bacterial flora was nearly 40%. The presence of C. casei in these samples was further confirmed by single-strand conformation polymorphism analysis and by a combined approach consisting of plate counting and 16S rRNA gene sequencing. We concluded that SYBR green I real-time PCR may be used as a reliable species-specific method for quantification of bacteria from the surface of cheeses.
Suicide Note Sentiment Classification: A Supervised Approach Augmented by Web Data

Objective To create a sentiment classification system for the Fifth i2b2/VA Challenge Track 2, which can identify thirteen subjective categories and two objective categories. Design We developed a hybrid system using Support Vector Machine (SVM) classifiers with augmented training data from the Internet. Our system consists of three types of classification-based systems: the first system uses spanning n-gram features for subjective categories, the second one uses bag-of-n-gram features for objective categories, and the third one uses pattern matching for infrequent or subtle emotion categories. The spanning n-gram features are selected by a feature selection algorithm that leverages emotional corpus from weblogs. Special normalization of objective sentences is generalized with shallow parsing and external web knowledge. We utilize three sources of web data: the weblog of LiveJournal which helps to improve the feature selection, the eBay List which assists in special normalization of information and instructions categories, and the suicide project web which provides unlabeled data with similar properties as suicide notes. Measurements The performance is evaluated by the overall micro-averaged precision, recall and F-measure. Result Our system achieved an overall micro-averaged F-measure of 0.59. Happiness_peacefulness had the highest F-measure of 0.81. We were ranked as the second best out of 26 competing teams. Conclusion Our results indicated that classifying fine-grained sentiments at sentence level is a non-trivial task. It is effective to divide categories into different groups according to their semantic properties. In addition, our system performance benefits from external knowledge extracted from publically available web data of other purposes; performance can be further enhanced when more training data is available.
Evaluating Narrative Family Therapy Using Single-System Research Designs

This study assessed the effectiveness of Narrative Therapy in reducing parent/child conflicts. Parents measured their child's progress by counting the frequency of specific behaviors during baseline and intervention phases. The practitioner-researcher used single-case methodology with a treatment package strategy, and the results were evaluated using three multiple baseline designs. Six families were treated using several Narrative Therapy techniques including: externalization, relative influence questioning, identifying unique outcomes and unique ac counts, bringing forth unique redescriptions, facilitating unique circulation, and assigning between-session tasks. Compared to baseline rates, five of six families showed improvements in parent/child conflict, rangingfrom an 88% to a 98% decrease in conflict. Improvements occurred only when Narrative Therapy was applied and were not observed in its absence.
Environmental surveillance of poliovirus and non-polio enterovirus in urban sewage in Dakar, Senegal (2007-2013)

Introduction Global poliomyelitis eradication initiative relies on (i) laboratory based surveillance of acute flaccid surveillance (AFP) to monitor the circulation of wild poliovirus in a population, and (ii) vaccination to prevent its diffusion. However, as poliovirus can survive in the environment namely in sewage, environmental surveillance (ES) is of growing importance as the eradication target is close. This study aimed to assess polioviruses and non polio enteroviruses circulation in sewage drains covering a significant population of Dakar. Methods From April 2007 to May 2013, 271 specimens of raw sewage were collected using the grab method in 6 neighborhoods of Dakar. Samples were processed to extract and concentrate viruses using polyethylene glycol and Dextran (two-phase separation method). Isolation of enteroviruses was attempted in RD, L20B and Hep2 cell lines. Polioviruses were identified by RT-PCR and Elisa. Non Polio Enteroviruses (NPEVs) were identified by RT-PCR and microneutralisation tests. Results Polioviruses and NPEVs were respectively detected in 34,3% and 42,8% sewage samples. No wild poliovirus neither circulating vaccine-derived Poliovirus (cVDPV) was detected. Neutralization assays have identified 49 non polio enteroviruses that were subsequently classified in 13 serotypes belonging to HEV-A (22, 4%), HEV-B (12, 24%), HEV-C (26, 53%) and HEV-D (6, 12%) species. Conclusion This study is the first documentation of enteroviruses environmental detection in Senegal. It shows the usefulness of environmental surveillance for indirect monitoring of the circulation and distribution of enteroviruses in the community.
Model Checking with Program Slicing Based on Variable Dependence Graphs

