Miracle-dz/UniFAR-FTUs huggingface dataset:
The Automation of Financial Statement Fraud Detection: A Framework Using Process Mining

Process mining is an analytical methodology that is used to analyze an entity’s business processes based on event logs that have been automatically recorded in the accounting information systems prior to the analysis. In order to analyze event logs, five characteristics need to be extracted from the accounting information system (Jans et al., 2014; Chiu and Jans, 2019): 1) activity: business activities in an event (e.g., “invoice receipt” in the procure-to-pay process); 2) process instance: the process instance of an event (e.g., a purchase order number in the procure-to-pay process); 3) resource: individual or party who conducted the activity (e.g., if Vincent signs purchase order A, then he is the “resource” of the activity “sign” in purchase order A); and 4) timestamp: the timestamp of an event (e.g., year, month, date, and time of the event: 2018-01-01 12:09:00). For example, if Vincent signs purchase order 0001 on 2017-12-31 at 12:00 pm, then for this event log, the activity is “Sign”, the process instance for this activity is “0001,” the resource for this activity is “Vincent,” and the timestamp for this activity is “2017-12-31 at 12:00 pm. ” The example event log for purchase order 0001 is shown in Table 1 (including the aforementioned example). [see Table 1, pg 96]
Potential biomarkers for testicular germ cell tumour: Risk assessment, diagnostic, prognostic and monitoring of recurrence

Testicular germ cell tumour (TGCT) is considered a relatively rare malignancy usually occurring in young men between 15 and 35 years of age, and both genetic and environmental factors contribute to its development. The majority of patients are diagnosed in an early‐stage of TGCTs with an elevated 5‐year survival rate after therapy. However, approximately 25% of patients show an incomplete response to chemotherapy or tumours relapse. The current therapies are accompanied by several adverse effects, including infertility. Aside from classical serum biomarker, many studies reported novel biomarkers for TGCTs, but without proper validation. Cancer cells share many similarities with embryonic stem cells (ESCs), and since ESC genes are not transcribed in most adult tissues, they could be considered ideal candidate targets for cancer‐specific diagnosis and treatment. Added to this, several microRNAs (miRNA) including miRNA‐371‐3p can be further investigated as a molecular biomarker for diagnosis and monitoring of TGCTs. In this review, we will illustrate the findings of recent investigations in novel TGCTs biomarkers applicable for risk assessment, screening, diagnosis, prognosis, prediction and monitoring of the relapse.
Systematics of Brucepattersonius Hershkovitz, 1998 (Rodentia, Sigmodontinae): molecular species delimitation and morphological analyses suggest an overestimation in species diversity

The genus Brucepattersonius currently includes eight valid species from the Atlantic Forest of south-eastern Brazil and north-eastern Argentina. Most of those species were described in the past 20 years, based on very small sample sizes: four species are known only from their type localities. Concerns on the taxonomic validity of some Brucepattersonius species have been raised in the recent literature. In this study, we present a phylogenetic hypothesis including samples throughout the distribution of the genus (among them the holotypes of two species) and employed single-locus molecular species delimitation analyses to provide a new hypothesis for species limits within Brucepattersonius. We also analysed intra- and interpopulation morphological and morphometric variation to apply an integrative taxonomic approach to species delimitation. Our phylogenetic inference of molecular data recovered four strongly supported clades within Brucepattersonius, which were recognized as distinct operational taxonomic units in all species delimitation analyses. The first two clades represent the Caparaó mountain range endemic B. griserufescens and its sister-group, the recently described species B. nebulosus. The third clade comprises several specimens assigned to B. soricinus but also includes the holotype of B. igniventris; we revise these species limits accordingly. The last clade is composed of samples from southern Brazil assigned to B. iheringi. Based on morphological and morphometric analyses, we find no clear structure among the three described species from Misiones Province, Argentina (B. guarani, B. misionensis and B. paradisus) and B. inheringi from southern Brazil, suggesting that those samples represent a single species. Therefore, in the present study we propose that the genus Brucepattersonius comprises only four diagnosable species: B. griserufescens (including B. albinasus), B. nebulosus, B. soricinus (including B. igniventris) and B. iheringi (including B. guarani, B. misionensis and B. paradisus).
Recency of Faulting and Subsurface Architecture of the San Diego Bay Pull-Apart Basin, California, USA

