Dynamic Selection Research Articles

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries

Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery are so far limited to static settings, contrasting with the sequential nature of the problem. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose dynamic selection and pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against two state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9 percentage points (%pt) in costs compared with a situation without OOH locations, 7%pt compared with a static selection and pricing policy, and 3.8%pt compared with a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest that practitioners adopt dynamic selection and pricing policies. History: This paper has been accepted for the Transportation Science special issue on TSL Conference 2023. Funding: This work was supported by TKI DINALOG.

Dynamic Parallel Multi-Server Selection and Allocation in Collaborative Edge Computing

Dynamic Parallel Multi-Server Selection and Allocation in Collaborative Edge Computing

Integrating machine learning with proof-of-authority-and-association for dynamic signer selection in blockchain networks

Integrating machine learning with proof-of-authority-and-association for dynamic signer selection in blockchain networks

A Markov chain-based multi-criteria framework for dynamic cloud service selection using user feedback

A Markov chain-based multi-criteria framework for dynamic cloud service selection using user feedback

Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment

Depression is a common mental health issue, especially among older adults, and early detection is critical to effective intervention and treatment. This study proposes an explainable multilayer dynamic ensemble framework optimized for detecting depression and predicting its severity. Using data from real individuals from the National Social Life, Health, and Aging Project (NSHAP), the framework integrates classical machine learning models, static ensemble techniques, and dynamic ensemble selection (DES) approaches to build a two-stage framework. The depression detection process is performed in the first stage, and the depression severity is predicted in the second stage only for depressed patients. Among the models evaluated, the FIRE-KNOP DES algorithm exhibited the highest performance, achieving 88.33% accuracy in detecting depression and 83.68% accuracy in predicting depression severity. A key innovation of this study is the incorporation of explainable AI (XAI) techniques to enhance model interpretability, making the framework more suitable for clinical applications. We explore different global and local XAI features. Model explainability highlights the significance of medically relevant mental and non-mental health features in improving the model’s performance, which enhances the trustworthiness of the domain experts in the model decisions. These findings offer a promising foundation for future applications of dynamic ensemble frameworks in mental health assessments.

RT-Cabi: an Internet of Things based framework for anomaly behavior detection with data correction through edge collaboration and dynamic feature fusion

The rapid advancement of Internet of Things (IoT) technologies brings forth new security challenges, particularly in anomaly behavior detection in traffic flow. To address these challenges, this study introduces RT-Cabi (Real-Time Cyber-Intelligence Behavioral Anomaly Identifier), an innovative framework for IoT traffic anomaly detection that leverages edge computing to enhance the data processing and analysis capabilities, thereby improving the accuracy and efficiency of anomaly detection. RT-Cabi incorporates an adaptive edge collaboration mechanism, dynamic feature fusion and selection techniques, and optimized lightweight convolutional neural network (CNN) frameworks to address the limitations of traditional models in resource-constrained edge devices. Experiments conducted on two public datasets, Edge-IIoT and UNSW_NB15, demonstrate that RT-Cabi achieves a detection accuracy of 98.45% and 90.94%, respectively, significantly outperforming existing methods. These contributions not only validate the effectiveness of the RT-Cabi model in identifying anomalous behaviors in IoT traffic but also offer new perspectives and technological pathways for future research in IoT security.

Emergence of IGH::CCND1 rearrangement and mutations in TP53, BTK, and BCL2 associated with therapy resistance in chronic lymphocytic leukemia

AbstractChronic lymphocytic leukemia (CLL) is an indolent low‐grade B‐cell neoplasm that generally responds well to treatment. Rarely, CLL cases exhibit an IGH::CCND1 rearrangement, presenting a diagnostic challenge in distinguishing between clonal evolution within CLL and an independent mantle cell lymphoma. We report a case of CLL with the emergence of an IGH::CCND1 rearrangement and mutations associated with treatment resistance, including TP53, BTK, and BCL2, during disease progression. Immunophenotypic, cytogenetic, and molecular analyses support that these alterations are secondary events within the CLL clone. This case demonstrates dynamic clonal selection and expansion in response to treatments, which, in turn, contributes to treatment resistance.

Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy.

