Routes to relevance: Philosophies of relevant logics

Abstract: Abstract Relevant logics are a family of non‐classical logics characterized by the behavior of their implication connectives. Unlike some other non‐classical logics, such as intuitionistic logic, there are multiple philosophical views motivating relevant logics. Further, different views seem to motivate different logics. In this article, we survey five major views motivating the adoption of relevant logics: Use Criterion, sufficiency, meaning containment, theory construction, and truthmaking. We highlight the philosophical differences as well as the different logics they support. We end with some questions for future research.


Nonlinear causal network learning via Granger causality based on extreme support vector regression

Abstract: For complex networked systems, based on the consideration of nonlinearity and causality, a novel general method of nonlinear causal network learning, termed extreme support vector regression Granger causality (ESVRGC), is proposed. The nonuniform time-delayed influence of the driving nodes on the target node is particularly considered. Then, the restricted model and the unrestricted model of Granger causality are, respectively, formulated based on extreme support vector regression, which uses the selected time-delayed components of system variables as the inputs of kernel functions. The nonlinear conditional Granger causality index is finally calculated to confirm the strength of a causal interaction. Generally, based on the simulation of a nonlinear vector autoregressive model and nonlinear discrete time-delayed dynamic systems, ESVRGC demonstrates better performance than other popular methods. Also, the validity and robustness of ESVRGC are also verified by the different cases of network types, sample sizes, noise intensities, and coupling strengths. Finally, the superiority of ESVRGC is successful verified by the experimental study on real benchmark datasets.


Exploiting negative correlation for unsupervised anomaly detection in contaminated time series

Abstract: Anomaly detection in time series data is crucial for many fields such as healthcare, meteorology, and industrial fault detection. However, traditional unsupervised time series anomaly detection methods suffer from biased anomaly measurement under contaminated training data. Most of existing methods employ hard strategies for contamination calibration by assigning pseudo-label to training data. These hard strategies rely on threshold selection and result in suboptimal performance. To address this problem, in this paper, we propose a novel unsupervised anomaly detection framework for contaminated time series (NegCo), which builds an effective soft contamination calibration strategy by exploiting the observed negative correlation between semantic representation and anomaly detection inherent within the autoencoder framework. We innovatively redefine anomaly detection in data contamination scenarios as an optimization problem rooted in this negative correlation. To model this negative correlation, we introduce a dual construct: morphological similarity captures semantic distinctions relevant to normality, while reconstruction consistency quantifies deviations indicative of anomalies. Firstly, the morphological similarity is effectively measured based on the representative normal samples generated from the center of the learned Gaussian distribution. Then, an anomaly measurement calibration loss function is designed based on negative correlation between morphological similarity and reconstruction consistency, to calibrate the biased anomaly measurement caused by contaminated samples. Extensive experiments on various time series datasets show that the proposed NegCo outperforms state-of-the-art baselines, achieving an improvement of 6.2% to 26.8% in Area Under the Receiver Operating Characteristics (AUROC) scores, particularly in scenarios with heavily contaminated training data.


Diffusion model-based text-guided enhancement network for medical image segmentation

Abstract: In recent years, denoising diffusion models have achieved remarkable success in generating pixel-level representations with semantic values for image generation modeling. In this study, we propose a novel end-to-end framework, called TGEDiff, focusing on medical image segmentation. TGEDiff fuses a textual attention mechanism with the diffusion model by introducing an additional auxiliary categorization task to guide the diffusion model with textual information to generate excellent pixel-level representations. To overcome the limitation of limited perceptual fields for independent feature encoders within the diffusion model, we introduce a multi-kernel excitation module to extend the model’s perceptual capability. Meanwhile, a guided feature enhancement module is introduced in Denoising-UNet to focus the model’s attention on important regions and attenuate the influence of noise and irrelevant background in medical images. We critically evaluated TGEDiff on three datasets (Kvasir-SEG, Kvasir-Sessile, and GLaS), and TGEDiff achieved significant improvements over the state-of-the-art approach on all three datasets, with F1 scores and mIoU improving by 0.88% and 1.09%, 3.21% and 3.43%, respectively, 1.29% and 2.34%. These data validate that TGEDiff has excellent performance in medical image segmentation. TGEDiff is expected to facilitate accurate diagnosis and treatment of medical diseases through more precise deconvolutional structural segmentation.


