SEMKIS-DSL: A Domain-Specific Language to Support Requirements Engineering of Datasets and Neural Network Recognition

Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and tools to satisfy customers’ expectations. The lack of tools that support requirements specification languages makes it difficult for engineers to describe dataset and neural network recognition skill requirements. Existing approaches often rely on traditional ad hoc approaches, without precise requirement specifications for data selection criteria, to build these datasets. Moreover, these approaches do not focus on the requirements of the neural network’s expected recognition skills. We aim to overcome this issue by defining a domain-specific language that precisely specifies dataset requirements and expected recognition skills after training for an NN-based system. In this paper, we present a textual domain-specific language (DSL) called SEMKIS-DSL (Software Engineering Methodology for the Knowledge management of Intelligent Systems) that is designed to support software engineers in specifying the requirements and recognition skills of neural networks. This DSL is proposed in the context of our general SEMKIS development process for neural network engineering. We illustrate the DSL’s concepts using a running example that focuses on the recognition of handwritten digits. We show some requirements and recognition skills specifications and demonstrate how our DSL improves neural network recognition skills

Clinical characteristics and outcomes of patients with acute myelogenous leukemia admitted to intensive care: a case-control study

<p>Abstract</p> <p>Background</p> <p>There is limited epidemiologic data on patients with acute myelogenous (myeloid) leukemia (AML) requiring life-sustaining therapies in the intensive care unit (ICU). Our objectives were to describe the clinical characteristics and outcomes in critically ill AML patients.</p> <p>Methods</p> <p>This was a retrospective case-control study. Cases were defined as adult patients with a primary diagnosis of AML admitted to ICU at the University of Alberta Hospital between January 1^st2002 and June 30^th2008. Each case was matched by age, sex, and illness severity (ICU only) to two control groups: hospitalized AML controls, and non-AML ICU controls. Data were extracted on demographics, course of hospitalization, and clinical outcomes.</p> <p>Results</p> <p>In total, 45 AML patients with available data were admitted to ICU. Mean (SD) age was 54.8 (13.1) years and 28.9% were female. Primary diagnoses were sepsis (32.6%) and respiratory failure (37.3%). Mean (SD) APACHE II score was 30.3 (10.3), SOFA score 12.6 (4.0) with 62.2% receiving mechanical ventilation, 55.6% vasoactive therapy, and 26.7% renal replacement therapy. Crude in-hospital, 90-day and 1-year mortality was 44.4%, 51.1% and 71.1%, respectively. AML cases had significantly higher adjusted-hazards of death (HR 2.23; 95% CI, 1.38-3.60, p = 0.001) compared to both non-AML ICU controls (HR 1.69; 95% CI, 1.11-2.58, p = 0.02) and hospitalized AML controls (OR 1.0, reference variable). Factors associated with ICU mortality by univariate analysis included older age, AML subtype, higher baseline SOFA score, no change or an increase in early SOFA score, shock, vasoactive therapy and mechanical ventilation. Active chemotherapy in ICU was associated with lower mortality.</p> <p>Conclusions</p> <p>AML patients may represent a minority of all critically ill admissions; however, are not uncommonly supported in ICU. These AML patients are characterized by high illness severity, multi-organ dysfunction, and high treatment intensity and have a higher risk of death when compared with matched hospitalized AML or non-AML ICU controls. The absence of early improvement in organ failure may be a useful predictor for mortality for AML patients admitted to ICU.</p

Transcriptomic responses of mixed cultures of ascomycete fungi to lignocellulose using dual RNA-seq reveal inter-species antagonism and limited beneficial effects on CAZyme expression

Gaining new knowledge through fungal monoculture responses to lignocellulose is a widely used approach that can lead to better cocktails for lignocellulose saccharification (the enzymatic release of sugars which are subsequently used to make biofuels). However, responses in lignocellulose mixed cultures are rarely studied in the same detail even though in nature fungi often degrade lignocellulose as mixed communities. Using a dual RNA-seq approach, we describe the first study of the transcriptional responses of wild-type strains of Aspergillus niger, Trichoderma reesei and Penicillium chrysogenum in two and three mixed species shake-flask cultures with wheat straw. Based on quantification of species-specific rRNA, a set of conditions was identified where mixed cultures could be sampled so as to obtain sufficient RNA-seq reads for analysis from each species. The number of differentially-expressed genes varied from a couple of thousand to fewer than one hundred. The proportion of carbohydrate active enzyme (CAZy) encoding transcripts was lower in the majority of the mixed cultures compared to the respective straw monocultures. A small subset of P. chrysogenum CAZy genes showed five to ten-fold significantly increased transcript abundance in a two-species mixed culture with T. reesei. However, a substantial number of T. reesei CAZy transcripts showed reduced abundance in mixed cultures. The highly induced genes in mixed cultures indicated that fungal antagonism was a major part of the mixed cultures. In line with this, secondary metabolite producing gene clusters showed increased transcript abundance in mixed cultures and also mixed cultures with T. reesei led to a decrease in the mycelial biomass of A. niger. Significantly higher monomeric sugar release from straw was only measured using a minority of the mixed culture filtrates and there was no overall improvement. This study demonstrates fungal interaction with changes in transcripts, enzyme activities and biomass in the mixed cultures and whilst there were minor beneficial effects for CAZy transcripts and activities, the competitive interaction between T. reesei and the other fungi was the most prominent feature of this study

Dipstick Test for Rapid Diagnosis of Shigella dysenteriae 1 in Bacterial Cultures and Its Potential Use on Stool Samples

