Association Rule Mining Meets Regression Analysis: An Automated Approach to Unveil Systematic Biases in Decision-Making Processes

Decisional processes are at the basis of most businesses in several application domains. However, they are often not fully transparent and can be affected by human or algorithmic biases that may lead to systematically incorrect or unfair outcomes. In this work, we propose an approach for unveiling biases in decisional processes, which leverages association rule mining for systematic hypothesis generation and regression analysis for model selection and recommendation extraction. In particular, we use rule mining to elicit candidate hypotheses of bias from the observational data of the process. From these hypotheses, we build regression models to determine the impact of variables on the process outcome. We show how the coefficient of the (selected) model can be used to extract recommendation, upon which the decision maker can operate. We evaluated our approach using both synthetic and real-life datasets in the context of discrimination discovery. The results show that our approach provides more reliable evidence compared to the one obtained using rule mining alone, and how the obtained recommendations can be used to guide analysts in the investigation of biases affecting the decisional process at hand.</p

Encoding High-Level Control-Flow Construct Information for Process Outcome Prediction

Outcome-oriented predictive process monitoring aims at classifying a running process execution according to a given set of categorical outcomes, leveraging data on past process executions. Most previous studies employ Recurrent Neural Networks to encode the sequence of events, without taking the structure of the process into account. However, process executions typically involve complex control-flow constructs, like parallelism and loops. Different executions of these constructs can be recorded as different event sequences in the event log. This makes it challenging for a recurrent classifier to detect potential relations between a high-level control-flow construct and the prediction target. This is especially true in the presence of high variability in process executions and lack of data. In this paper, we propose a novel approach which encodes the control-flow construct each event belongs to. First, we exploit Local Process Model mining techniques to extract frequently occurring control-flow patterns from the event log. Then, we employ different encoding techniques to enrich an on-going process execution with information related to the extracted control-flow patterns. We tested the proposed method on nine real-life event logs. The obtained results show consistent improvements in the prediction performance

Understanding the stumbling blocks of Italian higher education system:A process mining approach

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students’ careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students’ failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students’ performance. These techniques allow delving into students’ careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students’ graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula.</p

Isolation of Methicillin-Resistant Coagulase-Negative Staphylococcus (MRCoNS) from a fecal-contaminated stream in the Shenandoah Valley of Virginia

Staphylococcus is comprised of 41 known species, of which 18 can colonize humans. Despite the prevalence of infectious Staphylococcus within hospital settings and agriculture, there are few reports of Staphylococcus in natural bodies of water. A recent study by the US Food and Drug Administration found substantial contamination of poultry and other meats with Staphylococcus. We hypothesized that intensive farming of poultry adjacent to streams would result in contaminated runoff, resulting in at least transient occurrence of Staphylococcus spp. in stream waters and sediments. In this study, we sought to determine whether Staphylococcus occurs and persists within Muddy Creek, a stream located in Hinton, Virginia that originates at the Appalachian Mountains of Virginia and runs through various agricultural fields and adjacent to a poultry processing plant in the central Shenandoah Valley. Five different Staphylococcus spp. were detected in water and sediment from Muddy Creek. Mannitol Salt Agar (MSA) was used to isolate eleven Staphylococcus from both water and sediment. These isolates were Gram-positive, catalase-positive, and oxidase-negative cocci that were capable of fermenting mannitol. In addition, a method for screening putative staphylococci species from stream water and sediment was developed. Ten out of the eleven tested isolates were oxacillin resistant (now used to identify phenotypic methicillin-resistance) using a Kirby Bauer disc diffusion test. Furthermore, the isolates were susceptible to trimethoprim/sulfamethoxazole, tetracycline, and gentamicin while two of the isolates were resistant to erythromycin. Additionally, the BOX-PCR repetitive sequence fingerprinting method verified the presence of nine different strains among the isolates. Sequencing of the 16S rRNA gene identified five of the isolates as Staphylococcus equorum. The Biolog identification protocol further identified the remaining isolates as Staphylococcus xylosus, Staphylococcus lentus, Staphylococcus succinus, and Staphylococcus sciuri. Finally, polymerase chain reaction amplification (PCR) confirmed that ten of the eleven isolates harbored the mecA gene known to confer methicillin-resistance. Overall, the occurrence of coagulase-negative staphylococci (MRCoNS) in stream water and sediment represents a potential environmental and human health concern

Understanding the stumbling blocks of Italian higher education system:A process mining approach

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students’ careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students’ failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students’ performance. These techniques allow delving into students’ careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students’ graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula.</p

Towards Multi-perspective conformance checking with fuzzy sets

Conformance checking techniques are widely adopted to pinpoint possible discrepancies between process models and the execution of the process in reality. However, state of the art approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process. In this work, we propose a novel approach which allows to represent actors' tolerance with respect to violations and to account for severity of deviations when assessing executions compliance. We argue that besides improving the quality of the provided diagnostics, allowing some tolerance in deviations assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system.Comment: 15 pages, 5 figure

