LA REVOCATORIA FALLIMENTARE. ANALISI DI UN CASO CONCRETO.

La revocatoria fallimentare: revocabilità delle rimesse in conto corrente bancario alla luce della riforma organica della legge fallimentare. Un caso concreto: l'analisi di bilancio come strumento per la prova della conoscenza dello stato d'insolvenza; proposte di elaborazione delle rimesse di conto corrente

Detection of fraudulent financial papers by picking a collection of characteristics using optimization algorithms and classification techniques based on squirrels

To produce important investment decisions, investors require financial records and economic information. However, most companies manipulate investors and financial institutions by inflating their financial statements. Fraudulent Financial Activities exist in any monetary or financial transaction scenario, whether physical or electronic. A challenging problem that arises in this domain is the issue that affects and troubles individuals and institutions. This problem has attracted more attention in the field in part owing to the prevalence of financial fraud and the paucity of previous research. For this purpose, in this study, the main approach to solve this problem, an anomaly detection-based approach based on a combination of feature selection based on squirrel optimization pattern and classification methods have been used. The aim is to develop this method to provide a model for detecting anomalies in financial statements using a combination of selected features with the nearest neighbor classifications, neural networks, support vector machine, and Bayesian. Anomaly samples are then analyzed and compared to recommended techniques using assessment criteria. Squirrel optimization's meta-exploratory capability, along with the approach's ability to identify abnormalities in financial data, has been shown to be effective in implementing the suggested strategy. They discovered fake financial statements because of their expertise

Modulation of Oscillatory Power and Connectivity in the Human Posterior Cingulate Cortex Supports the Encoding and Retrieval of Episodic Memories

Existing data from noninvasive studies have led researchers to posit that the posterior cingulate cortex (PCC) supports mnemonic processes: It exhibits degeneration in memory disorders, and fMRI investigations have demonstrated memory-related activation principally during the retrieval of memory items. Despite these data, the role of the PCC in episodic memory has received only limited treatment using the spatial and temporal precision of intracranial EEG, with previous analyses focused on item retrieval. Using data gathered from 21 human participants who underwent stereo-EEG for seizure localization, we characterized oscillatory patterns in the PCC during the encoding and retrieval of episodic memories. We identified a subsequent memory effect during item encoding characterized by increased gamma band oscillatory power and a low-frequency power desynchronization. Fourteen participants had stereotactic electrodes located simultaneously in the hippocampus and PCC, and with these unique data, we describe connectivity changes between these structures that predict successful item encoding and that precede item retrieval. Oscillatory power during retrieval matched the pattern we observed during encoding, with low-frequency (below 15 Hz) desynchronization and a gamma band (especially high gamma, 70–180 Hz) power increase. Encoding is characterized by synchrony between the hippocampus and PCC, centered at 3 Hz, consistent with other observations of properties of this oscillation akin to those for rodent theta activity. We discuss our findings in light of existing theories of episodic memory processing, including the information via desynchronization hypothesis and retrieved context theory, and examine how our data fit with existing theories for the functional role of the PCC. These include a postulated role for the PCC in modulating internally directed attention and for representing or integrating contextual information for memory items

Disseny de la metodologia per l' inspecció d'embarcacions pesqueres a Vilanova i la Geltrú

La realització d’aquest Treball Final de Carrera, tractarà de dissenyar una metodologia bàsica d’inspecció per donar a conèixer la situació en la que es treballa en el sector pesquer en l’aspecte de la seguretat i la lluita contra la contaminació. Per això, serà molt important tenir la normativa reguladora d’aquest sector constant en tot moment. Destacant el Reial Decret 543/2007, principalment, a més a més tenint en compte els diferents convenis i normatives: Conveni de Torremolinos, Conveni internacional per la seguretat de la vida humana a la mar (SOLAS), i el Conveni per prevenir la contaminació en vaixells (MARPOL 73/78). El compliment de les disposicions generals es realitzen mitjan certificats oficials de reconeixement. L’expedició d’aquests certificats es fa per part de l’administració i significa que el vaixell compleix amb la normativa especifica estipulada i es apte per la navegació. Mitjançant la comprovació d’aquests certificats es podrà avaluar si els vaixells compleixen o no deficiències en aspectes de seguretat i lluita contra la contaminació juntament amb l’elaboració d’un ‘Check-list’. L’àmbit territorial on es portarà l’estudi serà al port pesquer de Vilanova i la Geltrú, per això es realitzarà una breu explicació de la historia pesquera d’ aquesta ciutat de la província de Barcelona i una explicació de la situació socioeconòmica que es viu a l’actualitat pel que fa a temes de les captures pesqueres, la recaptació econòmica que s’ha produït, la flota i els integrants de treballadors que la formen

IFMix: Utilizing Intermediate Filtered Images for Domain Adaptation in Classification

This paper proposes an iterative intermediate domain generation method using low- and high-pass filters. Domain shift is one of the prime reasons for the poor generalization of trained models in most real-life applications. In a typical case, the target domain differs from the source domain due to either controllable factors (e.g., different sensors) or uncontrollable factors (e.g., weather conditions). Domain adaptation methods bridge this gap by training a domain-invariant network. However, a significant gap between the source and the target domains would still result in bad performance. Gradual domain adaptation methods utilize intermediate domains that gradually shift from the source to the target domain to counter the effect of the significant gap. Still, the assumption of having sufficiently large intermediate domains at hand for any given task is hard to fulfill in real-life scenarios. The proposed method utilizes low- and high-pass filters to create two distinct representatio ns of a single sample. After that, the filtered samples from two domains are mixed with a dynamic ratio to create intermediate domains, which are used to train two separate models in parallel. The final output is obtained by averaging out both models. The method’s effectiveness is demonstrated with extensive experiments on public benchmark datasets: Office-31, Office-Home, and VisDa-2017. The empirical evaluation suggests that the proposed method performs better than the current state-of-the-art works.Peer reviewe

