Miracle-dz/UniFAR-FTUs huggingface dataset:
Forest fire detection using spiking neural networks

Forest fires is one of the main causes of environmental degradation and its detection and forecasting is challenging. A novel method of forest fire detection based on spiking neural networks is proposed in this paper. Data obtained from controlled experiments are used as input training samples and a detection model is established by considering the factors of temperature, humidity, carbon monoxide concentration, wind speed and wind direction. Experimental results show that the spiking neural network can achieve a detection accuracy of ∼91%, and therefore provides a better power/accuracy trade-off against existing approaches.
Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter

Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets. We present a method that relies solely on the account profile, allowing for the classification of individuals versus organizations based on a single tweet. Our method obtains accuracies comparable to methods that rely on much more information by leveraging two improvements: a character-based Convolutional Neural Network, and an automatically derived labeled corpus an order of magnitude larger than the previously available dataset. We make both the dataset and the resulting tool available.
Inhibition of Homophilic Interactions and Ligand Binding of the Receptor for Advanced Glycation End Products by Heparin and Heparin-Related Carbohydrate Structures

Background: Heparin and heparin-related sulphated carbohydrates inhibit ligand binding of the receptor for advanced glycation end products (RAGE). Here, we have studied the ability of heparin to inhibit homophilic interactions of RAGE in living cells and studied how heparin related structures interfere with RAGE–ligand interactions. Methods: Homophilic interactions of RAGE were studied with bead aggregation and living cell protein-fragment complementation assays. Ligand binding was analyzed with microwell binding and chromatographic assays. Cell surface advanced glycation end product binding to RAGE was studied using PC3 cell adhesion assay. Results: Homophilic binding of RAGE was mediated by V1- and modulated by C2-domain in bead aggregation assay. Dimerisation of RAGE on the living cell surface was inhibited by heparin. Sulphated K5 carbohydrate fragments inhibited RAGE binding to amyloid β-peptide and HMGB1. The inhibition was dependent on the level of sulfation and the length of the carbohydrate backbone. α-d-Glucopyranosiduronic acid (glycyrrhizin) inhibited RAGE binding to advanced glycation end products in PC3 cell adhesion and protein binding assays. Further, glycyrrhizin inhibited HMGB1 and HMGB1 A-box binding to heparin. Conclusions: Our results show that K5 polysaccharides and glycyrrhizin are promising candidates for RAGE targeting drug development.
Interactive Analysis of Word Vector Embeddings

Word vector embeddings are an emerging tool for natural language processing. They have proven beneficial for a wide variety of language processing tasks. Their utility stems from the ability to encode word relationships within the vector space. Applications range from components in natural language processing systems to tools for linguistic analysis in the study of language and literature. In many of these applications, interpreting embeddings and understanding the encoded grammatical and semantic relations between words is useful, but challenging. Visualization can aid in such interpretation of embeddings. In this paper, we examine the role for visualization in working with word vector embeddings. We provide a literature survey to catalogue the range of tasks where the embeddings are employed across a broad range of applications. Based on this survey, we identify key tasks and their characteristics. Then, we present visual interactive designs that address many of these tasks. The designs integrate into an exploration and analysis environment for embeddings. Finally, we provide example use cases for them and discuss domain user feedback.
Association Between Transfusion of RBCs and Subsequent Development of Delirium in Critically Ill Children*

Objectives: To determine the temporal relationship between the transfusion of RBCs and the subsequent development of delirium in a cohort of critically ill children. Design: Nested retrospective cohort study within prospective cohort study. Setting: Urban academic tertiary care PICU. Patients: All consecutive admissions from September 2014 through August 2015. Interventions: Children were screened twice daily for delirium during their PICU admission. Measurements and Main Results: Among 1,547 independent admissions screened for delirium, 166 (10.7%) were transfused RBCs. Children who were transfused RBCs were more than twice as likely to be delirious during their admission compared with children who were never transfused, after controlling for known predictors of delirium development (adjusted odds ratio, 2.16; 95% CI, 1.38–3.37; p = 0.001). Among transfused children, a temporal relationship was observed between receipt of RBCs and the subsequent development of delirium. For each additional 10 mL/kg of RBCs transfused, the recipients were 90% more likely to develop delirium or coma in the 72 hours following the transfusion, after controlling for confounders (adjusted odds ratio, 1.90; 95% CI, 1.14–3.17; p = 0.01). Anemia (represented by nadir hemoglobin prior to transfusion) was not associated with delirium development. Conclusions: In this cohort of critically ill children, there is an independent association between the receipt of an RBC transfusion and the subsequent development of delirium. Further prospective studies are warranted to replicate this finding and investigate possible pathophysiologic mechanisms for this association.
Sugar-Sweetened Soft Drinks and Fructose Consumption Are Associated with Hyperuricemia: Cross-Sectional Analysis from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

