Reviewerly/exHarmony huggingface dataset:
Federated Bayesian Optimization for Privacy-Preserving Neural Architecture Search

Deep neural networks have achieved remarkable success in various fields, leading to an increasing demand for automatic design of high-quality network architectures instead of manual design. Neural architecture search offers a solution to automatic tuning of network architectures and associated hyperparameters. Early work on NAS assumes that all the training data is stored centrally on a single device, which is unrealistic in many real-world applications. In recent years, there has been a surge in research on federated neural architecture search due to the growing amount of data on edge devices and the increasing concerns over security and privacy protection. It allows several parties to collaboratively search for an optimal model without leaking the privacy of the data. However, existing work on federated NAS transmits the entire weight parameters of the candidate architectures between the clients and the server, leading to high communication costs and potential risks of leaking raw data. To fill this gap, this paper proposes a federated Bayesian optimization NAS approach called FL-BONAS. Instead of uploading the original weight values to the server during each round, the clients cooperatively train an ensemble surrogate model as the neural predictor, and each client searches for its own architectures via Bayesian optimization without revealing either the local raw data or the architectures. To the best of our knowledge, this is the first work to train an ensemble model in Bayesian optimization for NAS under federated learning scenarios. FL-BONAS significantly improves the data efficiency while protecting privacy. The experimental results show that the proposed FL-BONAS can achieve better performance and protect raw data compared with centralized NAS.
Design of Cloud-Based TT&amp;C Center System

As an important part of aerospace mission engineering, the TT&C center system currently adopts technologies such as duplex duplexing, server clusters, disk RAID, data backup and recovery to ensure the uninterrupted operation, stability and reliability of the aerospace mission system. In addition to meet high-reliability and real-time requirements of the system, there is also a need to decouple the application software and physical resource, facilitate resource sharing, system expansion and application software upgrade, as well as easy system operation and maintenance, so the paper addresses the design of the cloud-based TT&C center system that is used to dynamically allocate resources, virtualize physical devices, provide services to the upper layers, services customization on-demand, resource sharing and interoperability, and flexible expansion. The deployed TT&C center cloud system shows that a novel integration of the TT&C application services which enables TT&C professionals to better accomplish spacecraft management and control missions. The cloud-based TT&C center system is tested and verified by actual aerospace missions.
Reservoir Computing Based on a Solid Electrolyte ZnO TFT: An Attractive Platform for Flexible Edge Computing

This is a repository copy of Reservoir computing based on a solid electrolyte ZnO TFT: an attractive platform for flexible edge computing.
FaaSCTDO: Collaborative Task-Data Orchestration for Serverless Workflows

The use of Function-as-a-Service (FaaS) platforms for executing complex serverless workflows has gained significant popularity. However, the stateless nature of FaaS requires functions to rely on remote storage to store their state, leading to performance overhead and reduced efficiency. Previous approaches using excess memory resources as caches can cause resource contention and decreased task execution performance. Moreover, the impact of workflow orchestration approaches on latency has not been fully explored. To address these challenges, we propose FaaSCTDO, a framework for collaborative task and data orchestration in serverless workflows. FaaSCTDO provides developers with comprehensive lifecycle management capabilities, treating data as orchestratable objects. It groups tasks and data with high data correlation together and allocates them on the same server node, leveraging data locality to expedite function access to data.
A Linear Programming Enhanced Genetic Algorithm for Hyperparameter Tuning in Machine Learning

In this paper, we formulate the hyperparameter tuning problem in machine learning as a bilevel program. The bilevel program is solved using a micro genetic algorithm that is enhanced with a linear program. While the genetic algorithm searches over discrete hyperparameters, the linear program enhancement allows hyper local search over continuous hyperparameters. The major contribution in this paper is the formulation of a linear program that supports fast search over continuous hyperparameters, and can be integrated with any hyperparameter search technique. It can also be applied directly on any trained machine learning or deep learning model for the purpose of fine-tuning. We test the performance of the proposed approach on two datasets, MNIST and CIFAR-10. Our results clearly demonstrate that using the linear program enhancement offers significant promise when incorporated with any population-based approach for hyperparameter tuning.
Multi-Objective Optimisation and Genetic Programming for Trading by Combining Directional Changes and Technical Indicators

