Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Birmingham A season for all things

Phenology plays an important role in many human–nature interactions, but these seasonal patterns are often overlooked in conservation. Here, we provide the first broad exploration of seasonal patterns of interest in nature across many species and cultures. Using data from Wikipedia, a large online encyclopedia, we analyzed 2.33 billion pageviews to articles for 31,751 species across 245 languages. We show that seasonality plays an important role in how and when people interact with plants and animals online. In total, over 25% of species in our data set exhibited a seasonal pattern in at least one of their language-edition pages, and seasonality is significantly more prevalent in pages for plants and animals than it is in a random selection of Wikipedia articles. Pageview seasonality varies across taxonomic clades in ways that reflect observable patterns in phenology, with groups such as insects and flowering plants having higher seasonality than mammals. Differences between Wikipedia language editions are significant; pages in languages spoken at higher latitudes exhibit greater seasonality overall, and species seldom show the same pattern across multiple language editions. These results have relevance to conservation policy formulation and to improving our understanding of what drives human interest in biodiversity. how well it fit a seasonal pattern and in doing so found several interesting patterns. First, seasonality is a significant factor in when people view information for many plants and animals online; over 20% of all of our species pages met our criteria for seasonality. Second, the prevalence of seasonality varies across different biological classes and also across languages. These variations appear to reflect differences in the life history of species and in the geographic distribution of languages and can correspond to phenological patterns in nature. Our results are relevant to conservationists seeking to understand how interest in various plants and animals may fluctuate over time.
International Conference on Recent Trends in Engineering & Technology ( ICRTET 2012 ) ISBN : 978-81-925922-0-6 31 Heterogeneous Protocol for Wireless Sensor Networks

Wireless sensor networks (WSN) are spatially distributed micro sensor systems. A WSN with large number of tiny nodes can be effectively used for gathering data in variety of environments like civil or military applications. WSN is a power constrained system, since nodes run on limited power batteries which shorten the life time of the system. Prolonging the life time depends on efficient communication protocol architecture. Energy consumption is therefore one of the most crucial design issue in WSN. So major challenge in WSN is to developing energy-efficient routing protocol which has significant impact on overall life time of the WSN.A clustering technique are the basics technique used to increase scalability, efficiency & life time of the sensors. LEACH hierarchical homogeneous communication protocol which greatly minimizes the energy consumed in collecting and disseminating data. In LEACH sensors nodes are considered to be homogeneous but some nodes may be used of different energy to prolong the life time of WSN and its reliability. In this paper, we study the impact of heterogeneity in energy of nodes to prolong the life time of WSN. A protocol is proposed, which is heterogeneous in energy than the LEACH protocol. Simulation results using NS-2.27 are shows that the proposed Leachheterogeneous system significantly reduces energy consumption and increase the total lifetime of the wireless sensor network compared to the homogeneous LEACH protocol.
Continuous‐discrete filters for bearings‐only underwater target tracking problems

In this work, we develop a continuous‐discrete shifted Rayleigh filter (CD‐SRF) and a continuous‐discrete sparse‐grid Gauss‐Hermite filter (CD‐SGHF) for a real‐life passive underwater bearings‐only target tracking problem. The stochastic difference equation describing the process model is derived from its continuous equivalent using Ito‐Taylor expansion of order 1.5. The performance of the proposed filters is compared in terms of root mean square error (RMSE), track divergence and computational time. For a fair comparison, popular filters like the unscented Kalman filter (UKF), cubature Kalman filter (CKF) and Gauss–Hermite filter (GHF) are implemented. The effect of initial uncertainty, measurement noise covariance and sampling time on filtering accuracy is also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer–Rao lower bound (CRLB). From simulation results, it was observed that CD filters performed with higher accuracy than their discrete equivalents, with CD‐SRF proving to be the most accurate among all the filters.
Environmental monitoring and assessment program forest health monitoring quality assurance project plan for detection monitoring project

