Miracle-dz/UniFAR-FTUs huggingface dataset:
Process Mining on Event Graphs: a Framework to Extensively Support Projects

Most business processes are supported nowadays by information systems, that record event data about the executions of the processes. Process mining plays an important role in linking the BPM field with data science, helping to identify the bottlenecks and the unwanted behavior, and to adopt strategies to improve the process, measuring the eventual benefit. While many algorithmic techniques have been developed for discovery, conformance checking and other process mining techniques, extracting data from today’s information systems requires the specification of a complex query that extracts the required information and groups the events in cases. The research project described in this paper proposes a novel framework to support process mining analysis, that uses the advances in graph algorithms and in-memory data processing in order to reduce the costs of extraction and transformation of the event data contained in the information systems. At the end of the project, a set of pre-processing, discovery and conformance checking techniques, that do not require the specification of a case notion, will be made available in different environments, technologies and languages, e.g., PM4Py, Spark, Neo4J, Celonis. In comparison to related work, this research aims to obtain a complete and scalable framework that supports process mining from the Extraction, Transformation and Load phase (from relational and non-relational databases) to the effective analysis/usage of the data, and to get a class of process models fully capturing the lifecycle and the interactions between different classes. Since the framework is aimed at real-life, complex, information systems, a goal of the project is to attain significantly better scalability than existing approaches.
Hadoken: a BERT-CRF Model for Medical Document Anonymization

In this paper we report our participation in the 2019 MEDDOCAN task where participants were provided with a synthetic corpus of clinical cases and required to de-identify the protected health information (PHI) in the corpus. Our system Hadoken utilizes BERT, a multi-layer bidirectional transformer encoder which can help learn deep bi-directional representations, and fine-tunes the pre-trained BERT model on training data for MEDDOCAN task. It then feeds the representation of a clinical case into a CRF output layer for token-level classification. To make the results more accurate, we apply some post-processing techniques based on a thorough error analysis. Through utilizing linguistic resources, we were able to achieve results of a higher recall. Our best F-Scores on the test set for Task1 and Task2 are 0.9375, 0.9428 [strict], and 0.9480 [merged] respectively. We find that Hadoken is a robust and competitive system and is applicable to multilingual NER tasks.
ELiRF-UPV at TASS 2019: Transformer Encoders for Twitter Sentiment Analysis in Spanish

This paper describes the participation of the ELiRF research group of the Universitat Politècnica de València in the TASS 2019 Workshop, framed within the XXXV edition of the International Congress of the Spanish Society for the Processing of Natural Language. We present the approach used for the Monolingual InterTASS task of the workshop, as well as the results obtained and a discussion of them. Our participation has focused mainly on employing the encoders of the Transformer model, based on self-attention mechanisms, achieving competitive results in the task addressed.
Petri Meta-Compiler - A Recursive Approach to System Design and Development

We propose a framework for the definition of Petri Net models in the Petri net approach. All the components: places, transitions, arcs are defined as Petri net objects. Assembled together they constitute a method to design business systems compositionally. Transitions are associated with a code, places contain data or are control places. Each entity (process) has a place, which serves as an interface to the external world (other processes). The approach is implemented in such a way that it is possible to compile the codes of the designed systems within one framework (the same in which the model is defined). The approach enjoys the bootstrap compilation, so the system can compile itself. We have developed a method to make it in 3 phases. The first phase defines objects, the second one makes the net structure and the third one generates the code of the system itself. The framework has been implemented and deployed in a few small and medium size internal business database projects. It provided great predictability of impact of changes and added flexibility to the system development. Characteristics of the resulting database structure with its immutable history of transactions seem compatible with blockchain concepts, thus we expect that such Petri Nets could become a convenient language for smart contracts.
A Methodology for Operationalizing Enterprise IT Architecture and Evaluating its Modifiability