Towards Multi-perspective Conformance Checking with Fuzzy Sets

Nowadays organizations often need to employ data-driven techniques to audit their business processes and ensure they comply with laws and internal/external regulations. Failing in complying with the expected process behavior can indeed pave the way to inefficiencies or, worse, to frauds or abuses. An increasingly popular approach to automatically assess the compliance of the executions of organization processes is represented by alignment-based conformance checking. These techniques are able to compare real process executions with models representing the expected behaviors, providing diagnostics able to pinpoint possible discrepancies. However, the diagnostics generated by state of the art techniques still suffer from some limitations. They perform a crisp evaluation of process compliance, marking process behavior either as compliant or deviant, without taking into account the severity of the identified deviation. This hampers the accuracy of the obtained diagnostics and can lead to misleading results, especially in contexts where there is some tolerance with respect to violations of the process guidelines. In the present work, we discuss the impact and the drawbacks of a crisp deviation assessment approach. Then, we propose a novel conformance checking approach aimed at representing actors’ tolerance with respect to process deviations, taking it into account when assessing the severity of the deviations. As a proof of concept, we performed a set of synthetic experiments to assess the approach. The obtained results point out the potential of the usage of a more flexible evaluation of process deviations, and its impact on the quality and the interpretation of the obtained diagnostics

Towards Augmenting Mental Health Personnel With LLM Technology To Provide More Personalized And Measurable Treatment Goals for Patients with Severe Mental Illness

Mobile health (mHealth) tools are increasingly being used in various mental health domains to monitor patients with Severe Mental Illnesses (SMI), with the aim of potentially increasing patient engagement with their treatment. Patients with SMI who are prescribed Flexible Assertive Community Treatment (FACT) create a treatment plan together with their case manager, which serves as the leading document describing the goals that will be worked on during treatment. In order to incorporate the treatment plan goals of a patient in an mHealth application, the treatment plan goals need to be measurable. However, in previous work, we discovered that on average, only 25% of the available treatment plans include measurable goals. We have developed a protocol for making measurable goals with patients with SMI to address this issue. However, we anticipate low adoption of the protocol due to the potentially time-consuming nature of the steps involved. To mitigate this, we are exploring the use of AI to generate measurable treatment plan goals for patients with SMI and introduce a new workflow. In our exploratory study, we created a prototype of a system that may enable case managers and patients with SMI to generate measurable treatment plan goals using Large Language Models

Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Untuk Pemberian Rekomendasi Pemilihan Sekolah Lanjutan (Studi Kasus Siswa Kelas IX MTs Nurul Anwar)

Pendidikan merupakan bidang yang paling penting dalam perkembangan suatu bangsa. Dalam rangka mewujudkan tujuan dari pendidikan nasional secara optimal maka setiap siswa perlu menempuh jenjang pendidikan formal setidaknya sampai siswa menempuh Sekolah Lanjutan Tingkat Atas (SLTA) Sejalan dengan hal ini maka setamat SLTP setiap siswa kelas IX seharusnya melanjutkan pendidikan ke SLTA (SMK/SMA/MA/). Siswa kelas IX yang menempuh jenjang pendidikan SLTP sudah pasti akan dihadapkan dengan masalah memilih sekolah lanjutan, baik sekolah menengah umum maupun kejuruan. Memilih sekolah lanjutan menjadi faktor penting karena berkaitan dengan masa depan siswa. Salah satu pemodelan yang bisa digunakan untuk menentukan rekomendasi pemilihan sekolah lanjutan yaitu dengan Data Mining.Pemanfaatan teknik data mining diharapkan dapat membantu dalam Menentukan rekomondasi pemilihan sekolah lanjutan. Pada penelitian ini membandingkan teknik klasifikasi dari kinerja metode K-Nearst Neighbor dan Support VectorMachine.Atribut yang digunakan terdiri dari Nilai UNBK, Minat Siswa, dan Saran BK. Dengan menggunakan masing-masing data training dan data testing sebanyak 35 data. Hasil dari penelitian yang dilakukan, berdasarkan dari nilai akurasinya Support Vector Machine lebih tinggi yaitu 97,1% dibandingkan dengan K-Nearst Neighbor yaitu 85,7% .Hasil akhir dari penelitian ini adalah metode Support Vector Machine lebih baik digunakan dari pada metode K-Nearst Neighbor

Allosteric inhibition of a stem cell RNA-binding protein by an intermediary metabolite

Gene expression and metabolism are coupled at numerous levels. Cells must sense and respond to nutrients in their environment, and specialized cells must synthesize metabolic products required for their function. Pluripotent stem cells have the ability to differentiate into a wide variety of specialized cells. How metabolic state contributes to stem cell differentiation is not understood. In this study, we show that RNA-binding by the stem cell translation regulator Musashi-1 (MSI1) is allosterically inhibited by 18-22 carbon omega-9 monounsaturated fatty acids. The fatty acid binds to the N-terminal RNA Recognition Motif (RRM) and induces a conformational change that prevents RNA association. Musashi proteins are critical for development of the brain, blood, and epithelium. We identify stearoyl-CoA desaturase-1 as a MSI1 target, revealing a feedback loop between omega-9 fatty acid biosynthesis and MSI1 activity. We propose that other RRM proteins could act as metabolite sensors to couple gene expression changes to physiological state