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1. Automated Microservice Code-Smell Detection

   https://doi.org/10.1007/978-981-33-6385-4_20  

   Walker, A.; Das, D.; Cerny, T. (April 2021, Information Science and Applications. Lecture Notes in Electrical Engineering)

   null; null; null (Ed.)

   Microservice Architecture (MSA) is rapidly taking over modern software engineering and becoming the predominant architecture of new cloud-based applications (apps). There are many advantages to using MSA, but there are many downsides to using a more complex architecture than a typical monolithic enterprise app. Beyond the normal bad coding practices and code-smells of a typical app, MSA specific code-smells are difficult to discover within a distributed app. There are many static code analysis tools for monolithic apps, but no tool exists to offer code-smell detection for MSA-based apps. This paper proposes a new approach to detect code smells in distributed apps based on MSA. We develop an open-source tool, MSANose, which can accurately detect up to eleven different types of MSA specific code smells. We demonstrate our tool through a case study on a benchmark MSA app and verify its accuracy. Our results show that it is possible to detect code-smells within MSA apps using bytecode and or source code analysis throughout the development or before deployment to production. 

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2. Predictions, Role of Interventions and Effects of a Historic National Lockdown in India's Response to the the COVID-19 Pandemic: Data Science Call to Arms

   https://doi.org/10.1162/99608f92.60e08ed5  

   Ghosh, Parikshit; Baladandayuthapani, Veerabhadran; Banerjee, Mousumi; Mukherjee, Bhramar; Ray, Debashree; Salvatore, Maxwell; Bhattacharyya, Rupam; Wang, Lili; Du, Jiacong; Mohammed, Shariq; et al (June 2020, Harvard Data Science Review)

   With only 536 COVID-19 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25, 2020. The lockdown was first extended to May 3 soon after the analysis of this article was completed, and then to May 18 while this article was being revised. In this article, we use a Bayesian extension of the susceptible-infected-removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other, less severe nonpharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its health care and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown from 42–56 days is preferable to substantially ‘flatten the curve’ when compared to 21–28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and thus inform policymakers about one aspect of this multifaceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods, and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org. 

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3. On assessing control actions for epidemic models on temporal networks

   https://doi.org/10.1109/LCSYS.2020.2993104  

   Zino, Lorenzo; Rizzo, Alessandro; Porfiri, Maurizio (October 2020, IEEE Control Systems Letters)

   In this letter, we propose an epidemic model over temporal networks that explicitly encapsulates two different control actions. We develop our model within the theoretical framework of activity driven networks (ADNs), which have emerged as a valuable tool to capture the complexity of dynamical processes on networks, coevolving at a comparable time scale to the temporal network formation. Specifically, we complement a susceptible–infected–susceptible epidemic model with features that are typical of nonpharmaceutical interventions in public health policies: i) actions to promote awareness, which induce people to adopt self-protective behaviors, and ii) confinement policies to reduce the social activity of infected individuals. In the thermodynamic limit of large-scale populations, we use a mean-field approach to analytically derive the epidemic threshold, which offers viable insight to devise containment actions at the early stages of the outbreak. Through the proposed model, it is possible to devise an optimal epidemic control policy as the combination of the two strategies, arising from the solution of an optimization problem. Finally, the analytical computation of the epidemic prevalence in endemic diseases on homogeneous ADNs is used to optimally calibrate control actions toward mitigating an endemic disease. Simulations are provided to support our theoretical results. 

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4. Pedigree‐based assessment of recent population connectivity in a threatened rattlesnake

   https://doi.org/10.1111/1755-0998.13383  

   Martin, Scott A.; Lipps, Jr., Gregory J.; Gibbs, H. Lisle (March 2021, Molecular Ecology Resources)

   Abstract Managing endangered species in fragmented landscapes requires estimating dispersal rates between populations over contemporary timescales. Here, we developed a new method for quantifying recent dispersal using genetic pedigree data for close and distant kin. Specifically, we describe an approach that infers missing shared ancestors between pairs of kin in habitat patches across a fragmented landscape. We then applied a stepping‐stone model to assign unsampled individuals in the pedigree to probable locations based on minimizing the number of movements required to produce the observed locations in sampled kin pairs. Finally, we used all pairs of reconstructed parent‐offspring sets to estimate dispersal rates between habitat patches under a Bayesian model. Our approach measures connectivity over the timescale represented by the small number of generations contained within the pedigree and so is appropriate for estimating the impacts of recent habitat changes due to human activity. We used our method to estimate recent movement between newly discovered populations of threatened Eastern Massasauga rattlesnakes (Sistrurus catenatus) using data from 2996 RAD‐based genetic loci. Our pedigree analyses found no evidence for contemporary connectivity between five genetic groups, but, as validation of our approach, showed high dispersal rates between sample sites within a single genetic cluster. We conclude that these five genetic clusters of Eastern Massasauga rattlesnakes have small numbers of resident snakes and are demographically isolated conservation units. More broadly, our methodology can be widely applied to determine contemporary connectivity rates, independent of bias from shared genetic similarity due to ancestry that impacts other approaches. 

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5. Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery

   https://doi.org/10.1145/3531146.3533181  

   Rahmattalabi, Aida; Vayanos, Phebe; Dullerud, Kathryn; Rice, Eric (June 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency)

   We study the problem of learning, from observational data, fair and interpretable policies that effectively match heterogeneous individuals to scarce resources of different types. We model this problem as a multi-class multi-server queuing system where both individuals and resources arrive stochastically over time. Each individual, upon arrival, is assigned to a queue where they wait to be matched to a resource. The resources are assigned in a first come first served (FCFS) fashion according to an eligibility structure that encodes the resource types that serve each queue. We propose a methodology based on techniques in modern causal inference to construct the individual queues as well as learn the matching outcomes and provide a mixed-integer optimization (MIO) formulation to optimize the eligibility structure. The MIO problem maximizes policy outcome subject to wait time and fairness constraints. It is very flexible, allowing for additional linear domain constraints. We conduct extensive analyses using synthetic and real-world data. In particular, we evaluate our framework using data from the U.S. Homeless Management Information System (HMIS). We obtain wait times as low as an FCFS policy while improving the rate of exit from homelessness for underserved or vulnerable groups (7% higher for the Black individuals and 15% higher for those below 17 years old) and overall. 

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