The secular trend of hyperuricemia coincides with the substantial increase in the consumption of sugar-sweetened beverages. Our aim was to evaluate the association between the consumption of soft drinks, dietary fructose and unsweetened, non-processed fruit juices with hyperuricemia in a cross-sectional analysis of baseline data (2008–2010; n = 7173) of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The explanatory variables were the consumption of soft drinks, fruit juice, and fructose using a validated semi-quantitative food frequency questionnaire. The outcomes were hyperuricemia and the uric acid concentration in serum. Regression models were tested, and a significance level of 5% was adopted. In men, the daily consumption of a portion of soft drink/day (250 mL) almost doubled the chance of hyperuricemia with a linear trend. In women, the consumption of ≥0.1 to <1.0 soft drink/day was associated with a higher chance of hyperuricemia, but there was no linear trend. High fructose consumption in men and moderate and high consumption in women were associated with hyperuricemia. All categories of soft drinks consumption were linearly associated with increased serum uric acid levels. Our findings suggest that the consumption of soft drinks and dietary fructose is positively associated with a higher chance of hyperuricemia and higher uric acid levels in Brazilian adults.
Proprioceptive Weighting Ratio for Balance Control in Static Standing Is Reduced in Elderly Patients With Non–Specific Low Back Pain

MINI Elderly patients older than 65 years were divided into non-specific low back pain (NSLBP) and non-LBP (NLBP) groups. The postural control study of the relative contributions of different proprioceptive signals (relative proprioceptive weighting ratio [RPW]) revealed lower leg proprioceptive decreases (RPW 240 Hz) in NSLBP compared to NLBP. Study Design. A cross-sectional, observational study. Objective. The aim of this study was to determine a specific proprioceptive control strategy during postural balance in elderly patients with non-specific low back pain (NSLBP) and non-LBP (NLBP). Summary of Background Data. Proprioceptive decline is an important risk factor for decreased balance control in elderly patients with NSLBP. The resulting reduction in proprioception in the trunk or lower legs may contribute to a reduction in postural sway. This study aims to determine the specific proprioceptive control strategy used during postural balance in elderly patients with NSLBP and NLBP and to assess whether this strategy is related to proprioceptive decline in NSLBP. Methods. Pressure displacement centers were determined in 28 elderly patients with NSLBP and 46 elderly patients with NLBP during upright stances on a balance board without the benefit of vision. Gastrocnemius and lumbar multifidus muscle vibratory stimulations at 30, 60, and 240 Hz, respectively, were applied to evaluate the relative contributions of the different proprioceptive signals (relative proprioceptive weighting ratio, RPW) used in postural control. Results. Compared to elderly patients with NLBP, those with NSLBP had a lower RPW at 240 Hz and significantly higher RPW at 30 Hz. A logistic regression analysis showed that RPW at 240 Hz was independently associated with NSLBP after controlling for confounding factors. Conclusion. Elderly patients with NSLBP decreased their reliance on ankle strategy (RPW at 240 Hz) and hip strategy (RPW at 30 Hz) proprioceptive signals during balance control. The inability to control hip and ankle strategies indicates a deficit of postural control and is hypothesized to result from proprioceptive impairment. Moreover, elderly patients with NSLBP are at higher risk for lower leg proprioceptive decrease (240 Hz) through the NSLBP exacerbation. Level of Evidence: 4
A Statistical Model for Uplink/Downlink Intercell Interference and Cell Capacity in TDD HetNets