Directional changes (DC) have been shown to form an effective approach in algorithmic trading by converting fixed time series into event-based series and focusing on key events. Previous work has focused on forecasting the inflection point in the market and proposing new indicators under the DC framework, with just a handful of papers concerned with training and using DC indicators through machine learning. Earlier research has shown that genetic programming (GP) combining DC and physical time indicators could achieve positive returns with low risk. However, the fitness function used in that work is simply a risk-adjusted return. In this paper, we investigate whether a multi-objective optimisation approach could improve the performance of GP-based strategies in the market. We evaluate the cumulative return, risk, and rate of return of the proposed approach under 110 datasets from 10 different markets. Furthermore, we compare the proposed strategy against GP-based single objective optimisation (SOO) and buy-and-hold strategies. Our results show that the proposed approach significantly improves the cumulative return compared to SOO, from 14.29% to 62.04%, while also outperforming the buy-and-hold strategy.
Breast Cancer Prediction Using Classical Machine Learning Models

Machine Learning is an emerging topic of study nowadays. Due to their extensive capacity for data exploration and the discovery of illuminating patterns, machine learning, and data mining techniques have become extremely popular. Data mining techniques are also used in the healthcare industry and have proven particularly effective for cancer prognosis and prediction. Breast cancer is the second most significant factor in women's cancer-related death rates. In that situation, early detection of benign and malignant stages of breast cancer can enable the patient, and the doctor administers the appropriate treatments. In this study, a breast cancer dataset of 569 instances and 32 features were chosen to predict the stages of breast cancer. Four classification algorithms are employed: Support Vector Machine, K Nearest Neighbor, Multi layer Perceptron, and Radial Basis Neural Network. The algorithm's performance regarding Accuracy, Precision, Recall, and Roc Area are compared, and it is found that SVM outperformed the others with an accuracy rate of 98.05%. The paper also demonstrated how feature selection strategies might shorten training times before proposing a reliable and universal model for predicting the breast cancer stage.
A Filter-Based Feature Selection and Ranking Approach to Enhance Genetic Programming for High-Dimensional Data Analysis

Genetic programming (GP), as a predictive data analytic tool, has difficulties dealing with high-dimensional problems. Therefore, some GP variants have been proposed for this type of problem, such as multi-stage GP (MSGP). Filter-based feature selection is commonly used in the literature for various machine learning purposes. However, its application for GP is overlooked due to GP's capability to operate as a wrapper-based feature selection while trying to find an optimal expression of the target variable via a functional combination of predictors. The effectiveness of wrapper- and filer-based feature selection approaches in machine learning has been the subject of a long-standing debate in the literature. This study aims to introduce an efficient feature selection approach and couple it with MSGP in order to handle high-dimensional problems. In addition, the stages of the GP are systematically ordered based on the variables' information. The proposed approach is tested against five real high-dimensional datasets. The results show that GP's inherent wrapper feature selection ability can be advanced further by using a filter-based feature selection approach to shrink the search space, which results in improving computational costs, expression complexity and the accuracy of MSGP.
A Scenario-Based Functional Testing Approach to Improving DNN Performance

This paper proposes a scenario-based functional testing approach for enhancing the performance of machine learning (ML) applications. The proposed method is an iterative process that starts with testing the ML model on various scenarios to identify areas of weakness. It follows by a further testing on the suspected weak scenarios and statistically evaluate the model's performance on the scenarios to confirm the diagnosis. Once the diagnosis of weak scenarios is confirmed by test results, the treatment of the model is performed by retraining the model using a transfer learning technique with the original model as the base and applying a set of training data specifically targeting the treated scenarios plus a subset of training data selected at random from the original train dataset to prevent the so-call catastrophic forgetting effect. Finally, after the treatment, the model is assessed and evaluated again by testing on the treated scenarios as well as other scenarios to check if the treatment is effective and no side-effect caused. The paper reports a case study with a real ML deep neural network (DNN) model, which is the perception system of an autonomous racing car. It is demonstrated that the method is effective in the sense that DNN model's performance can be improved. It provides an efficient method of enhancing ML model's performance with much less human and compute resource than retrain from scratch.
Design of fin structured underwater data center enclosure based on integer programming