Recent contributions to information systems theory suggest that the primary role of a firm’s information technology (IT) architecture is to facilitate, and therefore ensure the continued alignment of a firm’s IT investments with a constantly changing business environment. Despite the advances we lack robust methods with which to operationalize enterprise IT architecture, in a way that allows us to analyze performance, in terms of the ability to adapt and evolve over time. We develop a methodology for analyzing enterprise IT architecture based on “Design Structure Matrices” (DSMs), which capture the coupling between all components in the architecture. Our method addresses the limitations of prior work, in that it i) captures the architecture “in-use” as opposed to high level plans or conceptual models; ii) identifies discrete layers in the architecture associated with different technologies; iii) reveals the “flow of control” within the architecture; and iv) generates measures that can be used to analyze performance. We apply our methodology to a dataset from a large pharmaceutical firm. We show that measures of coupling derived from an IT architecture DSM predict IT modifiability – defined as the cost to change software applications. Specifically, applications that are tightly coupled cost significantly more to change.
Compliance to Data Protection and Purpose Control Using Process Mining Technique

The business processes of an organisation are executed in certain boundaries. Some of the restrictions are raised from the environment of the organisations such as regulatory and supervisory constraints. One of the regulations that is imposed on organisations is the European General Data Protection Regulation (GDPR). The most important aspect of the GDPR rules is how organisations handle personal data of their customers. In this research, we focus on this aspect of the GDPR. Our goal is to develop a solution that enables organisations to deal with the challenges of becoming compliant with GDPR. We plan to use and improve process mining techniques to tackle the problems such as discovering data-flow and control-flow of business processes that have interaction with personal data of customers. Our approach consists of four phases: (1) discover process model based on purpose, (2) translate regulatory rules to technical rules, (3) develop privacy policy model base on the GDPR, (4) conformance analysis.
Integrated, Ubiquitous and Collaborative Process Mining with Chat Bots

Within the process mining ﬁeld we are witnessing a tremen-dous growth of applications and development frameworks available to perform data analyses. Such growth, which is very positive and desir-able, comes with the cost of learning each new tool and diﬃculties in integrating diﬀerent systems in order to complement the analyses. In addition, we are noticing the lack of tools enabling collaboration among the users involved in a project. Finally, we think it would be highly rec-ommended to enable ubiquitous processing of data. This paper proposes a solution to all these issues by presenting a chat bot which can be included in discussions to enable the execution of process mining directly from the chat.
OFDMA-Enabled Wi-Fi Backscatter

In this paper, we for the first time demonstrate how to enable OFDMA in Wi-Fi backscatter for capacity and concurrency enhancement. With our approach, the excitation signal is reflected, modulated and shifted to lie in the frequency band of the OFDM subcarrier by the tag; OFDMA is realized by coordinating tags to convey information to the receiver with orthogonal subcarriers concurrently through backscatter. The crux of the design is to achieve strict synchronization among communication components, which is more challenging than in regular OFDMA systems due to the more prominent hardware diversity and uncertainty for backscattering. We reveal how the subtle asychnronization scenarios particularly for backscattering can incur system offsets, and present a series of novel designs for the excitation signal transmitter, tag, and receiver to address the issue. We build a prototype in 802.11g OFDM framework to validate our design. Experimental results show that our system can achieve 5.2-16Mbps aggregate throughput by allowing 48 tags to transmit concurrently, which is 1.45-5x capacity and 48x concurrency compared with the existing design respectively. We also design an OFDMA tag IC, and the simulation and numerical analysis results show that the tag's power consumption is in tens of μW.
UACh at MEX-A3T 2019: Preliminary Results on Detecting Aggressive Tweets by Adding Author Information Via an Unsupervised Strategy

In this paper we describe our participation for the Aggres-siveness Detection Track in the second edition of MEX-A3T. We evaluate diﬀerent strategies for text classiﬁcation, including classiﬁers such as Support Vector Machines and a Multilayer Perceptron trained on n-grams (words and characters) and word embeddings. We also study the inclusion of features to try to give context to the text messages and explore if people verbally attack diﬀerently depending on their traits and overall environment. Preliminary results show that our strategy is competitive to detect aggression in tweets, ranking in 2nd place with respect to the participants of 2018 and 2019.
INGEOTEC at IberLEF 2019 Task HaHa