The Case for Boosting Mobile Application QoE via Smart Band Switching in 5G/xG Networks

Abstract: 5G and future 6G networks support diverse combinations of access technologies, architectures, and radio frequencies, with each combination termed as a "band" henceforth. Through comprehensive measurements in 12 cities across 5 countries, we experimentally show that operator-configured default bands are often highly sub-optimal, particularly under mobility. We then propose smart band switching, where a UE's band can be dynamically changed to improve the network performance and boost the application QoE. We discuss challenges, opportunities, and design choices for building a practical smart band switching system. We further develop preliminary UE-side band-switching logic on commodity smartphones, and evaluate it on commercial 5G networks.


Crowdotic: A Privacy-Preserving Hospital Waiting Room Crowd Density Estimation with Non-speech Audio

Abstract: While passive audio recording of a crowd contains information about crowd density, such algorithm has limited applicability in hospital and clinical spaces with higher privacy expectations. In this preliminary work, we propose Crowdotic that uses a transformer-based model and exclusively non-speech audio segments for crowd density estimation in a hospital waiting room. To accomplish this, we deployed our sensor-based platform in the waiting room of a large hospital with IRB approval over a period of several months to capture non-speech audio and thermal images for the training and evaluation of our models. Our proposed approach shows that it is possible to achieve a reasonably high degree of accuracy with just a single microphone which can listen to non-speech based audio compared to other baselines. In addition to demonstrating superior performance without utilizing speech audio, we conduct further analysis using differential privacy techniques to provide additional privacy guarantees. Overall, our preliminary work demonstrates the viability of employing non-speech audio data for accurate occupancy estimation, while also ensuring the exclusion of speech-related content and providing robust privacy protections through differential privacy guarantees.


Challenges and opportunities in onboarding smart-home devices

Abstract: Smart-home devices have become integral to daily routines, but their onboarding procedures - setting up a newly acquired smart device into operational mode - remain understudied. The heterogeneity of smart-home devices and their onboarding procedure can easily overwhelm users when they scale up their smart-home system. While Matter, the new IoT standard, aims to unify the smart-home ecosystem, it is still evolving, resulting in mixed compliance among devices. In this paper, we study the complexity of device onboarding from users' perspectives. We thus performed cognitive walkthroughs on 12 commercially available smart-home devices, documenting the commonality and distinctions of the onboarding process across these devices. We found that onboarding smart home devices can often be tedious and confusing. Users must devote significant time to creating an account, searching for the target device, and providing Wi-Fi credentials for each device they install. Matter-compatible devices are supposedly easier to manage, as they can be registered through one single hub independent of the vendor. Unfortunately, we found such a statement is not always true. Some devices still need their own companion apps and accounts to fully function. Based on our observations, we give recommendations about how to support a more user-friendly onboarding process.


CodeQueries: A Dataset of Semantic Queries over Code

Abstract: Developers often have questions about semantic aspects of code they are working on, e.g., “Is there a class whose parent classes declare a conflicting attribute?”. Answering them requires understanding code semantics such as attributes and inheritance relation of classes. An answer to such a question should identify code spans constituting the answer (e.g., the declaration of the subclass) as well as supporting facts (e.g., the definitions of the conflicting attributes). The existing work on question-answering over code has considered yes/no questions or method-level context. We contribute a labeled dataset, called CodeQueries, of semantic queries over Python code. Compared to the existing datasets, in CodeQueries, the queries are about code semantics, the context is file level and the answers are code spans. We curate the dataset based on queries supported by a widely-used static analysis tool, CodeQL, and include both positive and negative examples, and queries requiring single-hop and multi-hop reasoning.


