Miracle-dz/UniFAR-FTUs huggingface dataset:
Test Case Prioritization Using Relevant Slices

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible. The sizes of test suites grow as software evolves. Due to resource constraints, it is important to prioritize the execution of test cases so as to increase chances of early detection of faults. Prior techniques for test case prioritization are based on the total number of coverage requirements exercised by the test cases. In this paper, we present a new approach to prioritize test cases based on the coverage requirements present in the relevant slices of the outputs of test cases. We present experimental results comparing the effectiveness of our prioritization approach with that of existing techniques that only account for total requirement coverage, in terms of ability to achieve high rate of fault detection. Our results present interesting insights into the effectiveness of using relevant slices for test case prioritization
Novel Perspectives in the Detection of Oral and Nasal Oxidative Stress and Inflammation in Pediatric United Airway Diseases

United airway disease (UAD) concept proposed that asthma and rhinitis are both different clinical manifestation of a single inflammatory process. The aim of this study is to assess in upper and lower airways the level of inflammation and oxidative stress and to investigate the relationship between biomarkers in persistent allergic rhinitis (PER) and in concomitant asthma with PER. By a cross-sectional study we measured oral and nasal (FENO) and oral and nasal EBC 8-isoprostane, LTB4 and PGE in children with PER (n=14) and with PER and concomitant intermittent asthma (IA; n=25), mild persistent asthma (mA; n=28), moderate persistent asthma (MA; n=13) and in Healthy Controls (HCs; n=13). Oral and nasal FENO concentrations were increased in children with PER, IA, mA and MA when compared with HCs. Nasal 8-isoprostane was higher in EBC of children with PER and asthma than in HCs. Oral and nasal LTB4 were higher in EBC of children with PER and mA than in HCs. Oral and nasal PGE2 concentrations were higher in EBC of children with PER than in HCs. Positive correlations between oral and nasal biomarkers were found in IA for LTB4 and PGE2, in mA for FENO, 8-isoprostane, LTB and PGE2, and in MA for PGE2. No correlations were observed in children with PER and HCs. Our results suggest that non-invasive markers of inflammation and oxidative stress might be useful to study the relationships between oral and nasal compartments in allergic children with PER and concomitant asthma with the aim of defining the UAD.
Real-Time Power Balancing Via Decentralized Coordinated Home Energy Scheduling

It is anticipated that an uncoordinated operation of individual home energy management (HEM) systems in a neighborhood would have a rebound effect on the aggregate demand profile. To address this issue, this paper proposes a coordinated home energy management (CoHEM) architecture in which distributed HEM units collaborate with each other in order to keep the demand and supply balanced in their neighborhood. Assuming the energy requests by customers are random in time, we formulate the proposed CoHEM design as a multi-stage stochastic optimization problem. We propose novel models to describe the deferrable appliance load [e.g., plug-in (hybrid) electric vehicles (PHEV)], and apply approximation and decomposition techniques to handle the considered design problem in a decentralized fashion. The developed decentralized CoHEM algorithm allow the customers to locally compute their scheduling solutions using domestic user information and with message exchange between their neighbors only. Extensive simulation results demonstrate that the proposed CoHEM architecture can effectively improve real-time power balancing. Extensions to joint power procurement and real-time CoHEM scheduling are also presented.
An Initial Investigation into Querying an Untrustworthy and Inconsistent Web

The Semantic Web is bound to be untrustworthy and inconsistent. In this paper, we present an initial approach for obtaining useful information in such an environment. In particular, we replace the question of whether an assertion is entailed by the entire Semantic Web with two other queries. The first asks if a statement is entailed if a set of documents is trusted. The second asks for the document sets that entail a specific statement. We propose a mechanism which leverages research on assumption-based truth maintenance systems to efficiently compute and represent the contexts of the statements and manage inconsistency. For these queries, our approach provides significant improvement over the naive solution to the problem.
Extracting Melodic Pattern of 'Mohan Veena' from Polyphonic Audio Signal of North Indian Classical Music