International audienceBACKGROUND: We describe a test for rapid detection of S. dysenteriae 1 in bacterial cultures and in stools, at the bedside of patients. METHODOLOGY/PRINCIPAL FINDINGS: The test is based on the detection of S. dysenteriae 1 lipopolysaccharide (LPS) using serotype 1-specific monoclonal antibodies coupled to gold particles and displayed on a one-step immunochromatographic dipstick. A concentration as low as 15 ng/ml of LPS was detected in distilled water and in reconstituted stools in 10 minutes. In distilled water and in reconstituted stools, an unequivocal positive reaction was obtained with 1.6×10⁶ CFU/ml and 4.9×10⁶ CFU/ml of S. dysenteriae 1, respectively. Optimal conditions to read the test have been determined to limit the risk of ambiguous results due to appearance of a faint yellow test band in some negative samples. The specificity was 100% when tested with a battery of Shigella and unrelated strains in culture. When tested on 328 clinical samples in India, Vietnam, Senegal and France by laboratory technicians and in Democratic Republic of Congo by a field technician, the specificity (312/316) was 98.7% (95% CI:96.6-99.6%) and the sensitivity (11/12) was 91.7% (95% CI:59.8-99.6%). Stool cultures and the immunochromatographic test showed concordant results in 98.4 % of cases (323/328) in comparative studies. Positive and negative predictive values were 73.3% (95% CI:44.8-91.1%) and 99.7% (95% CI:98-100%). CONCLUSION: The initial findings presented here for a simple dipstick-based test to diagnose S. dysenteriae 1 demonstrates its promising potential to become a powerful tool for case management and epidemiological surveys

SEMKIS-DSL: a Domain-Specific Language for Specifying Neural Networks’ Key-Properties

Neural networks are becoming increasingly part of today’s software systems. These neural networks are simplified models of the human brain that are mainly capable of learning from large datasets to compute some function based on recognized data. Engineering these datasets and these neural network-based software systems is a complicated and challenging task. Software engineers require methods and tools to engineer these datasets and neural networks for their customers and to satisfy their requirements. In general, they lack methods and tools to support the engineering of dataset and neural networks that satisfy the customer’s requirements. They follow traditional approaches consisting of time-consuming, imprecise and manual activities. Typically, these approaches are not supported by any tool that precisely analyse and specify the neural network’s recognition skills. In our previous work, we have introduced the notion of key-properties for describing the neural network’s recognition skills. In this paper, we define a domain-specific language to support our SEMKIS software engineering methodology for the dataset augmentation to improve network’s key-properties. We present the SEMKIS-DSL for the specification of the key-properties of a neural network. We illustrate the concepts of our DSL with a running example specifying a neural network for recognizing a digital meter counter state. This running example demonstrates a specification of the neural network’s key-properties using the SEMKIS-DSL and a successful improvement of the neural network’s recognition skills

Formal Verification of Ecosystem Restoration Requirements Using UML and Alloy

United Nations have declared the current decade (2021-2030) as the ”UN Decade on Ecosystem Restoration” to join R&D forces to fight against the ongoing environmental crisis. Given the ongoing degradation of earth ecosystems and the related crucial services that they offer to the human society, ecosystem restoration has become a major society-critical issue. It is required to develop rigorously software applications managing ecosystem restoration. Reliable models of ecosystems and restoration goals are necessary. This paper proposes a rigorous approach for ecosystem requirements modeling using formal methods from a model-driven software engineering point of view. The authors describe the main concepts at stake with a metamodel in UML and introduce a formalization of this metamodel in Alloy. The formal model is executed with Alloy Analyzer, and safety and liveness properties are checked against it. This approach helps ensuring that ecosystem specifications are reliable and that the specified ecosystem meets the desired restoration goals, seen in our approach as liveness and safety properties. The concepts and activities of the approach are illustrated with CRESTO, a real-world running example of a restored Costa Rican ecosystem

Specifying key-properties to improve the recognition skills of neural networks

Software engineers are increasingly asked to build datasets for engineering neural network-based software systems. These datasets are used to train neural networks to recognise data. Traditionally, data scientists build datasets consisting of random collected or generated data. Their approaches are often costly, inefficient and time-consuming. Software engineers rely on these traditional approaches that do not support precise data selection criteria based on customer’s requirements. In this paper, we introduce an extended software engineering method for dataset augmentation to improve neural networks by satisfying the customer’s requirements. We introduce the notion of key-properties to describe the neural network’s recognition skills. Key-properties are used all along the engineering process for developing the neural network in cooperation with the customer. We propose a rigorous process for augmenting datasets based on the analysis and specification of the key-properties. We conducted an experimentation on a case study on the recognition of the state of a digital meter counter. We demonstrate an informal specification of the neural network’s key-properties and a successful improvement of a neural network’s recognition of the meter counter state

An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Alloy and UML

Since the emergence of deep learning (DL) a decade ago, only few software engineering development methods have been defined for systems based on this machine learning approach. Moreover, rare are the DL approaches addressing specifically requirements engineering. In this paper, we define a model-driven engineering (MDE) method based on traditional requirements engineering to improve datasets requirements engineering. Our MDE method is composed of a process supported by tools to aid customers and analysts in eliciting, specifying and validating dataset structural requirements for DL-based systems. Our model driven engineering approach uses the UML semi-formal modeling language for the analysis of datasets structural requirements, and the Alloy formal language for the requirements model execution based on our informal translational semantics. The model executions results are then presented to the customer for improving the dataset validation activity. Our approach aims at validating DL-based dataset structural requirements by modeling and instantiating their datatypes. We illustrate our approach with a case study on the requirements engineering of the structure of a dataset for classification of five-segments digits images