The Quality Assurance Project Plan (QAP) is written specifically for the Detection Minitoring project of the interagency Forest Health Monitoring (FHM) program. Sections 1 through 3 briefly explain key features of the Environmental Monitoring and Assessment Program (EMAP), the FHM program, and their interrelationship, respectively. Section 4 describes the general quality assurance (QA) requirements for the FHM Detection Monitoring project. Section 5 contains the separate QAPs for each forest condition indicator: site condition and tree growth and regeneration, tree crown condition, tree damage assessment, photosynthetically active radiation (PAR), vegetation structure, ozone bioindicator plants, and lichen communities.
Budapest University of Technology and Economics PhD School in Psychology Department of Ergonomics and Psychology DEVELOPMENT OF A TEAM-LEVEL USABILITY TESTING METHOD OF GROUPWARE Thesis booklet

publications Geszten, D. (2021). Teammunkát támogató szoftverek felhasználói élménye a team-szintű használhatósági vizsgálat módszer kialakításának folyamata. A Magyar Pszichológia Társaság XXIX. Országos Tudományos Nagygyűlésének Kivonatkötete, 2021, 87-88.
Unlocking the Antibiofilm Potential of Natural Compounds by Targeting the NADH:quinone Oxidoreductase WrbA

Biofilm-dwelling cells endure adverse conditions, including oxidative imbalances. The NADH:quinone oxidoreductase enzyme WrbA has a crucial role in the mechanism of action of antibiofilm molecules such as ellagic and salicylic acids. This study aimed to exploit the potential of the WrbA scaffold as a valuable target for identifying antibiofilm compounds at non-lethal concentrations. A three-dimensional computational model, based on the published WrbA structure, was used to screen natural compounds from a virtual library of 800,000 compounds. Fisetin, morin, purpurogallin, NZ028, and NZ034, along with the reference compound ellagic acid, were selected. The antibiofilm effect of the molecules was tested at non-lethal concentrations evaluating the cell-adhesion of wild-type and WrbA-deprived Escherichia coli strains through fluorochrome-based microplate assays. It was shown that, except for NZ028, all of the selected molecules exhibited notable antibiofilm effects. Purpurogallin and NZ034 showed excellent antibiofilm performances at the lowest concentration of 0.5 μM, in line with ellagic acid. The observed loss of activity and the level of reactive oxygen species in the mutant strain, along with the correlation with terms contributing to the ligand-binding free energy on WrbA, strongly indicates the WrbA-dependency of purpurogallin and NZ034. Overall, the molecular target WrbA was successfully employed to identify active compounds at non-lethal concentrations, thus revealing, for the first time, the antibiofilm efficacy of purpurogallin and NZ034.
Evidence of correlated incommensurate structural and magnetic order in highly oxygen-doped layered nickelate Nd 2 NiO 4 . 23

Nd 2 NiO 4 + δ is a nonstoichiometric oxide, crystallizing in the Ruddlesden-Popper-type framework and exhibiting a wide range of oxygen nonstoichiometry δ with a complex structural phase diagram. Excess oxygen atoms insert holes, directly inﬂuencing the Ni valence state and associated electronic structure. We report here the existence of a correlated incommensurate structural and antiferromagnetic order below T N ≈ 150 K in highly oxygen-doped Nd 2 NiO 4 + δ with δ ≈ 0.23. The crystal and magnetic structures are investigated by polarized and unpolarized single-crystal neutron diffraction studies together with synchrotron x-ray powder diffraction and macroscopic magnetization measurements. Due to the long-range ordering of excess oxygen atoms, the real structure of the compound is incommensurately modulated, represented by Q n = ± 0 . 813 a ∗ ± 0 . 519 b ∗ wave vectors. The antiferromagnetic order is characterized by the presence of incommensurate Bragg peaks of type ( h ± ε , 0, l / 2), with h and l being odd-integer numbers and the magnetic incommensurability ε ≈ 0.36. The in-plane magnetic correlation length is found to be ξ ab = 184(1) Å but is signiﬁcantly reduced along the c axis to ξ c = 39(2) Å, indicating the quasi-two-dimensional nature of the antiferromagnetic correlations. Our study indicates that the excess oxygen atoms nevertheless strongly enhance the magnetic correlations between the NiO 2 planes, resulting in a doubling of the magnetic unit cell along the c axis. In contrast to Nd 2 NiO 4 . 1 , in which the incommensurate periodicity of oxygen and magnetic ordering was found to be identical, the structural and electronic orderings in heavily oxygen-doped Nd 2 NiO 4 . 23 show two independent modulation vectors. Our study thus unravels a strong correlation between structural and electronic orderings in the highly oxygen-doped Ln 2 NiO 4 + δ system.
International Conference on Recent Trends in Computing 2015 ( ICRTC-2015 ) A Novel Approach for Finding Rare Items Based on Multiple Minimum Support Framework