RFCSC: Communication efficient reinforcement federated learning with dynamic client selection and adaptive gradient compression

In the field of public safety, high-quality data is often owned by governments, companies, and organizations, making it difficult to train effective models through centralized datasets. This paper leverages federated learning as a mechanism to address data privacy concerns. Within the federated learning framework, the data across different clients is typically Non-IID (non-Independently and Identically Distributed). Furthermore, The complexity of sensitive image recognition tasks in public safety, along with the large number of model parameters, can lead to communication congestion issues in federated learning. To solve these challenges, this paper proposes a Reinforcement Federated Client Selection and Gradient Compression Method(RFCSC). By integrating client data prototypes with accuracy metrics, we dynamically assess the contribution of each client in federated learning. Through an intelligent dynamic incentive mechanism based on reinforcement learning, high-quality client nodes are dynamically selected to participate in federated learning, achieving dynamic adaptive aggregation, reducing the influence of Non-IID data, reducing communication cost, enhancing model accuracy, and achieving a balance between quality and efficiency. To address the issue of high communication cost for parameter transmission, we propose an adaptive model compression strategy in reinforcement federated learning which make local clients find an appropriate compression rate. This approach not only reduces gradient communication overhead but also minimizes the impact of compression on model accuracy. The effectiveness of our proposed approach are corroborated through comprehensive experiments conducted on one private dataset and two public datasets.

A Dynamic Selection Method for Touch-Based Continuous Authentication On Mobile Devices

Touch biometric is one of the promising modalities to realise continuous authentication (CA) on mobile devices by distinguishing between touch strokes performed by the legitimate and illegitimate users. While the benefit of the scheme is promising, the effectiveness of different classification methods is not thoroughly understood. Little consideration has been given to dynamic selection of classifiers. In this paper, we proposed a dynamic selection method to deal with the security and usability needs of touch-based CA. Instead of classifying all touch samples using the same classifier, the method classifies each touch sample using the most promising classifier(s) from a pool of classifiers based on a certain measure of competence. The classifiers that achieve the highest level of competence will be selected to perform the classification task. We compared the proposed method with other dynamic selection methods, well-known single classifiers, as well as static ensemble methods. The experimental results show the potential and feasibility of the proposed method to improve the authentication performance of touch-based CA against the benchmark methods. We found that the proposed method can produce promising results with relatively low equal error rate in many scenarios of the datasets, with relatively high consistency.

Lossless Approximate Pattern Matching: Automated Design of Efficient Search Schemes.

This study introduces a pioneering approach to automate the creation of search schemes for lossless approximate pattern matching. Search schemes are combinatorial structures that define a series of searches over a partitioned pattern. Each search specifies the processing order of these parts and the cumulative lower and upper bounds on the number of errors in each part of the pattern. Together, these searches ensure the identification of all approximate occurrences of a search pattern within a predefined limit of k errors. While existing literature offers designed schemes for up to k = 4 errors, designing search schemes for larger k values incurs escalating computational costs. Our method integrates a greedy algorithm and a novel Integer Linear Programming (ILP) formulation to design efficient search schemes for up to k = 7 errors. Comparative analyses demonstrate the superiority of our ILP-optimal schemes over alternative strategies in both theoretical and practical contexts. Additionally, we propose a dynamic scheme selection technique tailored to specific search patterns, further enhancing efficiency. Combined, this yields runtime reductions of up to 53% for higher k values. To facilitate search scheme generation, we present Hato, an open-source software tool (AGPL-3.0 license) employing the greedy algorithm and utilizing CPLEX for ILP solving. Furthermore, we introduce Columba 1.2, an open-source lossless read-mapper (AGPL-3.0 license) implemented in C++. Columba surpasses existing state-of-the-art tools by identifying all approximate occurrences of 100,000 Illumina reads (150 bp) in the human reference genome within 24 seconds (maximum edit distance of 4) and 75 seconds (maximum edit distance of 6) using a single CPU core. Notably, our study showcases Columba's capability to align 100,000 reads of length 50, with high error rates and up to an edit distance of 7, in a mere 2 hours and 15 minutes. This achievement is unmatched by other lossless aligners, which require over 3 hours for edit distance 5 alignments. Moreover, Columba exhibits a mapping rate four times higher than that of a lossy tool for this dataset.

