Microservice-based Reference Architecture for Semantics-aware Measurement Systems

Cloud technologies have become more important than ever with the rising need for scalable and distributed software systems. A pattern that is used in many such systems is a microservice-based architecture (MSA). MSAs have become a blueprint for many large companies and big software systems. In many scientific fields like energy and environmental informatics, efficient and scalable software systems with a primary focus on measurement data are a core requirement. Nowadays, there are many ways to solve research questions using data-driven approaches. Most of them have a need for large amounts of measurement data and according metadata. However, many measurement systems still follow deprecated guidelines such as monolithic architectures, classic relational database principles and are missing semantic awareness and interpretation of data. These problems and the resulting requirements are tackled by the introduction of a reference architecture with a focus on measurement systems that utilizes the principles of microservices. The thesis first presents the systematic design of the reference architecture by using the principles of Domain-driven Design (DDD). This process ensures that the reference architecture is defined in a modular and sustainable way in contrast to complex monolithic software systems. An extensive scientific analysis leads to the core parts of the concept consisting of the data management and semantics for measurement systems. Different data services define a concept for managing measurement data, according meta data and master data describing the business objects of the application implemented by using the reference architecture. Further concepts allow the reference architecture to define a way for the system to understand and interpret the data using semantic information. Lastly, the introduction of a frontend framework for dashboard applications represents an example for visualizing the data managed by the microservices

The relationship of the co-curriculum with student faith development: Challenge and support at a college of the church

Luther College (Decorah, IA) is a liberal arts school of 2550 students that has been affiliated with the Evangelical Church in America since 1861. Its mission includes a higher calling to help students connect faith with learning, freedom with responsibility, and life\u27s work with service. The college co-curriculum includes more than seventy campus organizations, seventeen intercollegiate sports, thirteen music ensembles, a comprehensive work-study program, and numerous community connections. Amidst these chaotic activities and events, relationships, and challenging learning environments, student faith is developing. Administrators at the college do not seem to understand the significant relationship of the co-curriculum with student faith development. The purpose of this case study is to describe, understand, and assess how and to what extent the co-curriculum contributes to student faith development at a college of the church. James W. Fowler\u27s stages of faith model and Sharon Dolaz Parks\u27 extension of Fowler\u27s model into higher education help to inform and guide the research. Other faith development scholarship also provides student affairs professionals with methods for understanding the college student as a person of faith. With the recent resurgence in values-based education in the United States, now is the time that college administrators seriously consider matters of student faith development when creating, implementing and assessing co-curricular programs at their higher education institutions. This is especially important at a college of the church, which must be diligent in its efforts to distinguish itself from other institutions in the highly competitive educational market place of today. Twenty junior and senior class students were nominated to participate in qualitative interviews. The interview format was divided into four distinct sections: demographic information, college of the church perceptions, co-curricular commitments, and faith development experiences. Interview findings were categorized into a two-tier model, which includes challenges to student faith development, and supports for student faith development at a college of the church. Practical improvements for student affairs practice and future research efforts in faith development are discussed in the concluding chapter of this study

Teaching History of Mathematics: A Dialogue

Many colleges and universities offer a course in the history of mathematics. While the potential benefits for students taking such a course might be apparent, it is often less clear how teaching a history of mathematics course can be a transformational experience for faculty. We present a dialogue between the authors regarding their experiences teaching history of mathematics courses, including their motivation for doing so, the impact these experiences have had on their classroom practices and assessment methods, and the opportunities history of mathematics courses offer for incorporating social justice, equity, and inclusion into the study of mathematics. Our goal is to shine additional light on unexpected ways that teaching the history of mathematics can transform faculty perspectives and practices

Dynamic Federated Learning for Heterogeneous Learning Environments

The emergence of the Internet of Things (IoT) has resulted in a massive influx of data generated by various edge devices. Machine learning models trained on this data can provide valuable insights and predictions, leading to better decision-making and intelligent applications. Federated Learning (FL) is a distributed learning paradigm that enables remote devices to collaboratively train models without sharing sensitive data, thus preserving user privacy and reducing communication overhead. However, despite recent breakthroughs in FL, the heterogeneous learning environments significantly limit its performance and hinder its real-world applications. The heterogeneous learning environment is mainly embodied in two aspects. Firstly, the statistically heterogeneous (usually non-independent identically distributed) data from geographically distributed clients can deteriorate the FL training accuracy. Secondly, the heterogeneous computing and communication resources in IoT devices often result in unstable training processes that slow down the training of a global model and affect energy consumption. Most existing studies address only the unilateral side of the heterogeneity issue, either the statistical or the resource heterogeneity. However, the resource heterogeneity among various devices does not necessarily correlate with the distribution of their training data. We propose Dynamic Federated Learning (DFL) to address the joint problem of data and resource heterogeneity in FL. DFL combines resource-aware split computing of deep neural networks and dynamic clustering of training participants based on the similarity of their sub-model layers. Using resource-aware split learning, the allocation of the FL training tasks on resource-constrained participants is adjusted to match their heterogeneous computing capabilities, while resource-capable participants carry out the classic FL training. We employ centered kernel alignment for determining the similarity of neural network layers to address the data heterogeneity and carry out layerwise sub-model aggregation. Preliminary results indicate that the proposed technique can improve training performance (i.e., training time, accuracy, and energy consumption) in heterogeneous learning environments with both data and resource heterogeneity

Factors influencing transfusion-associated HLA sensitization in patients bridged to heart transplantation using ventricular assist device.

