Regression Driven F--Transform and Application to Smoothing of Financial Time Series

In this paper we propose to extend the definition of fuzzy transform in order to consider an interpolation of models that are richer than the standard fuzzy transform. We focus on polynomial models, linear in particular, although the approach can be easily applied to other classes of models. As an example of application, we consider the smoothing of time series in finance. A comparison with moving averages is performed using NIFTY 50 stock market index. Experimental results show that a regression driven fuzzy transform (RDFT) provides a smoothing approximation of time series, similar to moving average, but with a smaller delay. This is an important feature for finance and other application, where time plays a key role.Comment: IFSA-SCIS 2017, 5 pages, 6 figures, 1 tabl

Neural Network Aided Glitch-Burst Discrimination and Glitch Classification

We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored.In order to provide a proof of concept,we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-tonoise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where someintelligenceat the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry point

Fitting ST-OWA operators to empirical data

The OWA operators gained interest among researchers as they provide a continuum of aggregation operators able to cover the whole range of compensation between the minimum and the maximum. In some circumstances, it is useful to consider a wider range of values, extending below the minimum and over the maximum. ST-OWA are able to surpass the boundaries of variation of ordinary compensatory operators. Their application requires identification of the weighting vector, the t-norm, and the t-conorm. This task can be accomplished by considering both the desired analytical properties and empirical data.<br /

A model for opinion agreement and confidence in multi-expert multi-criteria decision making

In multi-expert multi-criteria decision making problems, we often have to deal with different opinions, different importance of criteria and experts, missing data, unexpressed opinions and experts who are not fully confident with their judgment. All these factors make the problem more dificult to solve, and run the risk of making the model logic less transparent. In this paper, we present a model based on simple assumptions described by logical rules, in order to maintain the model transparency and verifiability. In particular the model explicitly considers the level of agreement of experts, such as their importance and confidence

Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification. <br /

Implementing the Future Rural Policy. A Multi-stakeholder Governance Test in Reality

In a changing and turbulent economic global scenario, the public sustain to agriculture will face counteracting local forces originating from some local actors, unwilling to accept all the facets of the CAP and the RDP. Power and leadership of actors and main social leader can help or contrasts implementation of public policies. So that policymakers will not avoid the confron- tation with people and the hard work to continuously survey their willings and interests. The comparison between two case-studies located in different Italian regions (the Marches and Friuli Venezia-Giulia) showed the essential role of local interests in the success/failure of implement- ing the public interventions. The paper reports results of case-studies located in different socio- geographical areas, the case of the \u2018Verdicchio of Matelica Wine Road\u2019 in the Marches, and the case of the \u2018Rural District Bassa Pianura Friulana\u2019. The failure of a proposed rural policy depends on local counteracting interests. Implementing the Future Rural Policy in a view of positive suc- cess does necessitate the continual and fatiguing consultations with local communities

First Report of Tomato Leaf Curl New Delhi Virus Causing Yellow Leaf Curl of Pepper in Europe

Tomato leaf curl New Delhi virus (ToLCNDV), a bipartite begomovirus (family Geminiviridae) with two circular ssDNA genome components (DNA-A and DNA-B), is transmitted in a circulative nonpropagative manner by the whitefly Bemisia tabaci (Gennadius). Although it was first reported in Asia on tomato and other solanaceous crops such as eggplant, potato, and chilli pepper in the Mediterranean basin, this virus was mainly detected on cucurbits and only sporadically on tomato and on two wild solanaceous species, Datura stramonium L. and Solanum nigrum L. (Juárez et al. 2019). In 2018, separate surveys were carried out in protected cultivations of sweet pepper (Capsicum annuum L.) in two Italian regions: Lazio and Campania. The greenhouses were in areas with high density of B. tabaci and where ToLCNDV outbreaks occurred on cucurbits since 2016 (Panno et al. 2019). Some plants showing symptoms of yellowing and leaf curling were found in both regions, whereas fruit symptoms were neither observed nor reported by farmers. This disease syndrome, known as yellow leaf curl disease (YLCD), can be caused in pepper by several begomoviruses, as reported recently in a review listing the viruses causing YLCD in peppers in Thailand (Chiemsombat et al. 2018). Symptomatic leaves were collected during late summer 2018 from different pepper plants as well as from the neighboring zucchini cultivations, showing the typical symptomatology induced by ToLCNDV. Total DNA was extracted (DNeasy Plant Mini kit, Qiagen, Germany), and the presence of ToLCNDV was ascertained by PCR with the specific primers ToLCNDV-CP1 and ToLCNDV-CP2 (Panno et al. 2019; Parrella et al. 2018). ToLCNDV infection was further ascertained in three symptomatic leaf samples from Campania by using specific ToLCNDV ImmunoStrips (Agdia, Elkhart, IN). Successively, one symptomatic pepper sample from each greenhouse was selected and amplified by rolling circle amplification technique (RCA; Inoue-Nagata et al. 2004). The amplicons were cloned, and the DNA-A and DNA-B were full-length sequenced. The sequences were deposited in GenBank NCBI database (MK732932 DNA-A and MK732933 DNA-B, pepper sample from Campania; MK756106 DNA-A and MK756107 DNA-B, pepper sample from Lazio). The RCA analysis was performed also on a ToLCNDV-infected zucchini sample collected in the same area in Lazio region (MK756108 DNA-A and MK756109 DNA-B). The analysis of the ToLCNDV sequences showed a low level of genetic variability between the two pepper isolates from Lazio and Campania regions (rate of substitutions: 0.016 for DNA-A and 0.023 for DNA-B). A high genetic similarity was recorded between the zucchini isolate and both the pepper isolates from Campania (0.019 for DNA-A and 0.023 for DNA-B) and Lazio (0.003 for both DNA-A and B). The three characterized isolates showed a high sequence homology also with both the DNA-A (MH577751 from a melon isolate) and DNA-B (MH577673 from a zucchini isolate) of the ToLCNDV-ES genotype (Fortes et al. 2016), which differed in 15 and 13 nucleotide substitutions from pepper sample from Lazio, 29 and 51 substitutions from Campania sample, and 10 and 5 substitutions from zucchini sample. High homology was also identified compared with the other Spanish isolates collected since the first appearance of the virus (2014) and to the Tunisian (2015) and Moroccan (2018) isolates, confirming the hypothesis that the Mediterranean population of ToLCNDV is highly conserved (Juárez et al. 2019). To our knowledge, this is the first report of ToLCNDV infection on pepper in Europe and indicates that sweet pepper could also act as a reservoir of the virus for further spread to other solanaceous plants and cucurbits