Fault traceability of power grid dispatching system based on DPHS-MDS and LambdaMART algorithm

Under the background of increasing system service data, it is difficult to trace the faults of power dispatching system. The current fault tracing method has some problems, such as low precision, low efficiency and difficult troubleshooting. To solve this problem, a fault tracing method based on data partition hybrid sampling method and multiple incremental regression tree algorithm is proposed. In this paper, it first uses the hybrid sampling method of data partition and dynamic selection technology to detect the business anomaly, and then applies the clustering algorithm and the information difference graph model to realize the fault tracing of system components. The experimental results showed that the F-metric value and geometric mean value of the study design method were 0.964 and 0.685, respectively. In addition, normalized discounted cumulative gains were observed in the top 10, and the mean average precision of the top 10 was 0.752 and 0.186, respectively. The proposed method can effectively improve the fault tracing efficiency of power grid operation and maintenance personnel, and provide strong data support for the safety maintenance of power grid dispatching system.

Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings

In this study, the extent of concrete building distress is used to determine whether a building needs to be demolished and maintained, and the study focuses on accurately identifying target distress in different complex contexts and accurately distinguishing between their categories. To solve the problem of insufficient feature extraction of small targets in bridge disease images under complex backgrounds and noise, we propose the YOLOv8 Dynamic Plus model. First, we enhanced attention on multi-scale disease features by implementing structural reparameterization with parallel small-kernel expansion convolution. Next, we reconstructed the relationship between localization and classification tasks in the detection head and implemented dynamic selection of interactive features using a feature extractor to improve the accuracy of classification and recognition. Finally, to address problems of missed detection, such as inadequate extraction of small targets, we extended the original YOLOv8 architecture by adding a layer in the feature extraction phase dedicated to small-target detection. This modification integrated the neck part more effectively with the shallow features of the original three-layer YOLOv8 feature extraction stage. The improved YOLOv8 Dynamic Plus model demonstrated a 7.4 percentage-point increase in performance compared to the original model, validating the feasibility of our approach and enhancing its capability for building disease detection. In practice, this improvement has led to more accurate maintenance and safety assessments of concrete buildings and earlier detection of potential structural problems, resulting in lower maintenance costs and longer building life. This not only improves the safety of buildings but also brings significant economic benefits and social value to the industries involved.

Vertical farming goes dynamic: optimizing resource use efficiency, product quality, and energy costs

Vertical farming is considered to be a key enabler for transforming agrifood systems, especially in or nearby urbanized areas. Vertical farming systems (VFS) are advanced indoor cropping systems that allow for highly intensified and standardized plant production. The close control of environmental parameters makes crop production stable and repeatable, ensuring year-round uniform product quality and quantity irrespective of location. However, due to continuous changes in plant physiology and development, as well as frequent changes in electricity prices, the optimum conditions for crop production and its associated costs can change within days or even minutes. This makes it beneficial to dynamically adjust setpoints for light (intensity, spectrum, pattern, and daylength), CO2, temperature, humidity, air flow, and water and nutrient availability. In this review, we highlight the beneficial effects that dynamic growth conditions can have on key plant processes, including improvements in photosynthetic gas exchange, transpiration, organ growth, development, light interception, flowering, and product quality. Our novel findings based on modeling and experimentation demonstrate that a dynamic daily light intensity pattern that responds to frequent changes in electricity prices can save costs without reducing biomass. Further, we argue that a smart, dynamic VFS climate management requires feedback mechanisms: several mobile and immobile sensors could work in combination to continuously monitor the crop, generating data that feeds into crop growth models, which, in turn, generate climate setpoints. In addition, we posit that breeding for the VFS environment is at a very early stage and highlight traits for breeding for this specialized environment. We envision a continuous feedback loop between dynamic crop management, crop monitoring, and trait selection for genotypes that are specialized for these conditions.

A cluster-based quantum key distribution with dynamic node selection: an improved approach for scalability and security in quantum communication

A cluster-based quantum key distribution with dynamic node selection: an improved approach for scalability and security in quantum communication

Accelerating large-scale DEA computation using sequential categorization and dynamic reference set selection

Data envelopment analysis (DEA) is a well-known data-enabled analytic tool for evaluating relative efficiency of units with multiple inputs and multiple outputs. The DEA computation increases substantially in the presence of large samples. In this study, we first recall two lemmas to distinguish efficient units using arithmetic operations without solving linear programming (LP). Using the used lemmas, the total sample of units is partitioned into several sequential blocks, where units in the preceding blocks are relatively efficient to those in the subsequent blocks. A novel reference set selection procedure is then formulated. We implement the proposed approach into one of the fastest existing methods and demonstrate a significant improvement in elapsed time. We conduct simulation experiments and illustrate the outcomes across varying dimensions, cardinality, and density.