This manuscript describes INGEOTEC’s participation in the second Humor Analysis based on Human Annotation (HAHA) task on IberLEF’2019. Our approach to solve the task was based on perform an extensive comparison of several text classifiers using a 80-20 holdout cross-validation methodology. We found that our generic text categorization and regression system (μTC) had the best performance. Finally, we conducted an analysis over the training dataset illustrating some of the task’s complexity.
Overview of the Task on Irony Detection in Spanish Variants

This paper introduces IroSvA, the first shared task fully dedicated to identify the presence of irony in short messages (tweets and news comments) written in three different variants of Spanish. The task consists in: given a message, automatic systems should recognize whether the message is ironic or not. Moreover, with respect to the previous tasks on irony detection, the messages are not considered as isolated texts but together with a given context (e.g. a headline or a topic). The task is comprised by three different subtasks: i) irony detection in tweets from Spain, ii) irony detection in Mexican tweets, and iii) irony detection in news comments from Cuba. These subtasks aim at studying the way irony changes across the distinct Spanish variants. We received 14 submissions from 12 teams. Participating systems were evaluated against the test dataset using F1 macro averaged. The highest classification scores obtained for the three subtasks are F1=0.7167, F1=0.6803, and F1=0.6596, respectively.
The Business Process Management Game

The Business Process Management Game is a serious game that teaches various aspects of business process management. Students can play the game in groups. Acting as the 'management team' of a business unit, they have to design a business process for that unit that is optimal in terms of cost, customer satisfaction, and waiting and service times. Groups compete with each other to create the process that performs best. In doing so, they can practice their business process modeling, analysis, re-design, and mining skills. The game got much positive feedback from students in offcial student evaluations of a course in which it is used.
Evaluation of in vitro and in vivo immunostimulatory activities of poly (lactic-co-glycolic acid) nanoparticles loaded with soluble and autoclaved Leishmania infantum antigens: A novel vaccine candidate against visceral leishmaniasis

Objective: To prepare and characterize poly lactic-co-glycolic acid (PLGA) nanoparticles loaded with soluble leishmanial antigen or autoclaved leishmanial antigen and explore in vitro and in vivo immunogenicity of antigen encapsulated nanoparticles. Methods: Water/oil/water double emulsion technique was employed to synthesize PLGA nanoparticles, and scanning electron microscopy, Fourier transform infrared spectroscopy and Zeta-potential measurements were used to identify the characteristics of nanoparticles. Cytotoxicity of synthetized nanoparticles on J774 macrophage were investigated by MTT assays. To determine the in vitro immunostimulatory efficacies of nanoparticles, griess reaction and ELISA was used to measure the amounts of NO and cytokines. During the in vivo analysis, Balb/c mice were immunized with vaccine formulations, and protective properties of nanoparticles were measured by Leishman Donovan unit in the liver following the infection. Cytokine levels in spleens of mice were determined by ELISA. Results: MTT assay showed that neither soluble leishmanial antigen nor autoclaved leishmanial antigen encapsulated nanoparticles showed cytotoxicity against J774 macrophage cells. Contrary to free antigens, both autoclaved leishmanial antigen-nanoparticle and soluble leishmanial antigen-nanoparticle formulations led to a 10 and 16-fold increase in NO amounts by macrophages, respectively. Leishman Donovan unit calculations revealed that soluble leishmanial antigen-nanoparticles and autoclaved leishmanial antigen-nanoparticles yielded 52% and 64% protection against visceral leishmaniasis in mouse models. Besides, in vitro and in vivo tests demonstrated that by increasing IFN-γ and IL-12 levels and inhibiting IL-4 and IL-10 secretions, autoclaved leishmanial antigen-nanoparticles and soluble leishmanial antigennanoparticles triggered Th1 immune response. Conclusions: Both autoclaved leishmanial antigen-nanoparticles and soluble leishmanial antigen-nanoparticles formulations provide exceptional in vitro and in vivo immunostimulatory activities. Hence, PLGA-based antigen delivery systems are recommended as potential vaccine candidates against visceral leishmaniasis.
Vol23#1_Incidental vs. Instructional Approaches to Increasing Reading Vocabulary