An Approach for Providing Recommendation for Requirements Non-Conformant with Requirement Templates (RTs)

Abstract: RTs generally possess a fixed syntactic structure and comprise pre-defined slots, and requirements written in the format of RTs must conform with the template structure. Suppose the requirements do not conform to the RT. In that case, manually verifying the conformity of requirements to RTs becomes a tedious task due to the large size of industry requirement documents and introduces the possibility of errors. Furthermore, rewriting requirements to conform to the template structure when they initially do not conform presents a significant challenge. This paper proposes a tool-based approach that automatically verifies whether Functional Requirements (FRs) conform to RTs. It recommends a Template Conformance (TC) requirement by generating a semantically identical requirement that Conforms to the template structure. Our study focused on two well-known RTs, Easy Approach to Requirements Syntax (EARS) and RUPPs, for checking conformance and making recommendations. We utilized Natural Language Processing (NLP) techniques and applied our approach to industrial and publicly available case studies. Our results demonstrate that the proposed tool-based approach facilitates requirement analysis and aids in recommending requirements based on their conformity with RTs. Our results show an accuracy of 83.9% for providing recommendations to non-conformant requirements with RTs.


Accelerating Software Development Using Generative AI: ChatGPT Case Study

Abstract: The Software Development Life Cycle (SDLC) comprises multiple phases, each requiring Subject Matter Experts (SMEs) with phase-specific skills. The efficacy and quality of deliverables of each phase are skill dependent. In recent times, Generative AI techniques, including Large-scale Language Models (LLMs) like GPT, have become significant players in software engineering. These models, trained on extensive text data, can offer valuable contributions to software development. Interacting with LLMs involves feeding prompts with the context information and guiding the generation of textual responses. The quality of the response is dependent on the quality of the prompt given. This paper proposes a systematic prompting approach based on meta-model concepts for SDLC phases. The approach is validated using ChatGPT for small but complex business application development. We share the approach and our experience, learnings, benefits obtained, and the challenges encountered while applying the approach using ChatGPT. Our experience indicates that Generative AI techniques, such as ChatGPT, have the potential to reduce the skills barrier and accelerate software development substantially.


Poster: Fusing Event and Depth Sensing for Dynamic Objects Localization and Tracking

Abstract: Background and motivation. The localization and tracking of high-speed dynamic objects find applications across various domains. For example, tracking unmanned aerial vehicles (UAVs) serves a crucial role for regulatory authorities, enabling the swift identification of illicitly operated drones.


A Contrastive Explanation Tool for Plans in Hybrid Domains

Abstract: This paper presents a tool for generating contrastive explanations of plans in hybrid domains. The tool offers a collection of contrastive questions over a plan, such as "Why use action A?", for users to select. An explanation of the user question is produced by contrasting the original plan against an alternative that meets the user’s expectation implicit from the question. The tool has the provision to contrast with the best alternative that the underlying planner can generate in terms of plan makespan (duration of a plan) and length. The current version supports two planners in the hybrid domain, namely SMTPlan+, and ENHSP, which a tool user can select. The tool consists of (1) A web-based interactive GUI for selecting questions, viewing contrastive plans and the generated explanations, and (2) A back-end implementing an iterative re-modeling and re-planning algorithm. We demonstrate the working of the tool over two case studies.


A Codebert Based Empirical Framework for Evaluating Classification-Enabled Vulnerability Prediction Models

Abstract: Software vulnerabilities are discovered quite frequently and cause substantial damage and security breaches. In the realm of cybersecurity, the detection of vulnerabilities plays a pivotal role in safeguarding critical systems and data. In this paper, we evaluate the efficacy of the source code embeddings obtained from the pre-trained CodeBERT model in detecting vulnerable code snippets and perform a comparative analysis with different data pre-processing and ML classification algorithms to recommend the best techniques for vulnerability detection. Precisely, we investigated the predictive ability of 540 different models developed using the CodeBERT embeddings, five feature selection techniques, a class balancing technique, and fifteen classification algorithms. Unlike regular deep learning-based models, these models are quick to implement and require minimal computational resources. The models are evaluated on robustness in detecting codes with known and previously unseen vulnerabilities. The developed models display high average accuracy and AUC, proving the efficiency of the CodeBERT embeddings in capturing vulnerabilities. We find that ensembles of decision trees (RF and EXTR) perform the best for vulnerability detection, which can be attributed to the advantages of ensembling. Furthermore, the effectiveness of class balancing and feature selection techniques as data pre-processing techniques is evaluated using box plots and the Friedman test. The significance of this research lies in its contribution to the field of cybersecurity by introducing reliable and efficient vulnerability detection methods.