Time-division duplexing (TDD) systems can allow a dynamic adjustment of uplink and downlink resources according to the time-variant traffic loads. The interference generated by the concurrent uplink and downlink transmission in different cells, brings a new challenge to interference modeling in cellular networks. In this paper, an analytical framework is developed to evaluate the performance of a TDD system in heterogeneous networks (HetNets) which considers a concurrent uplink and downlink transmission in two different types of cells, macro and small cells. Firstly, we derive an analytical expression for the distribution of the interferer location considering all possible interference scenarios that could occur in TDD- based networks while taking into account the harmful impact of interference. Secondly, based on the latter result, we derive the distribution and moment generating function (MGF) of the uplink and downlink inter-cell interference considering a network consisting of one macro cell and one small cell. Finally, we build on the derived expressions to analyze the average capacity of the reference cell in both uplink and downlink transmissions. Monte- Carlo simulation results are provided to demonstrate the accuracy of the derived analytical expressions, in various realistic scenarios.
Factors Affecting the Intention to Adopt Big Data Technology : A Study Based on Financial Services Industry of Sri Lanka

The term “Big data” has gained huge popularity in recent years and it is rapidly becoming a new technology trend and a major strategic weapon for business organizations seeking to gain a competitive advantage. The main objectives of this research study are to identify the factors determining the intention to adopt big data technology in financial services industry of Sri Lanka and to investigate the nature of relationship between those factors and the intention to adopt big data technology. In order to reach these objectives, a structured questionnaire survey was carried out to collect data from 30 licensed finance companies in Sri Lanka. The data were analyzed by using multivariate quantitative techniques through descriptive and inferential statistical methods. The empirical findings reveal that there is a significant positive association among compatibility, relative advantage, technological resource competency, organizational size, absorptive capacity, competition intensity, regulatory support and organizational intention for big data technology adoption. However, complexity and environmental uncertainty have a significant negative relationship with the same. These findings will have various implications including theoretical and policy implications.
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis

Predicate argument structure analysis is a task of identifying structured events. To improve this field, we need to identify a salient entity, which cannot be identified without performing coreference resolution and predicate argument structure analysis simultaneously. This paper presents an entity-centric joint model for Japanese coreference resolution and predicate argument structure analysis. Each entity is assigned an embedding, and when the result of both analyses refers to an entity, the entity embedding is updated. The analyses take the entity embedding into consideration to access the global information of entities. Our experimental results demonstrate the proposed method can improve the performance of the inter-sentential zero anaphora resolution drastically, which is a notoriously difficult task in predicate argument structure analysis.
Duration of the Presence of Infectious Zika Virus in Semen and Serum

Zika virus (ZIKV) has recently caused a large epidemic in the Americas that is associated with birth defects. Although ZIKV is primarily transmitted by Aedes mosquitoes, ZIKV RNA is detectable in blood and semen of infected individuals for weeks or months, during which sexual and other modes of transmission are possible. However, viral RNA is usually detectable longer than infectious virus is present. We determined the frequency of isolation of infectious virus from semen and serum samples prospectively obtained from a cohort of patients in Puerto Rico. We confirmed isolation of infectious virus on the basis of a tissue culture cytopathic effect, an increase in virus genome copy equivalents (GCE), and positive results of immunofluorescence analysis; virus in infected cells was quantitated by flow cytometry. These criteria confirmed the presence of infectious virus in semen specimens from 8 of 97 patients for up to 38 days after initial detection when virus loads are >1.4 × 106 genome copy equivalents/mL. Two serum isolates were obtained from 296 patients. These findings can help guide important prevention guidelines for persons that may potentially be infectious and transmit ZIKV sexually.
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-suited to discovering errors which occur only for rare inputs. Specifically, we develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF, random mutations of inputs to a neural network are guided by a coverage metric toward the goal of satisfying user-specified constraints. We describe how fast approximate nearest neighbor algorithms can provide this coverage metric. We then discuss the application of CGF to the following goals: finding numerical errors in trained neural networks, generating disagreements between neural networks and quantized versions of those networks, and surfacing undesirable behavior in character level language models. Finally, we release an open source library called TensorFuzz that implements the described techniques.
Rethinking the Prevailing Security Paradigm