In Southern California, plate boundary motion between the North American and Pacific plates is distributed across several sub-parallel fault systems. The offshore faults of the California Continental Borderland (CCB) are thought to accommodate ∼10–15% of the total plate boundary motion, but the exact distribution of slip and the mechanics of slip partitioning remain uncertain. The Newport-Inglewood-Rose Canyon fault is the easternmost fault within the CCB whose southern segment splays out into a complex network of faults beneath San Diego Bay. A pull-apart basin model between the Rose Canyon and the offshore Descanso fault has been used to explain prominent fault orientations and subsidence beneath San Diego Bay; however, this model does not account for faults in the southern portion of the bay or faulting east of the bay. To investigate the characteristics of faulting and stratigraphic architecture beneath San Diego Bay, we combined a suite of reprocessed legacy airgun multi-channel seismic profiles and high-resolution Chirp data, with age and lithology controls from geotechnical boreholes and shallow sub-surface vibracores. This combined dataset is used to create gridded horizon surfaces, fault maps, and perform a kinematic fault analysis. The structure beneath San Diego Bay is dominated by down-to-the-east motion on normal faults that can be separated into two distinct groups. The strikes of these two fault groups can be explained with a double pull-apart basin model for San Diego Bay. In our conceptual model, the western portion of San Diego Bay is controlled by a right-step between the Rose Canyon and Descanso faults, which matches both observations and predictions from laboratory models. The eastern portion of San Diego Bay appears to be controlled by an inferred step-over between the Rose Canyon and San Miguel-Vallecitos faults and displays distinct fault strike orientations, which kinematic analysis indicates should have a significant component of strike-slip partitioning that is not detectable in the seismic data. The potential of a Rose Canyon-San Miguel-Vallecitos fault connection would effectively cut the stepover distance in half and have important implications for the seismic hazard of the San Diego-Tijuana metropolitan area (population ∼3 million people).
A theoretical analysis of the isothermal elastohydrodynamic lubrication of concentrated contacts. I. Direction of lubricant entrainment coincident with the major axis of the Hertzian contact ellipse

Engineers have known since the last century that a substantial film of lubricant must be present at the contact between gear teeth. However, it is only in the last twenty-five years that analytical techniques have developed to the extent where theoretical predictions of film thickness are in accord with experience. This has come about through the in corporation in analysis of the effects of elastic distortion of the solids and the enhancement of lubricant viscosity due to pressure. Formulae for the prediction of both minimum and central film thickness in concentrated contacts such as those occurring in gear sets, rolling element bearings and cam and follower arrangements are now available to designers. Elastohydrodynamic analyses have almost entirely been restricted to the case of pure rolling in which the direction of lubricant entrainment has coincided with the minor axis of the Hertzian contact ellipse. While such analyses are indeed satisfactory for a wide range of practical configurations, there are situations for which the effects of flow direction have not been adequately explored. For example, in the roller-rib contacts in cylindrical and taper roller bearings and in the conjunctions occurring in high conformity gearing, a more reasonable approximation to the geometric configuration would be to consider the lubricant entraining vector to be parallel to the major axis of the contact ellipse. More generally in helical, spiral bevel and hypoid gearing the lubricant entrainment may be at an angle to the minor axis of the Hertzian ellipse. Part I of the present paper presents a study of the case where lubricant entrainment coincides with the line of the major axis of the contact ellipse, while part II addresses the more general case of an arbitrary flow direction. Seventy-two new solutions to the problem of the elastohydrodynamic lubrication of concentrated contacts with rolling along a principal axis have been computed. In part I of the paper thirty-three of these solutions are presented for lubricant entrainment in the direction of the major axis of the contact ellipse. These latter solutions therefore extend the range of geometrical configurations considered previously by B. J. Hamrock and D. Dowson, whose design predictions are widely used at present. New expressions for the calculation of minimum and central film thickness are presented, which enable the prediction with confidence of these quantities for the case when the lubricant entraining vector coincides with the major axis of the Hertzian contact ellipse. Comparison of the very extensive data presented in the paper with the limited information available from previous relevant studies is undertaken. In addition the major features distinguishing the new solutions for those previously computed are identified. It is expected that the results of the study will enable the lubricant film thickness to be predicted with increased confidence for a wide range of machine elements.
Gas Turbine/Solar Parabolic Trough Hybrid Designs

A strength of parabolic trough concentrating solar power (CSP) plants is the ability to provide reliable power by incorporating either thermal energy storage or backup heat from fossil fuels. Yet these benefits have not been fully realized because thermal energy storage remains expensive at trough operating temperatures and gas usage in CSP plants is less efficient than in dedicated combined cycle plants. For example, while a modern combined cycle plant can achieve an overall efficiency in excess of 55%; auxiliary heaters in a parabolic trough plant convert gas to electricity at below 40%. Thus, one can argue the more effective use of natural gas is in a combined cycle plant, not as backup to a CSP plant. Integrated solar combined cycle (ISCC) systems avoid this pitfall by injecting solar steam into the fossil power cycle; however, these designs are limited to about 10% total solar enhancement. Without reliable, cost-effective energy storage or backup power, renewable sources will struggle to achieve a high penetration in the electric grid. This paper describes a novel gas turbine / parabolic trough hybrid design that combines solar contribution of 57% and higher with gas heat rates that rival that for combined cycle natural gas plants. The design integrates proven solar and fossil technologies, thereby offering high reliability and low financial risk while promoting deployment of solar thermal power.Copyright © 2011 by ASME
Effects of diversifying the planting period of maize on herbivorous and their natural enemies