A Hierarchical Attention Networks based Model for Bug Report Prioritization

Abstract: Software is becoming increasingly complex, leading to a surge in bugs. Bug reports typically contain essential details for bug reproduction and resolution. Currently, a triage engineer manually reads bug reports, classifies the bugs, and assigns priorities to the critical bugs. This task is time-consuming. While machine learning and deep learning models have been developed to automate this task, some have shown subpar performance, and others, although promising, have demonstrated inconsistency when applied to new bug reports. This paper proposes a Hierarchical Attention Network (HAN) model for prioritizing software bug reports. The bug reports are preprocessed through text normalization, tokenization using the DistilBERT tokenizer, and attention mechanisms to capture hierarchical relationships within the text. The model architecture incorporates bidirectional GRU layers and attention mechanisms at both word and sentence levels. The proposed model is evaluated on four software projects using precision, recall, and F1-score measures. Next, the proposed model is compared with convolution neural networks (CNN), support vector machines (SVM), random forests, and logistic regression techniques. The results showed that the proposed HAN model performed better than the other techniques.


SecSEC: Securing Smart Ethereum Contracts

Abstract: Smart contracts are a driving force for the Ethereum blockchain. A smart contract is a code that resides on blockchain and executes when certain predetermined conditions are satisfied. Ethereum smart contracts handle ether (a cryptocurrency) equivalent to millions of dollars and other essential assets. A bug in the smart contract can cause financial loss and damage to essential assets. The Ethereum community has developed ample tools to detect bugs in smart contracts. However, the tools generate false reports. We plan to integrate existing tools and detect smart contract bugs by combining the best of each tool. We use the Logistic Regression model to combine the tools. We are working on a training dataset of around 40K real-world Ethereum smart contracts labelled with vulnerabilities for the Logistic Regression model. We also plan to improve individual tools by finding the drawbacks of each tool. Finally, we aim to develop a new tool that will be better than existing tools regarding precision and recall.