There exist number of musical instruments in the world; each has its own melody and recognition techniques. The purpose of this paper is to propose a hypothesis for extraction of melodic pattern from the polyphonic audio recording of North Indian Classical Music files related to Mohan Veena. The paper discusses about the useful algorithm, application and techniques for extraction and evaluation. The five stages procedure: first, stream separation by Non-negative Matrix Factorization (NMF). Second, instrument recognition – extracting stream related to Mohan Veena using spectrogram and autocorrelation technique. Third, extracting methods which comprises of Sinusoid extraction, system analyzes the audio signal and extract spectral peaks for constructing the salience function where the spectral peaks could be used to compute representation of pitch salience over time, followed by pitch counter technique. Fourth, melody extraction from the extracted pitches counter and finally about the evaluation technique with some discussion.
Hierarchical Dynamic Thermal Management Method for High-Performance Many-Core Microprocessors

It is challenging to manage the thermal behavior of many-core microprocessors while still keeping them running at high performance since the control complexity increases as the core number increases. In this article, a novel hierarchical dynamic thermal management method is proposed to overcome this challenge. The new method employs model predictive control (MPC) with task migration and a DVFS scheme to ensure smooth control behavior and negligible computing performance sacrifice. In order to be scalable to many-core systems, the hierarchical control scheme is designed with two levels. At the lower level, the cores are spatially clustered into blocks, and local task migration is used to match current power distribution with the optimal distribution calculated by MPC. At the upper level, global task migration is used with the unmatched powers from the lower level. A modified iterative minimum cut algorithm is used to assist the task migration decision making if the power number is large at the upper level. Finally, DVFS is applied to regulate the remaining unmatched powers. Experiments show that the new method outperforms existing methods and is very scalable to manage many-core microprocessors with small performance degradation.
Electromagnetic interference from radio frequency identification inducing potentially hazardous incidents in critical care medical equipment.

CONTEXT Health care applications of autoidentification technologies, such as radio frequency identification (RFID), have been proposed to improve patient safety and also the tracking and tracing of medical equipment. However, electromagnetic interference (EMI) by RFID on medical devices has never been reported. OBJECTIVE To assess and classify incidents of EMI by RFID on critical care equipment. DESIGN AND SETTING Without a patient being connected, EMI by 2 RFID systems (active 125 kHz and passive 868 MHz) was assessed under controlled conditions during May 2006, in the proximity of 41 medical devices (in 17 categories, 22 different manufacturers) at the Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands. Assessment took place according to an international test protocol. Incidents of EMI were classified according to a critical care adverse events scale as hazardous, significant, or light. RESULTS In 123 EMI tests (3 per medical device), RFID induced 34 EMI incidents: 22 were classified as hazardous, 2 as significant, and 10 as light. The passive 868-MHz RFID signal induced a higher number of incidents (26 incidents in 41 EMI tests; 63%) compared with the active 125-kHz RFID signal (8 incidents in 41 EMI tests; 20%); difference 44% (95% confidence interval, 27%-53%; P < .001). The passive 868-MHz RFID signal induced EMI in 26 medical devices, including 8 that were also affected by the active 125-kHz RFID signal (26 in 41 devices; 63%). The median distance between the RFID reader and the medical device in all EMI incidents was 30 cm (range, 0.1-600 cm). CONCLUSIONS In a controlled nonclinical setting, RFID induced potentially hazardous incidents in medical devices. Implementation of RFID in the critical care environment should require on-site EMI tests and updates of international standards.
Data Mining and Predictive Analytics-Assignment 1 Image Popularity Prediction on Social Networks

Posts on social networks usually have various contents, including image, text, and metadata (such as user information). An example post from Chiclopia website is shown in Figure 1. Using these information, our goal is to predict an image’s popularity. For this purpose, we explored the Chictopia dataset1 from [4] and implemented the paper’s main tasks. We combined features in various ways and performed regression with different models. In addition, we experimented with a multi-label, multi-class tag recommendation task: given the available post features, we predict possible tags that a user may associate with the post image. Our results show that using features from social networks can help us predict image popularity.
TencentRec: Real-time Stream Recommendation in Practice

With the arrival of the big data era, opportunities as well as challenges arise in both industry and academia. As an important service in most web applications, accurate real-time recommendation in the context of big data is of high demand. Traditional recommender systems that analyze data and update models at regular time intervals cannot satisfy the requirements of modern web applications, calling for real-time recommender systems. In this paper, we tackle the ``big", ``real-time" and ``accurate" challenges in real-time recommendation, and propose a general real-time stream recommender system built on Storm named TencentRec from three aspects, i.e., ``system", ``algorithm", and ``data". We analyze the large amount of data streams from a wide range of applications leveraging the considerable computation ability of Storm, together with a data access component and a data storage component developed by us. To deal with various application specific demands, we have implemented several classic practical recommendation algorithms in TencentRec, including the item-based collaborative filtering, the content based, and the demographic based algorithms. Specially, we present a practical scalable item-based CF algorithm in detail, with the super characteristics such as robust to the implicit feedback problem, incremental update and real-time pruning. With the enhancement of real-time data collection and processing, we can capture the recommendation changes in real-time. We deploy the TencentRec in a series of production applications, and observe the superiority of TencentRec in providing accurate real-time recommendations for 10 billion user requests everyday.
History and Heritage Made Accessible: The Lee County, Virginia Story