In embedded control systems, the potential risks of software defects have been increasing because of software complexity which leads to, for example, timing related problems. These defects are rarely found by tests or simulations. To detect such defects, we propose a modeling method which can generate software models for model checking with a program slicing technique based on a variable dependence graph. We have applied the proposed method to one case in automotive control software and demonstrated the effectiveness of the method. Furthermore, we developed a software tool to automate model generation and achieved a 35% decrease in total verification time on model checking.
Immunomagnetic detection of Bacillus stearothermophilus spores in food and environmental samples

There are currently no methods for the rapid and sensitive detection of bacterial spores that could be used to direct raw materials containing high spore loads away from products that pose a food safety risk. Existing methods require an overnight incubation, cannot detect spores below 10(5) CFU/ml, or are not specific to particular species. This work describes a method to specifically detect < 10(4) CFU of bacterial spores per ml within 2 h. Polyclonal antibodies to Bacillus stearothermophilus spores were attached to 2.8-micron-diameter magnetic polystyrene beads by using a polythreonine cross-linker via the antibody carbohydrate moiety. A biotin-avidin-amplified sandwich enzyme-linked immunosorbent assay coupled to a fluorescent substrate was used to quantitate captured spores. The concentration of B. stearothermophilus spores in samples was linearly correlated to fluorescent activity (r2 = 0.99) with a lower detection limit of 8 x 10(3) CFU/ml and an upper detection limit of 8 x 10(5) CFU/ml. The detection limits are not fixed and can be changed by varying the immunomagnetic bead concentration. Several food and environmental samples were tested to demonstrate the versatility of the assay.
It’s all about Trust and Loyalty: Partner Selection Mechanisms in Tourism Networks

Does expected partner or task related criteria affect how trust and loyalty is developed between tourism firms? This paper poses a model, which is developed on the assumption that well defined partner and task related partner expectations and experienced trust, should build loyalty between tourism firms. The model provides a more precise assessment for scholars, which believes that trust is related to the risk that is associated with personal relationships between two or more firms, and that risk depends on expectations that are derived from personal relationships. The model also integrates the role of experienced trust in transforming different partner selection criteria into loyalty. The contribution of the model is that it provides insights into cooperative relationships by examining the role of trust between small tourism firms when cooperation is established. Data from a sample of 96 tourism firms in the Northern U.S. is used to test the model. The findings provide implications for tourism scholars and policy-makers who are interested in developing managerial strategies that are based on personal relationships.
Process-Based Measures of Creative Problem-Solving Skills: III. Category Selection

Because people must organize information and combine or reorganize categories to produce creative solutions, there is reason to suspect that concepts play an important role in creative thought. Unfortunately, little is known about the kind of concepts that contribute to the success of people' s creative problem-solving efforts. Accordingly, 135 college students were presented with a series of novel, ill-defined problems in which they were asked to select a set of concepts they thought would help them solve these problems. These indices of concept preferences were then correlated with the quality and originality of the solutions obtained on a set of two creative problem-solving tasks. The use of concepts organized around long-term goals was positively related to indices of solution quality and originality. The implications of these findings for understanding the kind of concepts that contribute to creative thought are discussed.
Classification in a Skewed Online Trade Fraud Complaint Corpus

This paper explores how machine learning techniques can be used to support handling of skewed online trade fraud complaints, by predicting whether a complaint will be withdrawn or not. To optimize the performance of each classifier, the influence of resampling, word weighting, and word normalization on the classification performance is assessed. It is found that machine learning can indeed be used for this purpose, by improving the baseline performance in comparison to the skewness ratio up to 13 pp using Logistic Regression. Furthermore, the results show that data alteration techniques can improve classifier performance on a skewed dataset up to 13.5 pp.
Event-triggered strategies for industrial control over wireless networks