Construction of two-qubit gates using B Gate

Abstract: Abstract In the circuit model of quantum computation, an entangling two-qubit gate and a set of single-qubit gates are used as universal gate set or basis gates for doing quantum computation. CNOT and <?CDATA $\sqrt{\mathrm{iSWAP}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi>iSWAP</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gates, the perfect entanglers that can create maximally entangled two-qubit states in one application, are broadly used entangling two-qubit basis gates in quantum computers. In this paper, we analyze the potentiality of <?CDATA $\sqrt{{\rm{B}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gate, an unexplored non-perfect entangler of the form, <?CDATA $\exp \left(\tfrac{i\pi ({\hat{\sigma }}_{x}\otimes {\hat{\sigma }}_{x})}{8}+\tfrac{i\pi ({\hat{\sigma }}_{y}\otimes {\hat{\sigma }}_{y})}{16}\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>exp</mml:mi> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mstyle displaystyle="false"> <mml:mfrac> <mml:mrow> <mml:mi>i</mml:mi> <mml:mi>π</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>σ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> <mml:mo>⨂</mml:mo> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>σ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mrow> <mml:mn>8</mml:mn> </mml:mrow> </mml:mfrac> </mml:mstyle> <mml:mo>+</mml:mo> <mml:mstyle displaystyle="false"> <mml:mfrac> <mml:mrow> <mml:mi>i</mml:mi> <mml:mi>π</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>σ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:msub> <mml:mo>⨂</mml:mo> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>σ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mrow> <mml:mn>16</mml:mn> </mml:mrow> </mml:mfrac> </mml:mstyle> </mml:mrow> </mml:mfenced> </mml:math> , as an entangling two-qubit basis gate in quantum computers by studying its ability to generate other two-qubit gates. We derive a necessary condition for a two-qubit gate to be generated by n applications of <?CDATA $\sqrt{{\rm{B}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gate. Using this condition, we show that the gates that can be generated by two and three applications of <?CDATA $\sqrt{{\rm{B}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gate are contained in the 50% and 92.97% of the volume of Weyl chamber of two-qubit gates, respectively. We prove that two applications of <?CDATA $\sqrt{{\rm{B}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gate can generate both <?CDATA $\sqrt{\mathrm{SWAP}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi>SWAP</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> and <?CDATA ${\sqrt{\mathrm{SWAP}}}^{\dagger }$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:msqrt> <mml:mrow> <mml:mi>SWAP</mml:mi> </mml:mrow> </mml:msqrt> </mml:mrow> <mml:mrow> <mml:mo>†</mml:mo> </mml:mrow> </mml:msup> </mml:math> which is not possible for CNOT and <?CDATA $\sqrt{\mathrm{iSWAP}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi>iSWAP</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gates; further, we conjecture that three applications of <?CDATA $\sqrt{{\rm{B}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gate can generate all perfect entanglers. Finally, we discuss about the construction of a three independent parameter universal two-qubit quantum circuit using four <?CDATA $\sqrt{{\rm{B}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gates that can generate all two-qubit gates. In the end, we mention about the schemes to implement <?CDATA $\sqrt{{\rm{B}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> gate in ion-trap quantum computers.


Modeling matrix variate time series via hidden Markov models with skewed emissions

Abstract: Abstract Data collected today have increasingly become more complex and cannot be analyzed using regular statistical methods. Matrix variate time series data is one such example where the observations in the time series are matrices. Herein, we introduce a set of three hidden Markov models using skewed matrix variate emission distributions for modeling matrix variate time series data. Compared to the hidden Markov model with matrix variate normal emissions, the proposed models present greater flexibility and are capable of modeling skewness in time series data. Parameter estimation is performed using an expectation maximization algorithm. We then look at both simulated data and salary data for public Texas universities.


A Non-Convex Fractional-Order Differential Equation for Medical Image Restoration

Abstract: We propose a new non-convex fractional-order Weber multiplicative denoising variational generalized function, which leads to a new fractional-order differential equation, and prove the existence of a unique solution to this equation. Furthermore, the model is solved using the partial differential equation (PDE) method and the alternating direction multiplier method (ADMM) to verify the theoretical results. The proposed model is tested on some symmetric and asymmetric medical computerized tomography (CT) images, and the experimental results show that the combination of the fractional-order differential equation and the Weber function has better performance in medical image restoration than the traditional model.


Design and assessment of a virtual reality learning environment for firefighters

Abstract: The use of virtual reality (VR) in firefighter training is promising because it provides cost-effective, safe environments that arouse similar behavioral responses to real-life scenarios. However, the pedagogical potential of VR and its impact on learning outcomes compared to traditional methods is currently an under-explored area. This research investigates how well VR can support learning compared to traditional methods in the context of training firefighters in combating vegetation fires. A VR learning environment was developed, informed by a “design for learning” framework providing a pedagogical underpinning. A between-subjects experiment was conducted with 40 participants to measure the knowledge transfer of the VR learning environment against the official textbook. In addition, VR's theorized learning benefits of intrinsic motivation, situational interest, and self-efficacy were compared with textbook-based learning. Lastly, the design quality of the learning environment was assessed based on its learning and user experience. We employed a primarily quantitative approach to data collection and analysis, using a combination of knowledge test results and questionnaires, with supporting qualitative data from semi-structured interviews and observation notes to answer our hypotheses. The results found a significant difference between the knowledge transfer of both conditions, with textbook-based learning more effectively transferring factual and conceptual knowledge than VR. No significant difference was found in reported self-efficacy between the two conditions but was found in reported levels of intrinsic motivation and situational interest, which were higher in the VR condition. The design was found to have facilitated a good user and learning experience, assessed via questionnaire responses. During interviews, VR participants reported high levels of satisfaction with the experience, praising the hands-on learning approach and interactivity, while reporting frustration with the lack of knowledge reinforcement and initial difficulties with the controls. A key finding was that presence was found to be negatively associated with knowledge transfer, which we theorize to be caused by the novelty of the realistic VR environment distracting participants from the more familiar lesson content. This research contributes to the body of work related to knowledge transfer within VR in this domain while highlighting key pedagogical and design considerations that can be used to inform future design implementations.


