Influential factors of aligning Spotify squads in mission-critical and offshore projects – a longitudinal embedded case study

Changing the development process of an organization is one of the toughest and riskiest decisions. This is particularly true if the known experiences and practices of the new considered ways of working are relative and subject to contextual assumptions. Spotify engineering culture is deemed as a new agile software development method which increasingly attracts large-scale organizations. The method relies on several small cross-functional self-organized teams (i.e., squads). The squad autonomy is a key driver in Spotify method, where a squad decides what to do and how to do it. To enable effective squad autonomy, each squad shall be aligned with a mission, strategy, short-term goals and other squads. Since a little known about Spotify method, there is a need to answer the question of: How can organizations work out and maintain the alignment to enable loosely coupled and tightly aligned squads? In this paper, we identify factors to support the alignment that is actually performed in practice but have never been discussed before in terms of Spotify method. We also present Spotify Tailoring by highlighting the modified and newly introduced processes to the method. Our work is based on a longitudinal embedded case study which was conducted in a real-world large-scale offshore software intensive organization that maintains mission-critical systems. According to the confidentiality agreement by the organization in question, we are not allowed to reveal a detailed description of the features of the explored project

ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks

Electrocardiogram (ECG) acquisition requires an automated system and analysis pipeline for understanding specific rhythm irregularities. Deep neural networks have become a popular technique for tracing ECG signals, outperforming human experts. Despite this, convolutional neural networks are susceptible to adversarial examples that can misclassify ECG signals and decrease the model's precision. Moreover, they do not generalize well on the out-of-distribution dataset. The GAN architecture has been employed in recent works to synthesize adversarial ECG signals to increase existing training data. However, they use a disjointed CNN-based classification architecture to detect arrhythmia. Till now, no versatile architecture has been proposed that can detect adversarial examples and classify arrhythmia simultaneously. To alleviate this, we propose a novel Conditional Generative Adversarial Network to simultaneously generate ECG signals for different categories and detect cardiac abnormalities. Moreover, the model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples. Consequently, we compare our architecture and show how it outperforms other classification models in normal/abnormal ECG signal detection by benchmarking real world and adversarial signals.Comment: Accepted to ICMLA 202

Clustering ellipses for anomaly detection

Comparing, clustering and merging ellipsoids are problems that arise in various applications, e.g., anomaly detection in wireless sensor networks and motif-based patterned fabrics. We develop a theory underlying three measures of similarity that can be used to find groups of similar ellipsoids in p-space. Clusters of ellipsoids are suggested by dark blocks along the diagonal of a reordered dissimilarity image (RDI). The RDI is built with the recursive iVAT algorithm using any of the three (dis) similarity measures as input and performs two functions: (i) it is used to visually assess and estimate the number of possible clusters in the data; and (ii) it offers a means for comparing the three similarity measures. Finally, we apply the single linkage and CLODD clustering algorithms to three two-dimensional data sets using each of the three dissimilarity matrices as input. Two data sets are synthetic, and the third is a set of real WSN data that has one known second order node anomaly. We conclude that focal distance is the best measure of elliptical similarity, iVAT images are a reliable basis for estimating cluster structures in sets of ellipsoids, and single linkage can successfully extract the indicated clusters

Barriers to Quality Perioperative Care Delivery in Low- and Middle-Income Countries: A Qualitative Rapid Appraisal Study

BACKGROUND: Provision of timely, safe, and affordable surgical care is an essential component of any high-quality health system. Increasingly, it is recognized that poor quality of care in the perioperative period (before, during, and after surgery) may contribute to significant excess mortality and morbidity. Therefore, improving access to surgical procedures alone will not address the disparities in surgical outcomes globally until the quality of perioperative care is addressed. We aimed to identify key barriers to quality perioperative care delivery for 3 “Bellwether” procedures (cesarean delivery, emergency laparotomy, and long-bone fracture fixation) in 5 low- and middle-income countries (LMICs). METHODS: Ten hospitals representing secondary and tertiary facilities from 5 LMICs were purposefully selected: 2 upper-middle income (Colombia and South Africa); 2 lower-middle income (Sri Lanka and Tanzania); and 1 lower income (Uganda). We used a rapid appraisal design (pathway mapping, ethnography, and interviews) to map out and explore the complexities of the perioperative pathway and care delivery for the Bellwether procedures. The framework approach was used for data analysis, with triangulation across different data sources to identify barriers in the country and pattern matching to identify common barriers across the 5 LMICs. RESULTS: We developed 25 pathway maps, undertook >30 periods of observation, and held >40 interviews with patients and clinical staff. Although the extent and impact of the barriers varied across the LMIC settings, 4 key common barriers to safe and effective perioperative care were identified: (1) the fragmented nature of the care pathways, (2) the limited human and structural resources available for the provision of care, (3) the direct and indirect costs of care for patients (even in health systems for which care is ostensibly free of charge), and (4) patients’ low expectations of care. CONCLUSIONS: We identified key barriers to effective perioperative care in LMICs. Addressing these barriers is important if LMIC health systems are to provide safe, timely, and affordable provision of the Bellwether procedures