New event-based sampling strategies can support the efficient use of radio resources in wireless control systems. Motivated by the recent introduction of wireless network nodes in process control industry, we consider the particular demands these closed-loop systems set on the wireless communication and the influence the communication has on the control performance. In the paper, it is pointed out that by letting sensor nodes transmit only when needed, it is possible to minimize the communication bandwidth utilization in these systems. We show how classical control strategies commonly based on periodic sampling, such as proportional-integral-derivative control and minimum variance control, can be cast in an event-based setting in which decentralized communication decisions are taken suitable for commonly used contention-based medium access control protocols. Event-triggered sampling for estimation is also reviewed. Simulated examples illustrate the results.
Suppressive effects of neutrophil by Salp16‐like salivary gland proteins from Ixodes persulcatus Schulze tick

Salp16, a 16‐kDa tick salivary gland protein, is known to be the molecule involved in the transmission of Anaplasma phagocytophilum, an obligate intracellular pathogen causing zoonotic anaplasmosis, from its mammalian hosts to Ixodes scapularis. Recently, the presence of A. phagocytophilum was documented in Japan and Ixodes persulcatus was identified as one of its vectors. The purpose of this study was to identify Salp16 genes in I. persulcatus and characterize their function. Two cDNA clones encoding the Salp16‐like sequences were obtained from the salivary glands of fed female I. persulcatus ticks and designated Salp16 Iper1 and Iper2. Gene expression analyses showed that the Salp16 Iper genes were expressed specifically in the salivary glands and were up‐regulated by blood feeding. These proteins attenuated the oxidative burst of activated bovine neutrophils and inhibited their migration induced by the chemoattractant interleukin‐8 (IL‐8). These results demonstrate that Salp16 Iper proteins contribute to the establishment of blood feeding as an immunosuppressant of neutrophil, an essential factor in innate host immunity. Further examination of the role of Salp16 Iper in the transmission of pathogens, including A. phagocytophilum, will increase our understanding of the tick–host–pathogen interface.
Design and testing of biological scaffolds for delivering reparative cells to target sites in the lung

This study summarizes the development and testing of a scaffold to promote engraftment of cells in the distal lung. A fibrinogen–fibronectin–vitronectin hydrogel (FFVH) was developed and optimized with respect to its mechanical and biological properties for this application. In vitro, FFVH scaffolds promoted attachment, histiotypic growth and expression of basement membrane proteins by primary ovine lung mesenchymal cells derived from lung biopsies. In vivo testing was then performed to assess the ability of FFVHs to promote cell engraftment in the sheep lung. Treatment with autologous cells delivered using FFVH was clinically well tolerated. Cells labelled with a fluorescent dye (PKH‐26) were detected at treatment sites after 1 month. Tissue mass (assessed by CT imaging) and lung perfusion (assessed by nuclear scintigraphy) were increased at emphysema test sites. Post‐treatment histology demonstrated cell proliferation and increased elastin expression without scarring or collapse. No treatment‐related pathology was observed at healthy control sites. FFVH scaffolds promote cell attachment, spreading and extracellular matrix expression in vitro and apparent engraftment in vivo, with evidence of trophic effects on the surrounding tissue. Scaffolds of this type may contribute to the development of cell‐based therapies for patients with end‐stage pulmonary diseases. Copyright © 2009 John Wiley & Sons, Ltd.
Candidate gene association study for diabetic retinopathy in Chinese patients with type 2 diabetes