Knowledge of the specific insect densities during crop development is necessary to perform appropriate measures for the control of insect pests and to minimize yield losses. In a previous study, both spatial and temporal approaches were adopted to analyse the influence of landscape structure and field variables on herbivore and predatory insects on maize. Both types of variables influenced insect abundance, but the highest effect was found with maize phenology. Given that the field planting date could modulate the influence produced by the structure of the landscape on herbivores and predatory insects, analyses of population dynamics must be performed at both the local and landscape levels. The anterior prompted us to study these aspects in the two common planting periods (early and late) in northern Spain. The present study tests the hypothesis that the period of maize planting could have a higher effect than phenology or interannual variation on the abundance of natural enemies and herbivores on maize. Our results showed that only the abundances of ‘other herbivore thrips’ and Syrphidae were significantly different between the two planting periods. Moreover, we found significant effects of planting period when we performed yearly analysis in 2015 for Coccinellidae and Chrysopidae and in 2016 and 2017 for Aeolothrips sp. Most of the taxa had abundance peaks in earlier growth stages, which are related to pollination (before or during), while only Stethorus punctillum and Syrphidae increased later in the season. Furthermore, Frankliniella occidentalis, aphids, Syrphidae and Coccinellidae registered higher abundances in fields sown in the late planting period than in the rest of the insect species. The results of the present study highlight the effects of sowing dates on insect dynamics in maize. Highlights Herbivorous and natural enemy abundance peaks in earlier growth stages. Abundance of thrip and Syrphidae were different between the two planting periods. Abundance of Coccinellidae, Chrysopidae and Aeolothrips sp. was different when performed yearly analysis.
Towards an IoT-Based Architecture for Monitoring andAutomated Decision-Making in an Aviary Environment

Internet of Things (IoT) is a prominent technology in which everyday objects can be equipped with identifying, sensing, networking, and processing capabilities that allow them to communicate with each another and with other devices and services over the Internet to achieve some goal. In agribusiness, the use of technologies, such as IoT, is called precision livestock farming, which includes the use of different technologies in production and care of livestock animals. Brazil is one of the largest poultry producers in the world, being the first in exportation. Despite that, poultry production faces difficulties due to sensitivity that birds have to numerous environmental factors in aviaries, such as lightning, sounds, harmful gases, air humidity, food quality and clean water. When these variables are not well controlled, problems in meat quality and poultry production are likely to occur. As a result, farmers may face severe financial losses. Thus, providing a healthy environment is essential, and to achieve this, accurate monitoring and fast decision making are required in order to solve the problem as soon as possible. In this paper a detailed process of requirements elicitation and architectural design for IoT-Based aviary monitoring systems is proposed along with an informal literature review from the area. As preliminary results, we delivered a requirements document with functional and nonfunctional requirements closer to the real needs of farmers and an architectural proposal that can be used as a reference for further studies.
Scale Calibration of Deep Ranking Models

Learning-to-Rank (LTR) systems are ubiquitous in web applications nowadays. The existing literature mainly focuses on improving ranking performance by trying to generate the optimal order of candidate items. However, virtually all advanced ranking functions are not scale calibrated. For example, rankers have the freedom to add a constant to all item scores without changing their relative order. This property has resulted in several limitations in deploying advanced ranking methods in practice. On the one hand, it limits the use of effective ranking functions in important applications. For example, in ads ranking, predicted Click-Through Rate (pCTR) is used for ranking and is required to be calibrated for the downstream ads auction. This is a major reason that existing ads ranking methods use scale calibrated pointwise loss functions that may sacrifice ranking performance. On the other hand, popular ranking losses are translation-invariant. We rigorously show that, both theoretically and empirically, this property leads to training instability that may cause severe practical issues. In this paper, we study how to perform scale calibration of deep ranking models to address the above concerns. We design three different formulations to calibrate ranking models through calibrated ranking losses. Unlike existing post-processing methods, our calibration is performed during training, which can resolve the training instability issue without any additional processing. We conduct experiments on the standard LTR benchmark datasets and one of the largest sponsored search ads dataset from Google. Our results show that our proposed calibrated ranking losses can achieve nearly optimal results in terms of both ranking quality and score scale calibration.
A PERFORMANCE ANALYSIS ON DRONE LOCO POSITIONING SYSTEM FOR TWO-WAY RANGING PROTOCOL