Our concern is with the effectiveness of different approaches to vocabulary, relative to two major, related educational goals: Increasing the overall size of students' reading vocabulary and increasing students' ability to comprehend text. Educators and basal publishers rightly recognize the importance of vocabulary knowledge in reading and, therefore, include some form of vocabulary instruction in most current reading programs. Our thesis, however, is that explicit vocabulary instruction, even at its best, is not very effective at producing substantial gains in overall vocabulary size or in reading comprehension. Major progress towards these goals can only be attained by increasing incidental vocabulary learning. In the first section of this article we consider the size of the task: that is, the number of words students would have to team to make any substantial gains in overall vocabulary size. We feel that the size of the task is almost universally underestimated, or else simply not taken into account. Teaching the meanings of individual words may be effective for a specific reading lesson but it cannot result in any substantial increase in overall vocabulary size. In the short run, incidental teaming looks ineffective compared to almost any other instructional approach to vocabulary growth. But when the size of the task is accurately assessed, it is seen that the bulk of children's vocabulary growth necessarily comes through incidental learning. We will argue that regular and sustained reading can lead to substantial gains in vocabulary size. In the second section we will consider the nature of the task: that is, the type of word knowledge that is necessary to facilitate reading comprehension. Evidence from a number of studies1 shows that reliable gains in reading comprehension can be produced through instruction of words from a given passage only if the instruction provides multiple encounters that supply a variety of information about the instructed words. Since vocabulary instruction can supply multiple, rich encounters for only a small number of words (or a small number of encounters for a slightly larger number of words), students must have additional opportunities to encounter large numbers of words repeatedly.
The mTOR Signal Pathway Is Overactivated in Human Lymphatic Malformations.

Background: Lymphatic malformations (LMs) are congenital low-flow vascular anomalies resulting from abnormal embryogenesis. Clinical researches have shown that rapamycin, a specific inhibitor of mTOR, is effective in treating LMs. It suggests the abnormality of mTOR signal pathway in LMs. Methods and Results: From January 2009 to December 2018, 10 patients who accepted the resection of LMs were enrolled into the study. Samples of each subtype of LMs (macrocystic, microcystic, and mixed subtypes) were further investigated. Expression of molecules in mTOR signal pathway-mTORC1, p70 S6, p-p70 S6, elF4EBP1, and p-elF4EBP1-in LMs were investigated by immunohistochemical staining. Location of mTORC1, p70 S6, and elF4EBP1 in LMs were shown by immunofluorescence co-staining. Phosphorylation level of mTOR signal pathway in LMs was examined by Western blotting. Immunohistochemical staining showed the expression of mTORC1, p70 S6, p-p70 S6, eIF4EBP1, and p-eIF4EBP1 in LMs. Immunofluorescence staining further verified the co-expression of mTORC1, p70 S6, and eIF4EBP1 in the lymphatic endothelium of LMs. Western blotting analysis revealed obviously higher phosphorylation level of mTOR signal pathway in LMs than that in normal skins (P < 0.05). Conclusions: The results showed that the mTOR signal pathway was overactivated in LMs. The study provides compelling evidence for treating LMs or syndromes with lymphatic anomalies by inhibiting mTOR signaling.
Effect of Preheating Treatment before Defatting on the Flavor Quality of Skim Milk