Dynamic portfolio optimization with the MARCOS approach under uncertainty

This paper introduces a dynamic portfolio selection approach that integrates the robustness of interval type-2 fuzzy sets (IT2FSs) with the flexibility of the MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) method, offering a novel framework for asset evaluation amidst uncertainty. The IT2FSs enhance the adaptability of asset criteria representation, while the innovative application of MARCOS within the IT2FS environment refines the asset selection process. The weighted semi-absolute deviation metric is embraced to capture portfolio risk characteristic, which ingeniously harnesses the utility function values derived from the IT2F-MARCOS framework to delineate the anticipated return profile. On this basis, a dynamic bi-objective portfolio allocation model with realistic constraints and dynamic risk preference is formulated to rebalance portfolio periodically. Empirical evidence demonstrates the robustness and effectiveness of this approach compared to benchmark indexes, different allocation strategies, and the TOPSIS-based selection method, offering significant advancements in portfolio optimization for investors navigating uncertain markets. This study endeavors to contribute to the field of portfolio management, providing a thoughtful approach that enhances both theoretical understanding and practical application.

PDSMV3-DCRNN: A novel ensemble deep learning framework for enhancing phishing detection and URL extraction

Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). As a result, it is beneficial to examine URLs before accessing them to spot a phishing assault. Several algorithms based on machine learning have been presented to detect phishing attempts. However, these approaches often suffer from lower performance outcomes, such as lower accuracy, longer response times, and higher false positive rates. Furthermore, many existing methods rely heavily on predefined feature sets, which may limit their adaptability and robustness. In contrast, our proposed method leverages a more dynamic feature selection process, which includes the Conditional Wasserstein Generative Adversarial Network (CWGAN) for addressing data imbalance and the Binary Grey Goose Optimization Algorithm (BGGOA) for optimal feature selection. This dynamic approach enhances the model's ability to adapt to varying data characteristics, improving detection performance. The proposed solution is divided into two stages: pre-deployment and deployment. During the pre-deployment stage, the dataset is preprocessed, including data transformation, handling irrelevant and redundant data, and ensuring data balancing. Minority samples are increased using CWGAN to avoid class imbalance. Features are then selected using BGGOA, resulting in a feature-reduced dataset used for training and testing ensemble deep learning classifiers, specifically the Novel Pyramid Depth-wise Separable-MobileNetV3 (PyDS-MV3) and Deformable Convolutional Residual Neural Network (DCRNN), termed PDSMV3-DCRNN. During the deployment phase, the Boosted ConvNeXt approach extracts URL features fed into the trained classifier to predict "phishing" or "benign". According to experimental findings, the proposed solution outperforms all other tested approaches, displaying a faster training time of 0.11 s and achieving an optimal accuracy of 99.21%.

Bayesian learning in dynamic portfolio selection under a minimax rule

Bayesian learning in dynamic portfolio selection under a minimax rule

Category-sensitive semi-supervised semantic segmentation framework for land-use/land-cover mapping with optical remote sensing images

High-quality land-use/land-cover mapping with optical remote sensing images yet presents significant work. Even though fully convolutional semantic segmentation models have recently contributed to popular solutions, the lack of annotation data may lead to severe degradations in their inference performance. Besides, the category confusion in high-resolution representations will further exacerbate the adverse effects. In this paper, we propose a category-sensitive semi-supervised semantic segmentation framework to address these weaknesses by employing massive unlabeled data. With the perturbations from adopted hybrid data augmentation structures, we first focus on the output space and execute regularization constraints to learn category-specific discriminative features. It is formulated with a consistency self-training procedure where a dynamic class-balanced threshold selection scheme is proposed to provide high-confident pseudo supervisions for each category. In addition, we introduce pixel-wise contrastive learning on the common embedding space from both labeled and unlabeled data domains to further facilitate the semantic dependencies among category features, in which the reliable labels are leveraged as guidance for pixel sample selection. We verify the proposed framework on two benchmark land-use/land-cover datasets, and the experimental results demonstrate its competitive performance to other state-of-the-art semi-supervised methods.