Pattern mining methods describe valuable and advantageous items from a large amount of records stored in the corporate datasets and repositories. While mining, literature has almost singularly focused on frequent itemset but in many applications rare ones are of higher interest. For Example medical dataset can be considered, where rare combination of prodrome plays a vital role for the physicians. As rare items contain worthwhile information, researchers are making efforts to examine effective methodologies to extract the same. In this paper, an effort is made to analyze the complete set of rare items for finding almost all possible rare association rules from the dataset. The Proposed approach makes use of Maximum constraint model for extracting the rare items. A new approach is efficient to mine rare association rules which can be defined as rules containing the rare items. Based on the study of relevant data structures of the mining space, this approach utilizes a tree structure to ascertain the rare items. Finally, it is demonstrated that this new approach is more virtuous and robust than the existing algorithms. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015).
Beta Upper Confidence Bound Policy for the Design of Clinical Trials

The multi-armed bandit problem is a classic example of the exploration-exploitation trade-off well suited to model sequential resource allocation under uncertainty. One of its typical motivating applications is the adaptive designs in clinical trials which modify the trial's course in accordance with the pre-specified objective by utilizing results accumulating in the trial. Since the response to a procedure in clinical trials is not immediate, the multi-armed bandit policies require adaptation to delays to retain their theoretical guarantees. In this work, we show the importance of such adaptation by evaluating policies using the publicly available datasetThe International Stroke Trial of a randomized trial of aspirin and subcutaneous heparin among 19,435 patients with acute ischaemic stroke. In addition to adapted policies, we analyze the Upper Confidence Bound policy with the beta feedback to mitigate delays when the certainty evidence of successful treatment is available in a relatively short-term period after the procedure.
FROM THE VENDOR'S PERSPECTIVE: EXPLORING THE VALUE PROPOSITION IN INFORMATION TECHNOLOGY

To date, most research on information technology (IT) outsourcing concludes that firms decide to outsource IT services because they believe that outside vendors possess production cost advantages. Yet it is not clear whether vendors can provide production cost advantages, particularly to large firms who may be able to replicate vendors’ production cost advantages in-house. Mixed outsourcing success in the past decade calls for a closer examination of the IT outsourcing vendor’s value proposition. While the client’s sourcing decisions and the client-vendor relationship have been examined in IT outsourcing literature, the vendor’s perspective has hardly been explored. In this paper, we conduct a close examination of vendor strategy and practices in one long-term successful applications management outsourcing engagement. Our analysis indicates that the vendor’s efficiency was based on the economic benefits derived from the ability to develop a complementary set of core competencies. This ability, in turn, was based on the centralization of decision rights from a variety and multitude of IT projects controlled by the vendor. The vendor was enticed to share the value with the client through formal and informal relationship management structures. We use the economic concept of
Advancing Active Authentication for User Privacy and Revocability with BioCapsules

Biometric Facial Authentication has become a pervasive mode of authentication in recent years. With this surge in popularity, concerns over the security and privacy of biometrics-based systems have grown. Therefore, there is a need for a system that can address security and privacy issues while remaining user-friendly and practical. The BioCapsule scheme is a flexible solution that can be embedded in existing biometrics systems in order to provide robust security and privacy protections. While BioCapsules have been evaluated for their static face authentication capabilities, this paper extends the scheme to Active Authentication, where a user is continuously authenticated throughout a session. We use the MOBIO dataset, which contains video recordings of 150 individuals using mobile devices over several sessions, in order to evaluate the BioCapsule scheme within the domain of Active Authentication. We find that the BioCapsule scheme not only performs comparably to baseline, unsecured system performance, but in some cases exceeds baseline performance in terms of False Acceptance Rate, False Rejection Rate, and Equal Error Rate. Through our experiments, we demonstrate that the BioCapsule scheme is a powerful and practical addition to existing biometrics-based Active Authentication systems to provide robust security and privacy protections.
Say Yes To The Fetch: Product Retrieval on a Structured Multimodal Catalog

Multimodal conversational agents allow the user to communicate through natural language and visual information. In e-commerce, this type of agents have the potential to lead to realistic and dynamic shopping experiences, where the cos-tumer finds the desired products more efficiently with the help of an agent. A common approach in this scenario is to build a representation space where both the textual and visual information of a product are close. Then, it is possible to search and retrieve products with queries from any of the modalities. This work proposes to generate this joint representation space by also taking into account prior knowledge about the fashion domain, to ensure that the retrieved products comply with the target type of products. Combining label relaxation with a taxonomy-based regularization, the proposed approach diminishes the penalization of the contrastive loss by assigning a smaller loss to other acceptable matches. Our results show that the proposed approach significantly reduces gross errors, like retrieving pants when the costumer is looking for t-shirts, while simultaneously achieving good retrieval performances. Additionally, this approach allows multimodal queries, where specific attributes can be modified by manipulating a visual query with text.
A semi-parametric hourly space–time weather generator