The underwater data center is a project to install Internet facilities such as servers in submarine containers with cooling function, and sink the data center into the submarine to solve many shortcomings of land-based data centers. In this paper, we analyze the design problem of an underwater data center shell by establishing a model of the number of servers and using mathematical-physical methods. We use the integer programming method to establish an integer programming model for the number of servers under space constraints, and solve the maximum number of servers in a data center under space constraints. For the heat dissipation problem, in this paper, we use Newton’s law of cooling to calculate the total heat dissipation of the container under the convection heat transfer cooling method. Based on the relationship between the heat dissipation and the heat production of a single server, the maximum number of servers in the data center under the temperature limit was determined, and the final result was obtained. In this paper, the fin structure is selected to increase the contact area between the container and seawater to enhance the heat dissipation effect of the data center. Integrate the integer programming model with the cooling model of the underwater data center to obtain the server number model. Through the calculation, the maximum value of the servers that can work normally in the cylindrical fin container is obtained. Finally, the reasonableness of the results is verified by CFD calculations. The study of this problem can provide some optimization suggestions for the underwater data center design departments of related companies.
Dynamic ensemble multi-strategy based bald eagle search optimization algorithm: A controller parameters tuning approach

To address the problems of bald eagle search algorithm (BES), easy fall into local optimums, limited diversity and slow convergence, a dynamic ensemble multi-strategy bald eagle search (DMBES) algorithm is proposed. Firstly, a nonlinear control factor is constructed to explore the potential range of population search, which helps to explore potential solutions. Next, a Levy flight strategy is introduced to better smooth the transition and explore the solution space through individual information interaction. Then, a dynamic selection strategy is proposed to help individuals obtain optimal solutions and better balance the algorithm's exploration and exploitation capabilities. Finally, in numerical experiments, classical benchmark functions and CEC 2022 test functions are selected to compare the performance of DMBES with other reliable and recent algorithms. In addition, box plots, Wilcoxon signed-rank test and Friedman rank test are used to confirm the significance of the results. In the controller parameter tuning experiments, the results show that the optimized controller of the DMBES algorithm outperforms the six comparison algorithms in terms of overshoot and rise time.
Quantum Biometric Fingerprint Encryption Based on Confusion and Feistel Algorithm

Biometrics are intended to add an extra layer of security to various applications by authenticating a user based on a biometric sample taken from them. At the same time advantage, from the users' perspective, is that they are no longer required to use biometric memorizing credentials. Fingerprinting has been in use since the 19 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century in determining identity. Because users' biometric templates are stored within any system, their security becomes extremely sensitive, as theft or simple copying of the biometric can compromise the identity of all users enrolled in the system. This paper presents a quantum-based biometric fingerprint encryption method. The method uses an adapted Novel Enhanced Quantum (NEQR) model to store biometric fingerprint in quantum systems and proposes quantum techniques of Confusion and Feistel algorithm to encryption it. The quantum encryption circuit was implemented and tested both in the IBM Quantum cloud and locally on the QASM quantum simulator.
Free the Turtles: Removing Nested Virtualization for Performance and Confidentiality in the Cloud

With the growing popularity of containers running in cloud environments, one might believe techniques like virtual machines (VMs) and nested virtualization are no longer essential for cloud computing, but this is not true. First, they are foundational for the Infrastructure as a Service (IaaS) base that most clouds use for their Kubernetes (K8s) offerings - nodes are deployed in VMs instead of on bare-metal machines to improve flexibility and resource utilization. Second, state-of-theart technology like Kata Containers or KubeVirt deploys VMs inside K8s, to improve container isolation or to offer a cloudnative way to deploy VMs. When such techniques are coupled with VM-based K8s worker node deployments, this requires the use of nested virtualization. However, nested virtualization introduces a larger trusted computing base (TCB) and therefore raises security concerns. In addition, it introduces an important performance overhead and cannot be easily applied to confidential computing, an emerging technology to allow the execution of sensitive workloads in the cloud. In this paper, we propose the secondary-VM (secVM) framework, an alternative to nested virtualization which addresses these challenges by flattening the nested hierarchy, but maintains the resource isolation features of nested virtualization. By removing the emulation layer required for nested virtualization, the secVM framework reduces the TCB, is compatible with any confidential computing techniques, and removes the overhead associated with nested virtualization. Therefore, in reference to the Turtles Project that introduced the concept of nested virtualization in the open-source virtualization technology - Kernel-based Virtual Machine (KVM), we argue it is time to free the “turtles”.
A model for automatic selection of IoT services in ambient assisted living for the elderly