Enhanced Data-Recalibration : Utilizing Validation Data to Mitigate Instance-Dependent Noise in Classification

This paper proposes a practical approach to deal with instance-dependent noise in classification. Supervised learning with noisy labels is one of the major research topics in the deep learning community. While old works typically assume class conditional and instance-independent noise, recent works provide theoretical and empirical proof to show that the noise in real-world cases is instance-dependent. Current state-of-the-art methods for dealing with instance-dependent noise focus on data-recalibrating strategies to iteratively correct labels while training the network. While some methods provide theoretical analysis to prove that each iteration results in a cleaner dataset and a better-performing network, the limiting assumptions and dependency on knowledge about noise for hyperparameter tuning often contrast their claims. The proposed method in this paper is a two-stage data-recalibration algorithm that utilizes validation data to correct noisy labels and refine the model iteratively. The algorithm works by training the network on the latest cleansed training Set to obtain better performance on a small, clean validation set while using the best performing model to cleanse the training set for the next iteration. The intuition behind the method is that a network with decent performance on the clean validation set can be utilized as an oracle network to generate less noisy labels for the training set. While there is no theoretical guarantee attached, the method’s effectiveness is demonstrated with extensive experiments on synthetic and real-world benchmark datasets. The empirical evaluation suggests that the proposed method has a better performance compared to the current state-of-the-art works. The implementation is available at https://github.com/Sbakhshigermi/EDR.acceptedVersionPeer reviewe

Selective Probabilistic Classifier Based on Hypothesis Testing

In this paper, we propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier. Previous works tend to apply a threshold either on the classification scores or the loss function to reject the inputs that violate the assumption. However, these methods cannot achieve the low False Positive Ratio (FPR) required in safety applications. The proposed method is a rejection option based on hypothesis testing with probabilistic networks. With probabilistic networks, it is possible to estimate the distribution of outcomes instead of a single output. By utilizing Z-Test over the mean and standard deviation for each class, the proposed method can estimate the statistical significance of the network certainty and reject uncertain outputs. The proposed method was experimented on with different configurations of the COCO and CIFAR datasets. The performance of the proposed method is compared with the Softmax Response, which is a known top-performing method. It is shown that the proposed method can achieve a broader range of operation and cover a lower FPR than the alternative.acceptedVersionPeer reviewe

Biologia de Cycloneda sanguinea e sua associação com pulgão em mudas de mangueira

Cycloneda sanguinea (Linnaeus, 1763) is one of the most common predator of aphids in Brazil. Details about its behaviour were studied on the field and its biology under laboratory conditions. It is a predator during both larval and adult phases. The female lays an average of 601 eggs well spread over a period of about 63 days (its adult life). The life cycle, from egg to adult was, in average, 18.1 days. Dinocampus coccinellae (Hymenoptera:Braconidae) was found parasiting the adults of Cycloneda sanguinea, and three other species of Hymenoptera were observed parasiting. The aphids observed were the Toxoptera aurantii (Boyer de Fonscolombe, 1841) species that, sometimes, are confused with T. citricidus (Kirk, 1907).Cycloneda sanguínea (Linnaeus, 1763) (joaninha) é um dos mais comuns predadores de afídeos do Brasil. Neste estudo, foram feitas observações em viveiro de mangueira (Mangifera indica L.) e em laboratório, pertencentes ao Centro de Pesquisa Agropecuária dos Cerrados - CPAC/EMBRAPA. Ela é predadora na fase larval e na fase adulta. A fêmea põe em média 601 ovos bem distribuídos em um período médio de 63 dias que coincide, praticamente, com a longevidade do adulto. O período de ovo a adulto tem a duração média de 18,1 dias. Foi encontrado o parasita Dinocampus coccinellae (Hymenoptera:Braconidae) parasitando o adulto da C. sanguinea, assim como três outras espécies de Hymenoptera foram observadas parasitando as larvas desse predador. Os pulgões observados na associação pertenciam à espécie Toxoptera aurantii (Boyer de Fonscolombe, 1841) que, algumas vezes, é confundida com a T. citricidus (Kirk, 1907)

The Position and Evil Role of the Wolf in Ferdowsi's Shahnameh

In analyzing epic texts, one encounters the special presence of some animals in the development of the epic genre, this presence is at times positive and totemic, and at others negative and evil. Like the wolf which in a number of world myths and epics has positive functions, but not so in others. In Shahname as well, the appearance of the wolf is mostly negative. Through the method of text analysis, this article aims to investigate the reasons for Ferdowsi’s negative view of the wolf. The results show that one important reason for presenting a negative and evil picture of the wolf in Shahname is the devilish presence of this animal in religious texts of ancient Iran, in which it was deemed a positive and rewarding action to kill this wolf. The other reason for this issue is that in Shahname, Ferdowsi equates Turanians with Turks. When he mentions Turanians, he likens them to wolves and uses the negative characteristics of these animals to describe them