Abstract. Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space–time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space–time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50–20 000 km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.
Framing Blame: Toward a Better Understanding of the Effects of Populist

Although previous research has argued that the media play a crucial role in populism’s success, we know too little about how populist messages affect preferences for populist parties. To advance this knowledge, we conducted an experiment in which the core of populist rhetoric – constructing the people as innocent in-group opposed to the establishment as culprit out-group – was manipulated in newspaper articles. The findings indicate that when political elites are blamed for a salient national problem, people are more likely to vote for a populist party and less likely to vote for the largest party in government. Populist vote intentions are indirectly affected via blame perceptions. These findings offer important insights into the media’s role in the electoral success of populism. Populist political parties have become increasingly more popular over the last decades. In the Netherlands, for example, Wilders’ right-wing Freedom Party became the third largest party in the general elections of 2010, winning 24 seats in parliament. On the left, Syriza made it into Greece’s government in 2015. In Latin America, populism has been influential for more than a century (e.g., Kaufman & Stallings, 1991). The success of populist movements has not gone unnoticed by scholars, as a large body of research has started to explain populist success from both the supply-side of populist parties and communication (e.g., Jagers & Walgrave, 2007) and the demand-side of populist voters (e.g., Mudde, 2007). On the supply-side, media coverage is argued to play a pivotal role in getting populist viewpoints across (e.g., Krämer, 2014). The issues addressed by populist parties are argued to be newsworthy, which enhances the visibility of populist actors and statements in the mass media (e.g., Boomgaarden & Vliegenthart, 2007; Rooduijn, 2014). People are assumed to be exposed to populist messages on a regular basis, which may affect how people think about politics and society in important ways (e.g., Krämer, 2014; Mazzoleni, 2008; Mudde, 2004). Mudde, for example, has claimed that media coverage has partially caused the success of populist parties. As other scholars have argued that populist communication is highly persuasive, the role of the media in populism’s global electoral success should not be underestimated (Hawkins, 2010; Rooduijn, 2014). Although scholars have increasingly emphasized the relevance of the media in populism’s success, empirical research that causally related exposure to populist messages to populist voting is scarce (for exceptions, see Bos, van der Brug & de Vreese, 2011; Sheets, Bos & Boomgaarden, 2016). Despite these few examples, little is known about how populist messages affect populist voting. To arrive at a better understanding of the effects of populist messages on populist voting and the mechanisms underlying
TroCCiBras and its partner projects HIBISCUS and TROCCINOX: The 2004 field campaign in the State of São Paulo

This overview paper summarizes the objectives of the Tropical Convection and Cirrus Brasil project, as well as those of the European Commission sponsored HIBISCUS and TROCCINOX projects, which conducted a joint field campaign in the State of Sao Paulo from January to March 2004. Emphasis is given on the description of different types of lidars and their deployment for obtaining meteorological data during the joint field campaign in 2004.
Algorithm for Optimal Sizing of PV/Wind/Diesel Hybrid Microgrid System Solar Cell Anomaly Detection Based on Wavelet Scattering Transform and Artificial Intelligence

The detection of the anomalies of the solar cells is done by testing the cells in the lab. However, this method is time consuming and expensive. The analysis of infrared solar cell images can reveal its status by classifying infrared images into anomaly and non-anomaly classes. The anomality can be due to many reasons. Therefore, it is required to not only classify image into anomaly and non-anomaly, but also, detect the anomality type. The image-based solar cell anomaly detection methods appearing in the literature used either machine learning or deep learning techniques. The main disadvantages of these methods are the lack of sufficient dataset and/or utilizing inappropriate features for classification. Machine learning requires robust feature extractor which are independent on the imaging condition. On the other hand, deep learning techniques doesn’t require feature extractor, however, results depend on the implemented filters in the network i.e the network architecture. In this proposal, we deal with multi-class anomaly detection from infrared images by using better representation of the images features by using Wavelet scattering Transform (WST). The WST coefficients are stable under signal deformations and globally invariant to signal translation and rotation. Based on the simulation results, the proposed method achieved an average accuracy of 99.98%.