BackgroundBridging heart failure patients with mechanical ventricular assist devices (VAD) enables access to transplantation. However, VAD is associated with increased risk for anti-HLA antibodies associated with rejection of subsequent allografts. Factors determining alloantibody formation in these patients remain undefined.MethodsWe performed a single-center retrospective cohort study of 164 patients undergoing heart transplantation from 2014 to 2017. Medical records including use of VAD, transfused blood products, anti-HLA antibody testing, crossmatch, and time to transplant were evaluated.ResultsPatients received an average of 13.8 red blood cell and 1.9 single-donor platelet units associated with VAD. There was a 28.7% increase in the incidence of anti-HLA antibodies after VAD. Development of anti-HLA antibodies did not correlate with volume or type of blood products, but with pre-VAD HLA sensitization status; relative risk of new alloantibodies in patients with pre-VAD antibodies was 3.5-fold higher than those without prior antibodies (P = .008). Development of new anti-HLA antibodies was associated with an increased time to transplant (169 vs 330 days, P = .013).ConclusionsOur findings indicate that the presence of anti-HLA antibodies pre-VAD was the most significant risk factor for developing additional antibodies post-VAD, suggesting that a subset of patients may be predisposed to alloantibody formation

Distributed and Federated Learning Optimization with Federated Clustering of IID-users

Federated Learning (FL) is one of the leading learning paradigms for enabling a more significant presence of intelligent applications in networked and Internet of Things (IoT) systems. It consists of individual user devices performing machine learning (ML) models training locally, so that only trained models due to privacy concerns, but not raw data, is transferred through the network for aggregation at the edge or cloud data centers [Li et al. 2019]. Due to the pervasive presence of connected devices such as smart phones and IoT devices in peoples lives, there is a growing concern about how we can preserve and secure users’ information. FL reduces the risk of exposing user information to attackers during transmission over networks or information leakages at the central data centers. Another advantage of FL is scalability and maintainability of intelligent applications in networked and IoT systems. Considering highly distributed environments in which such systems are deployed, collecting and transmitting raw user data for training of ML models at central data centers is a challenging task as it imposes huge workload on the networks and consumes high bandwidth. Training of ML models is distributed over locations and transmitting the trained models for aggregation alleviates these challenges. Among others, distributed and federated learning have applications in smart healthcare systems, where very sensitive user data is involved, and industrial IoT applications, where the amount of data for training may be too large and cumbersome to transport to central data centers. However, FL has the significant shortcoming of requiring user data to be Independent Identically Distributed (IID) (i.e., users which have similar data statistical distributions and are not mutually dependent) and make reliable predictions for a given group of users aggregated into a single model. IID users have similar statistical features, and thus can be aggregated into the same ML models. Since raw data is not available at the model aggregator, it is necessary to find IID users based solely on their trained machine learning models. We present a Neural Network-based Federated Clustering mechanism capable of clustering IID with no access to their raw data called Neural-network SIMilarity estimator, NSIM. Such mechanism performs significantly better than competing techniques for neural-network clustering [Pacheco et al. 2021]. We also present an alternative to the FedAvg aggregation algorithm used in traditional FL, which significantly increases the aggregated models’ reliability in terms of Mean Square Error by creating several training models over IID users in a real-world mobility prediction dataset. We observe improvements of up to 97.52% in terms of Pearson correlation between the similarity estimation by NSIM and ground truth based on the LCSS (Longest Common Sub-Sequence) similarity metric, in comparison with other state-of-the-art approaches. Federated Clustering of IID data in different geographical locations can improve performance of early warning applications such as flood prediction [Samikwa et al. 2020], where the data for some locations may have more statistical similarities. We further present a technique for accelerating ML inference in resource-constrained devices through distributed computation of ML models over IoT networks, while preserving privacy. This has the potential to improve the performance of time sensitive ML applications

ARES: Adaptive Resource-Aware Split Learning for Internet of Things

Distributed training of Machine Learning models in edge Internet of Things (IoT) environments is challenging because of three main points. First, resource-constrained devices have large training times and limited energy budget. Second, resource heterogeneity of IoT devices slows down the training of the global model due to the presence of slower devices (stragglers). Finally, varying operational conditions, such as network bandwidth, and computing resources, significantly affect training time and energy consumption. Recent studies have proposed Split Learning (SL) for distributed model training with limited resources but its efficient implementation on the resource-constrained and decentralized heterogeneous IoT devices remains minimally explored. We propose Adaptive REsource-aware Splitlearning (ARES), a scheme for efficient model training in IoT systems. ARES accelerates local training in resource-constrained devices and minimizes the effect of stragglers on the training through device-targeted split points while accounting for time-varying network throughput and computing resources. ARES takes into account application constraints to mitigate training optimization tradeoffs in terms of energy consumption and training time. We evaluate ARES prototype on a real testbed comprising heterogeneous IoT devices running a widely-adopted deep neural network and dataset. Results show that ARES accelerates model training on IoT devices by up to 48% and minimizes the energy consumption by up to 61.4% compared to Federated Learning (FL) and classic SL, without sacrificing the model convergence and accurac

String Method for the Study of Rare Events

We present a new and efficient method for computing the transition pathways, free energy barriers, and transition rates in complex systems with relatively smooth energy landscapes. The method proceeds by evolving strings, i.e. smooth curves with intrinsic parametrization whose dynamics takes them to the most probable transition path between two metastable regions in the configuration space. Free energy barriers and transition rates can then be determined by standard umbrella sampling technique around the string. Applications to Lennard-Jones cluster rearrangement and thermally induced switching of a magnetic film are presented.Comment: 4 pages, 4 figure