This thesis creates a digital space to preserve the heritage of Lee County, Virginia through community input of stories, values, and beliefs, loosely based on the concept of an eco-museum as described by Corsane, Davis, and Murtas (2009). The virtual museum space includes a history of Lee County, Virginia, and the community’s heritage ideals, as identified through local interviews, research, and stories. The results of the research created www.theleecountystory. com, a community-based website centered on heritage preservation that incorporates regional values identified through ethnographic research and educational information from local organizations and the University of Mississippi. The digital community space includes community input, local archaeology, and regional organizations, and acts to connect academics, community members, area groups, and government entities through a virtual platform. The creation of www.theleecountystory. com is meant to highlight the importance of academic work engages and incorporates the communities in which research occurs.
CYP1B1 variants are associated with prostate cancer in non-Hispanic and Hispanic Caucasians

Cytochrome P450 1B1 (CYP1B1) is involved in the activation of many carcinogens and in the metabolism of steroid hormones. We compared allele, genotype and haplotype frequencies of six single-nucleotide polymorphisms (SNPs) within CYP1B1 among non-Hispanic Caucasians (496 cases and 498 controls) and Hispanic Caucasians (153 cases and 240 controls). In the Hispanic Caucasians, the GG genotype for rs1056836 decreased the risk for prostate cancer (PCa) when compared with the CC genotype [odds ratio (OR) = 0.31, P = 0.04, 95% confidence interval (CI) = 0.10–0.96]. Among non-Hispanic Caucasian men with more aggressive PCa, the prevalence of several SNPs (rs2567206, rs2551188, rs2617266, rs10012 and rs1056836) was significantly associated with the disease status. A common C-G-C-C-G-A haplotype for rs2567206-rs2551188-rs2617266-rs10012-rs1056836-rs1800440 showed an inverse association with PCa risk in Hispanic Caucasians (OR = 0.19, P = 0.04, 95% CI = 0.04–0.95) and with aggressive disease status (i.e. Gleason score ≥7) in non-Hispanic Caucasian cases (OR = 0.64, P = 0.008, 95% CI = 0.47–0.89). In the non-Hispanic Caucasian cases, a second major haplotype T-A-T-G-C-A was positively associated with the high-grade disease status (OR = 1.77, P = 0.002, 95% CI = 1.24–2.53). Our findings suggest that genetic polymorphisms in CYP1B1 may modify the risk for PCa and support the role of CYP1B1 as a candidate gene for PCa.
Friend-or-Foe Q-learning in General-Sum Games

This paper des ribes an approa h to rein-for ement learning in multiagent general-sum games in whi h a learner is told to treat ea h other agent as either a \friend" or \foe". This Q-learning-style algorithm provides strong onvergen e guarantees ompared to an existing Nash-equilibrium-based learning rule.
Posttraumatic Stress Disorder and Community Collective Efficacy following the 2004 Florida Hurricanes

There is a paucity of research investigating the relationship of community-level characteristics such as collective efficacy and posttraumatic stress following disasters. We examine the association of collective efficacy with probable posttraumatic stress disorder and posttraumatic stress disorder symptom severity in Florida public health workers (n = 2249) exposed to the 2004 hurricane season using a multilevel approach. Anonymous questionnaires were distributed electronically to all Florida Department of Health personnel nine months after the 2004 hurricane season. The collected data were used to assess posttraumatic stress disorder and collective efficacy measured at both the individual and zip code levels. The majority of participants were female (80.42%), and ages ranged from 20 to 78 years (median = 49 years); 73.91% were European American, 13.25% were African American, and 8.65% were Hispanic. Using multi-level analysis, our data indicate that higher community-level and individual-level collective efficacy were associated with a lower likelihood of having posttraumatic stress disorder (OR = 0.93, CI = 0.88–0.98; and OR = 0.94, CI = 0.92–0.97, respectively), even after adjusting for individual sociodemographic variables, community socioeconomic characteristic variables, individual injury/damage, and community storm damage. Higher levels of community-level collective efficacy and individual-level collective efficacy were also associated with significantly lower posttraumatic stress disorder symptom severity (b = −0.22, p<0.01; and b = −0.17, p<0.01, respectively), after adjusting for the same covariates. Lower rates of posttraumatic stress disorder are associated with communities with higher collective efficacy. Programs enhancing community collective efficacy may be an important part of prevention practices and possibly lead to a reduction in the rate of posttraumatic stress disorder post-disaster.
Predicting Spatial and Temporal Gene Expression Using an Integrative Model of Transcription Factor Occupancy and Chromatin State

Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.
Home-based nursing interventions improve knowledge of disease and management in patients with heart failure 1

OBJECTIVE: to assess patient knowledge of heart failure by home-based measurement of two NOC Nursing Outcomes over a six-month period and correlate mean outcome indicator scores with mean scores of a heart failure Knowledge Questionnaire. METHODS: in this before-and-after study, patients with heart failure received four home visits over a six-month period after hospital discharge. At each home visit, nursing interventions were implemented, NOC outcomes were assessed, and the Knowledge Questionnaire was administered. RESULTS: overall, 23 patients received home visits. Mean indicator scores for the outcome Knowledge: Medication were 2.27±0.14 at home visit 1 and 3.55±0.16 at home visit 4 (P<0.001); and, for the outcome Knowledge: Treatment Regimen, 2.33±0.13 at home visit 1 and 3.59±0.14 at home visit 4 (P<0.001). The correlation between the Knowledge Questionnaire and the Nursing Outcomes Classification scores was strong at home visit 1 (r=0.7, P<0.01), but weak and non significant at visit 4. CONCLUSION: the results show improved patient knowledge of heart failure and a strong correlation between Nursing Outcomes Classification indicator scores and Knowledge Questionnaire scores. The NOC Nursing Outcomes proved effective as knowledge assessment measures when compared with the validated instrument.
Binding Properties of General Odorant Binding Proteins from the Oriental Fruit Moth, Grapholita molesta (Busck) (Lepidoptera: Tortricidae)

Background The oriental fruit moth Grapholita molesta is a host-switching pest species. The adults highly depend on olfactory cues in locating optimal host plants and oviposition sites. Odorant binding proteins (OBPs) are thought to be responsible for recognizing and transporting hydrophobic odorants across the aqueous sensillum lymph to stimulate the odorant receptors (ORs) within the antennal sensilla and activate the olfactory signal transduction pathway. Exploring the physiological function of these OBPs could facilitate understanding insect chemical communications. Methodology/Principal Finding Two antennae-specific general OBPs (GOBPs) of G. molesta were expressed and purified in vitro. The binding affinities of G. molesta GOBP1 and 2 (GmolGOBP1 and 2) for sex pheromone components and host plant volatiles were measured by fluorescence ligand-binding assays. The distribution of GmolGOBP1 and 2 in the antennal sensillum were defined by whole mount fluorescence immunohistochemistry (WM-FIHC) experiments. The binding sites of GmolGOBP2 were predicted using homology modeling, molecular docking and site-directed mutagenesis. Both GmolGOBP1 and 2 are housing in sensilla basiconica and with no differences in male and female antennae. Recombinant GmolGOBP1 (rGmolGOBP1) exhibited broad binding properties towards host plant volatiles and sex pheromone components; rGmolGOBP2 could not effectively bind host plant volatiles but showed specific binding affinity with a minor sex pheromone component dodecanol. We chose GmolGOBP2 and dodecanol for further homology modeling, molecular docking, and site-directed mutagenesis. Binding affinities of mutants demonstrated that Thr9 was the key binding site and confirmed dodecanol bonding to protein involves a hydrogen bond. Combined with the pH effect on binding affinities of rGmolGOBP2, ligand binding and release of GmolGOBP2 were related to a pH-dependent conformational transition. Conclusion Two rGmolGOBPs exhibit different binding characteristics for tested ligands. rGmolGOBP1 has dual functions in recognition of host plant volatiles and sex pheromone components, while rGmolGOBP2 is mainly involved in minor sex pheromone component dodecanol perception. This study also provides empirical evidence for the predicted functions of key amino acids in recombinant protein ligand-binding characteristics.