Engineering Ambient Assisted Living applications for the elderly is challenging due to the diversity and rapid changes of both end users’ needs and technological environment equipment. Assistive applications can be provided as combinations of functionalities provided by IoT devices. With the pervasive availability of functionally equivalent IoT devices, they should be selected according to the specific deployment context in terms of user needs and conditions, device availability, and regulations when the operative context dynamic conditions can be set. Such selection is the objective of this work. We rely on a conceptual framework for self-adaptation as the enabler for a run-time decision-making process. It allows for representing relations among IoT devices, the functionalities they deliver, and the different modalities these functionalities are provided with in terms of goals, devices, and norms. The framework is based on three fundamental principles: (1) high-level abstractions separating the expected functionality, how it can be delivered, and who is responsible for its delivery; (2) AAL applications as the run-time composition of atomic functionalities; (3) centrality of the user in the system. The Device-Goal-Norm framework is proposed to specify diagrams for different AAL applications, together with the semantics to transform these diagrams into run-time models. We also provide a running implementation of a run-time model based on the belief–desire-intention paradigm.
SCOOP: A Scalable Object-Oriented Serverless Platform

Function-as-a-Service (FaaS) has been the primary component to drive the movement toward serverless computing. These lightweight and scalable components, though attractive, are non-trivial to accommodate the needs of long-running stateful applications. In this paper, we highlight the drawbacks of existing stateful FaaS proposals, in turn motivating the need to rethink the stateful serverless model for building general-purpose applications, while maintaining its benefits such as auto-scaling and pay-per-use cost model. We present a novel serverless model based on the object-oriented (OO) programming paradigm, with Object-as-a-Service (OaaS), acting as the only component of the serverless design. Through our experimental evaluations, we demonstrate that the proposed architecture, named SCOOP, can improve the end-to-end latency of applications by 52% and 58%, compared to the state-of-the-art stateless and stateful FaaS implementations, respectively, while reducing the SLO violations by up to 14% by scaling resources based on the traffic fluctuations in the WITS and Berkeley traces.
Reasoning about knowledge and conditional probability

We present a proof-theoretical and model-theoretical approach to reasoning about knowledge and conditional probability. We extend both the language of epistemic logic and the language of linear weight formulas, allowing statements like "Agent Ag knows that the probability of A given B is at least a half". We present both a propositional and a first-order version of the logic. We provide sound and complete axiomatizations for both logics and we prove decidability in the propositional case.
Towards fidelity of graph data augmentation via equivariance

Graph data augmentation is widely used in machine learning, and when serving the underlying model, it is necessary to ensure that the feature–graph–label tuples are consistent before and after augmentation. To this end, controlling the fidelity of graph data augmentation to satisfy such consistency becomes a fundamental issue. To date, some graph data augmentation literature has discussed the importance of augmenting fidelity, but few theoretical guarantees have been provided. Here we theoretically ensure the fidelity by introducing the notion of aug-equivariance, based on its classical counterpart of equivariance. In our work, we first provide conditions to ensure that Graph Convolution Network is aug-equivariant with respect to the proposed transformations for both data and label distributions. Based on the derived conditions, we propose a novel graph data augmentation framework named E-Aug. The framework enables both edges and features to be augmented under the constraints of aug-equivariance, and the E-Aug-enhanced data does not damage the features and topology of the original graph. We experimentally applied E-Aug to various architectures such as Graph Convolutional Networks and Graph Attention Networks to verify the effectiveness of our method. Our E-Aug models can consistently improve or closely match state-of-the-art results across four established graph benchmarks:the Cora, Citeseer and Pubmed citation network datasets, as well as the Europe air traffic network dataset.
Accelerating pharmaceutical R&amp;D with a user-friendly AI system for histopathology image analysis

A system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing platform, so that non-data scientist users (e.g., clinicians) can utilize and evaluate pre-trained algorithms and retrieve quantitative results. The outputs of analysis are automatically cataloged in the database to track data provenance and can be viewed interactively on the slide as annotations or heatmaps. Commonly used models for analysis of whole slide images including segmentation, extraction of hand-engineered features for segmented regions, and slide-level classification using multi-instance learning are included and new models can be added as needed. The source code that supports running inference with these models internally is backed up by a robust CI/CD pipeline to ensure model versioning, robust testing, and seamless deployment of the latest models. Examples of the use of this system in a pharmaceutical development workflow include glomeruli segmentation, enumeration of podocyte count from WT-1 immuno-histochemistry, measurement of beta-1 integrin target engagement from immunofluorescence, digital glomerular phenotyping from periodic acid-Schiff histology, PD-L1 score prediction using multi-instance learning, and the deployment of the open-source Segment Anything model to speed up annotation.