Skim milk has a poor flavor due to the lack of fat. Finding ways to improve the flavor quality of skim milk has attracted the attention of more and more researchers. The purpose of this study was to create a skim milk product with good flavor by processing. Briefly, raw milk was treated by preheating at pasteurization (85 °C, 15 s) and ultra-high temperature (UHT) instantaneous sterilization (137–141 °C, 4 s). Subsequently, the sample was centrifuged to remove fat and obtain two kinds of skim milk, namely, PSM (skim milk obtained by preheating at 85 °C, 15 s) and USM (skim milk obtained by preheating at 137–141 °C, 4 s). The results showed that the intensity of the main sensory attributes (overall liking, milk aroma, etc.) and the concentrations of the key flavor compounds (2-heptanone, 2-nonanone, decanal, hexanoic acid, etc.) were significantly higher in the USM (p < 0.05) than that of the PSM and RSM (skim milk without preheating). Principal component analysis (PCA) with E-Nose (electronic nose) showed that the RSM had significant differences in the milk aroma compared with the PSM and USM. Furthermore, it was found that there were good relationships between volatile compounds and sensory attributes by partial least squares regression (PLSR) analysis. These findings provided insights into improving the flavor quality of skim milk by preheating treatment instead of any flavor additives.
Diet Quality Is Associated With Mortality in Adults Aged 80 Years and Older: A Prospective Study

Diet quality has been associated with health outcomes and quality of life. However, the association between diet quality and mortality in older people, those aged 80 years and older, is understudied. Therefore, we conducted a prospective study to examine whether better diet quality, assessed by a validated dietary screening tool (DST), was associated with lower mortality in those aged 80 years and older.
Molecular Mechanisms Associated with ROR1-Mediated Drug Resistance: Crosstalk with Hippo-YAP/TAZ and BMI-1 Pathways

Signaling via the Wnt-related receptor tyrosine kinase-like orphan receptor 1 (ROR1) triggers tumorigenic features associated with cancer stem cells (CSCs) and epithelial–mesenchymal transition (EMT), while aberrant expression of ROR1 is strongly linked to advanced disease progression and chemoresistance. Several recent studies have shown that Wnt5a binding to ROR1 promotes oncogenic signaling by activating multiple pathways such as RhoA/Rac1 GTPases and PI3K/AKT, which in turn could induce transcriptional coactivator YAP/TAZ or polycomb complex protein BMI-1 signaling, respectively, to sustain stemness, metastasis and ultimately drug-resistance. These data point towards a new feedback loop during cancer development, linking Wnt5a-ROR1 signaling activation to YAP/TAZ or BMI-1 upregulation that could play an important role in disease progression and treatment resistance. This review focuses on the crosstalk between Wnt5a-ROR1 and YAP/TAZ or the BMI-1 signaling network, together with the current advancements in targeted strategies for ROR1-positive cancers.
Assessment of Clinicians' Views for Managing Children with Obesity in the Primary, Secondary, and Tertiary Settings.

Background: The aim of this study was to examine the perceptions of the assessment and management of children with obesity of primary, secondary, and tertiary care clinicians across two health districts in western Sydney and a specialty children's health network. Methods: Participants were 304 clinicians (medical, nursing, and allied health workers) in primary, secondary, and tertiary pediatric-level services. A questionnaire captured the training, assessment, and management approaches and perceived barriers to managing pediatric patients with obesity. Chi-squared tests and logistic regressions examined the differences in responses between clinicians. Results: Clinicians across all levels of health care had only moderate rates of training in obesity (48%), did not routinely measure tandem heights and weights (80%), and infrequently referred children to other services. Only 25% of clinicians frequently referred children to a weight management service (most frequently the dietitian). When comparing across health care settings, those in secondary-level services had higher rates of training (70%) and more frequently initiated treatment for obesity. Conclusion: Frequencies of routine identification and initiation of treatment for children with obesity are low among health professionals across health care settings, with some exceptions for secondary care clinicians. Greater and more intensive health professional training on the assessment and management of children with obesity is needed in Australia and may be a key factor in increasing health care for this common chronic condition.