Information leakage is a major concern for organizations. As information travels through the organization's eco-system, perimeter-based defense is no longer sufficient. Rather, organizations are implementing data-centric solutions that persist throughout the information life-cycle regardless of its location. Enterprise rights management (ERM) systems are an example of persistent data-centric security. ERM defines specific access rules as an instantiation of organizational information security policies and has been suggested as means of role-based access permissions control. Yet, evidence shows that employees often circumvent or work around organizational security rules and policies since these controls hinder task-performance. In this exploratory case study, we use the theory of workarounds as a lens to examine users' workaround behavior. We introduce an empowerment-based ERM system highlighting users' permission to override provisionally assigned access rules. The concept of empowered security policies is novel and presents a shift in the current security compliance paradigm. Subsequently, we compare users' compliance intention between empowered ERM users and conventional ERM users. Our descriptive results indicate that circumventing intention is lower while perceived responsibility and task-performance benefits are higher for the empowered ERM users than for the conventional ERM users. Compliance intention is higher for conventional ERM users than for empowered ERM users.
Multi-agent Epistemic Planning with Common Knowledge

In the past decade, multi-agent epistemic planning has received much attention from both dynamic logic and planning communities. Common knowledge is an essential part of multi-agent modal logics, and plays an important role in coordination and interaction of multiple agents. However, existing implementations of multi-agent epistemic planning provide very limited support for common knowledge, basically static propositional common knowledge. Our work aims to extend an existing multi-agent epistemic planning framework based on higher-order belief change with the capability to deal with common knowledge. We propose a novel normal form for multi-agent KD45 logic with common knowledge. We propose satisfiability solving, revision and update algorithms for this normal form. Based on our algorithms, we implemented a multi-agent epistemic planner with common knowledge called MEPC. Our planner successfully generated solutions for several domains that demonstrate the typical usage of common knowledge.
Exploiting Daily Trajectories for Efficient Routing in Vehicular Ad Hoc Networks

Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent Transportation System (ITS). Considering the various applications in ITS, a VANET must provide communication solutions in different situations. In particular, we are interested in dealing with situations where the unicast communication problem occurs in sparse network scenarios. In this paper, we shed light on the need for mechanisms that take into account the vehicles' trajectories. We present a characterization that shows the spatiotemporal regularity of the vehicle movement. We propose a new methodology for identifying the spatiotemporal relationship between vehicle trajectories. We create a novel method named ROSTER for unicast routing in sparse VANETs. Simulations results show that the proposed solution considerably reduces message overhead in the network by maintaining compatible levels of delivery rate in comparison with other protocols.
Nickel exposure reduces enterobactin production in Escherichia coli

Escherichia coli is a well‐studied bacterium that can be found in many niches, such as industrial wastewater, where the concentration of nickel can rise to low‐millimolar levels. Recent studies show that nickel exposure can repress pyochelin or induce pyoverdine siderophore production in Pseudomonas aueroginosa. Understanding the molecular cross‐talk between siderophore production, metal homeostasis, and metal toxicity in microorganisms is critical for designing bioremediation strategies for metal‐contaminated sites. Here, we show that high‐nickel exposure prolongs lag phase duration as a result of low‐intracellular iron levels in E. coli. Although E. coli cells respond to low‐intracellular iron during nickel stress by maintaining high expression of iron uptake systems such as fepA, the demand for iron is not met due to a lack of siderophores in the extracellular medium during nickel stress. Taken together, these results indicate that nickel inhibits iron accumulation in E. coli by reducing the presence of enterobactin in the extracellular medium.
Semantic decomposition and marker passing in an artificial representation of meaning

The research area of Distributed Artificial Intelligence aims at building intelligent agent systems. Multi-Agent Systems have been applied successfully in many domains, from an intermodal planning domain to cascading security thread simulations. But still, agents struggle with the meaning of concepts used in language. Intelligence needs language to form thoughts. Thus, the challenge addressed in this thesis is to provide a computable representation of meaning and evaluate its usefulness. Based on the theory of a mental lexicon and the thesis that meaning is a combination of symbolic and connectionist parts, I investigate the use of the theory of Natural Semantic Metalanguage (NSM) to build an artificial representation of meaning. I show that the use of NSM for creating a semantic graph out of different information sources can be utilized as a basis for Marker Passing algorithms. The Marker Passing algorithm encodes symbolic meaning to guide the reasoning over the connectionist semantic graph. Through the combination of a semantic graph and symbolic Marker Passing, I can combine connectionist and symbolic approaches to AI research to create my artificial representation of meaning. To test my approach, I build a semantic distance measure, a word sense disambiguation algorithm and a sentence similarity measure which all go head to head with the state-of-the-art. I apply those approaches to two use cases: A semantic service match marking and a contextdependent heuristics. I evaluate my heuristic by utilizing them in AI problem-solving component which uses AI planning guided by my heuristic.