Purpose To investigate whether variants in a set of eight candidate genes are associated with diabetic retinopathy (DR) in a cohort of Chinese patients with type 2 diabetes mellitus (T2DM). Methods Case-control study. Patients with T2DM were recruited from the Desheng community in urban Beijing and assigned into a DR group or diabetic without retinopathy (DWR) group, based on the duration of diabetes and grading of fundus images. Twenty-six single-nucleotide polymorphisms (SNPs) within eight candidate genes, including PPARγ, vascular endothelial growth factor (VEGF) and its receptor kinase insert domain receptor (KDR), erythropoietin, aldose reductase, protein kinase C-β, angiotensin-converting enzyme, and intercellular adhesion molecule 1, were analyzed using the MassARRAY genotyping system. Results A total of 500 patients with T2DM (216 with DR and 284 with DWR) were enrolled in the study. Significant associations of DR were noted with genotypes of four SNPs—rs699947 (p<0.001), rs833061 (p=0.001), rs13207351 (p<0.001), and rs2146323 (p=0.006)—in the VEGF gene and one variant, rs2071559, in the KDR gene (p=0.034). After adjustment for covariates, significant association of DR remained with the homozygous genotype of the minor allele for the SNPs rs699947 (odds ratio [OR] = 3.54, 95% confidence interval [CI]: 1.12–11.19), rs833061 (OR = 3.72, 95% CI: 1.17–11.85), rs13207351 (OR = 3.76, 95% CI: 1.21–11.71), and rs2146323 (OR = 2.8, 95% CI: 1.46–5.37) in the VEGF gene as well as the SNP rs2071559 (OR = 1.62, 95% CI: 1.08–2.41) in the KDR gene. However, only rs699947 and rs13207351 in the VEGF gene remained statistically significant after Bonferroni correction. No associations were found in other genes tested. Conclusions These data expanded previous observations on the association of DR with variants in the VEGF gene in Chinese patients with T2DM. Moreover, a possible association between DR and KDR polymorphisms is suggested.
High Prevalence of Enterobius vermicularis Infection among Schoolchildren in Three Townships around Yangon, Myanmar

In order to determine the status of Enterobius vermicularis infection among schoolchildren in suburban areas of Myanmar, 761 primary schoolchildren in 3 different townships around Yangon City were subjected to a survey using cello-tape anal swabs. The subjected schoolchildren were 383 boys and 378 girls who were 5-7 years of age. Only 1 anal swab was obtained from each child. The overall egg positive rate of E. vermicularis was 47.2% (359 positives), and sex difference was not remarkable (48.6% in boys and 45.8% in girls). However, the positive rate was the highest in South Dagon (54.6%) followed by Hlaing Thayar (43.8%) and North Dagon (34.8%). This difference was highly correlated with the living standards of the people in each township. Nucleotide sequence of the 5S rDNA from the eggs on the cello-tape (2 children) revealed 99.7% identity with that of E. vermicularis reported in GenBank. The results indicated that E. vermicularis infection is highly prevalent among primary schoolchildren around Yangon, Myanmar.
ROAM: Rule- and Motif-Based Anomaly Detection in Massive Moving Object Data Sets

With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is automated identification of suspicious movements. Due to the sheer volume of data associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features. In this study, we propose a new framework named ROAM (Ruleand Motif-based Anomaly Detection in Moving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments calledmotifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rulebased classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. We implemented ROAM and tested its components under a variety of conditions. Our experiments show that the system is efficient and effective at detecting abnormal moving objects.
Quicksand : A Lightweight Implementation of Probabilistic Programming for Procedural Modeling and Design

The past several years have seen the development of multiple probabilistic programming languages (PPLs) in the artificial intelligence community [9, 5, 12, 13, 7, 11]. In addition to their expressiveness, PPLs allow programmers to develop models modularly and independently from inference algorithms. Over the same period, computer graphics research has begun to demonstrate how probabilistic inference can enable complex procedural content creation [6, 14, 18]. But this work has relied on specialized models and inference algorithms, making the methods difficult for technical artists—who possess programming skills but not necessarily inference expertise—to adopt. Using probabilistic programming for procedural content creation has the potential to close this adoption gap.
Thresholding Bandits with Augmented UCB

In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold. A key feature of AugUCB is that it uses both mean and variance estimates to eliminate arms that have been sufficiently explored; to the best of our knowledge this is the first algorithm to employ such an approach for the considered TBP. Theoretically, we obtain an upper bound on the loss (probability of mis-classification) incurred by AugUCB. Although UCBEV in literature provides a better guarantee, it is important to emphasize that UCBEV has access to problem complexity (whose computation requires arms' mean and variances), and hence is not realistic in practice; this is in contrast to AugUCB whose implementation does not require any such complexity inputs. We conduct extensive simulation experiments to validate the performance of AugUCB. Through our simulation work, we establish that AugUCB, owing to its utilization of variance estimates, performs significantly better than the state-of-the-art APT, CSAR and other non variance-based algorithms.