Addressing durability in collaborative organising: Event atmospheres and polyrhythmic affectivity

Abstract: Collaborative organising is known to burn like a rocket: it thrives on intense passion, relationality and creativity but quickly falls into pieces. This article explores the underestimated role of events and their affective atmospheres to sustain collaborative work. Drawing insights from two ethnographic field studies within an open-source software community and a network of impact entrepreneurs, we introduce the notion of ‘polyrhythmic affectivity’ at the core of polycentric governance. It encapsulates how frictional reverberances between three atmospherically experienced affective intensities – togetherness, dissonance and mutuality – are able to maintain emergent yet enduring order. We argue that the collective motivational force of collaborative organising, can be stabilised through a process of ‘affective commoning’ to sustain collaborative atmospheres as shared creative resources.


Application of simulation and virtual reality to production learning

Abstract: Purpose This study explores the impact of new technologies, such as simulation and virtual reality, on the pedagogy and learning of engineering students. It aims to compare the effectiveness of these digital tools against traditional teaching methods in enhancing student learning experiences. Design/methodology/approach Utilizing a quantitative research approach, the study involved third-year engineering students from the “Production Management” course at the School of Engineering of Vitoria-Gasteiz. Data were collected through an ad hoc questionnaire and analyzed using SPSS software, focusing on student satisfaction, challenges in adopting new technologies and the evolving roles of students and teachers. Findings The research highlighted several key aspects. Firstly, it identified the need for adapting teaching methods to incorporate new technologies effectively. Secondly, the integration of simulation and virtual reality was found to facilitate a deeper understanding of real-world problems, as students could engage with these issues in a simulated, virtual environment. Finally, the study emphasized the importance of pedagogical approaches that leverage these technologies to increase student involvement and motivation. The results suggest a positive impact of digital tools on the learning process in engineering education. Research limitations/implications The study’s scope was limited to one course within a single institution, suggesting the need for broader research across various disciplines and educational settings. Originality/value This research offers valuable insights into the integration of simulation and virtual reality in engineering education, underscoring their potential to enhance the learning experience and knowledge acquisition among students.


An Algorithm for Measuring the Similarity of Histograms for Texture Image Segmentation

Abstract: A simple algorithm for measuring the similarity between multi-column histograms is presented. The proposed algorithm is intended for texture segmentation of images using histograms as texture features. The purpose of developing such a specialized algorithm is to more accurately determine the boundaries between neighboring texture segments. The algorithm is specially designed so that to express the similarity value as a percentage. The main peculiarity of the proposed algorithm is that when calculating the similarity value, it considers not only the corresponding histogram columns but also takes into account their neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The proposed algorithm was implemented as a computer program as an integral part of the image segmentation model. The efficiency of the histogram comparison algorithm is indirectly confirmed by the texture segmentation results of the image segmentation model in image processing experiments.


NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans

Abstract: Abstract Purpose This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). Methods We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared. Results The nnU-Net models significantly outperformed the MGA ( p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA’s median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]). Conclusions The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.