Localization is vital in UAV operation as it monitors the position of each drone in a workspace. Existing localization techniques such as GPS are limited for outdoor implementations and cannot be implemented inside closed spaces or GPS denied areas. To address this concern localization techniques, such as vision systems and radio systems, are developed. The drawback of vision systems is the cost of implementation as the system usually requires multiple cameras strategically positioned around the experimental space to monitor the aerial drone’s position and orientation. Radio localization, on the other hand, is a cheaper alternative for indoor localization as it requires only a set of anchor and tags that communicates through a certain radio frequency; however, experimental setups and materials on this localization technique is limited at this time. This paper offers an analysis of the performance of the loco positioning system, a form of radio localization, through varying configurations for swarm drone applications. The Loco Positioning System possesses two protocols; and this paper focuses on the Two-Way Ranging protocol. The study presents different setup configurations governed by 2 parameters; number of anchors used, and the distance set between anchors, and their corresponding performances. Data showed that an increase in anchor count from 3 to 6 decreases error from 25.96% to 8.45%, and that decreasing the distance between anchors 0.6 m to 1 m would give a minimal increase in error. Users may use these performance reports to determine their ideal setup based on the mentioned parameters.
When and why vision-language models behave like bags-of-words, and what to do about it?

Despite the success of large vision and language models (VLMs) in many downstream applications, it is unclear how well they encode compositional information. Here, we create the Attribution, Relation, and Order (ARO) benchmark to systematically evaluate the ability of VLMs to understand different types of relationships, attributes, and order. ARO consists of Visual Genome Attribution, to test the understanding of objects' properties; Visual Genome Relation, to test for relational understanding; and COCO&Flickr30k-Order, to test for order sensitivity. ARO is orders of magnitude larger than previous benchmarks of compositionality, with more than 50,000 test cases. We show where state-of-the-art VLMs have poor relational understanding, can blunder when linking objects to their attributes, and demonstrate a severe lack of order sensitivity. VLMs are predominantly trained and evaluated on large datasets with rich compositional structure in the images and captions. Yet, training on these datasets has not been enough to address the lack of compositional understanding, and evaluating on these datasets has failed to surface this deficiency. To understand why these limitations emerge and are not represented in the standard tests, we zoom into the evaluation and training procedures. We demonstrate that it is possible to perform well on retrieval over existing datasets without using the composition and order information. Given that contrastive pretraining optimizes for retrieval on datasets with similar shortcuts, we hypothesize that this can explain why the models do not need to learn to represent compositional information. This finding suggests a natural solution: composition-aware hard negative mining. We show that a simple-to-implement modification of contrastive learning significantly improves the performance on tasks requiring understanding of order and compositionality.
On the maximal multiplicity of long zero-sum free sequences over $C_p\oplus C_p$

In this paper, we point out that the method used in [Acta Arith. 128(2007) 245-279] can be modified slightly to obtain the following result. Let $\varepsilon \in (0,\frac 14)$ and $c>0$, and let $p$ be a sufficiently large prime depending on $\varepsilon$ and $c$. Then every zero-sumfree sequence $S$ over $C_p\oplus C_p$ of length $|S|\geq 2p-c\sqrt{p}$ contains some element at least $\lfloor p^{\frac14-\varepsilon}\rfloor$ times.
A Generalized Accelerated Composite Gradient Method: Uniting Nesterov's Fast Gradient Method and FISTA

Numerous problems in signal processing, statistical inference, computer vision, and machine learning, can be cast as large-scale convex optimization problems. Due to their size, many of these problems can only be addressed by first-order accelerated black-box methods. The most popular among these are the Fast Gradient Method (FGM) and the Fast Iterative Shrinkage Thresholding Algorithm (FISTA). FGM requires that the objective be finite and differentiable with a known gradient Lipschitz constant. FISTA is applicable to the broader class of composite objectives and is equipped with a line-search procedure for estimating the Lipschitz constant. Nonetheless, FISTA cannot increase the step size and is unable to take advantage of strong convexity. FGM and FISTA are very similar in form. However, they appear to have vastly differing convergence analyses. In this work we unite the two analyses. We generalize the previously introduced augmented estimate sequence framework as well as the related notion of the gap sequence. We use our tools to construct a Generalized Accelerated Composite Gradient Method that encompasses FGM and FISTA, along with their most popular variants. The Lyapunov property of the generalized gap sequence used in deriving our method implies that both FGM and FISTA are amenable to a Lyapunov analysis, common among optimization algorithms. We further showcase the flexibility of our tools by endowing our method with monotonically decreasing objective function values alongside a versatile line-search procedure. By simultaneously incorporating the strengths of FGM and FISTA, our method is able to surpass both in terms of robustness and usability. We support our findings with simulation results on an extensive benchmark of composite problems. Our experiments show that monotonicity has a stabilizing effect on convergence and challenge the notion present in the literature that, for strongly convex objectives, accelerated proximal schemes can be reduced to fixed momentum methods.