Enhancing Data Use Ontology (DUO) for health-data sharing by extending it with ODRL and DPV

Abstract: The Global Alliance for Genomics and Health is an international consortium that is developing the Data Use Ontology (DUO) as a standard providing machine-readable codes for automation in data discovery and responsible sharing of genomics data. DUO concepts, which are encoded using OWL, only contain the textual descriptions of the conditions for data use they represent, and do not specify the intended permissions, prohibitions, and obligations explicitly – which limits their usefulness. We present an exploration of how the Open Digital Rights Language (ODRL) can be used to explicitly represent the information inherent in DUO concepts to create policies that are then used to represent conditions under which datasets are available for use, conditions in requests to use them, and to generate agreements based on a compatibility matching between the two. We also address a current limitation of DUO regarding specifying information relevant to privacy and data protection law by using the Data Privacy Vocabulary (DPV) which supports expressing legal concepts in a jurisdiction-agnostic manner as well as for specific laws like the GDPR. Our work supports the existing socio-technical governance processes involving use of DUO by providing a complementary rather than replacement approach. To support this and improve DUO, we provide a description of how our system can be deployed with a proof of concept demonstration that uses ODRL rules for all DUO concepts, and uses them to generate agreements through matching of requests to data offers. All resources described in this article are available at: https://w3id.org/duodrl/repo.


Optimizing convolutional neural networks for IoT devices: performance and energy efficiency of quantization techniques

Abstract: Abstract This document addresses some inherent problems in Machine Learning (ML), such as the high computational and energy costs associated with their implementation on IoT devices. It aims to study and analyze the performance and efficiency of quantization as an optimization method, as well as the possibility of training ML models directly on an IoT device. Quantization involves reducing the precision of model weights and activations while still maintaining acceptable levels of accuracy. Using representative networks for facial recognition developed with TensorFlow and TensorRT, Post-Training Quantization and Quantization-Aware Training are employed to reduce computational load and improve energy efficiency. The computational experience was conducted on a general-purpose computer featuring an Intel i7-1260P processor and an NVIDIA RTX 3080 graphics card used as an accelerator. Additionally, a NVIDIA Jetson AGX Orin was used as an example of an IoT device. We analyze the feasibility of training on an IoT device, the impact of quantization optimization on knowledge transfer-trained models and evaluate the differences between Post-Training Quantization and Quantization-Aware Training in such networks on different devices. Furthermore, the performance and efficiency of NVIDIA’s inference accelerator (Deep Learning Accelerator - DLA, in its 2.0 version) available at the Jetson Orin architecture are studied. We concluded that the Jetson device is capable of performing training on its own. The IoT device can achieve inference performance similar to that of the more powerful processor, thanks to the optimization process, with better energy efficiency. Post-Training Quantization has shown better performance, while Quantization-Aware Training has demonstrated higher energy efficiency. However, since the accelerator cannot execute certain layers of the models, the use of DLA worsens both the performance and efficiency results.


Content and structure based attention for graph node classification

Abstract: Graph-structured data is ubiquitous in real-world applications, such as social networks, citation networks, and communication networks. Graph neural network (GNN) is the key to process them. In recent years, graph attention networks (GATs) have been proposed for node classification and achieved encouraging performance. It focuses on the content associated on nodes to evaluate the attention weights, and the rich structure information in the graph is almost ignored. Therefore, we propose a multi-head attention mechanism to fully employ node content and graph structure information. The core idea is to introduce the interactions in the topological structure into the existing GATs. This method can more accurately estimate the attention weights among nodes, thereby improving the convergence of GATs. Second, the mechanism is lightweight and efficient, requires no training to learn, can accurately analyze higher-order structural information, and can be strongly interpreted through heatmaps. We name the proposed model content- and structure-based graph attention network (CSGAT). Furthermore, our proposed model achieves state-of-the-art performance on a number of datasets in node classification. The code and data are available at https://github.com/CroakerShark/CSGAT.


Cooperative target estimation of strap-down missiles considering random measurement delay

Abstract: Aiming at the problem of cooperative target estimation for multiple strap-down missiles, this paper is concerned with developing a distributed cooperative estimation fusion algorithm considering the random transmission delay of observation data. As a stepping stone, the random measurement delay system with multiple strap-down missiles is transformed into a delay-free system with correlation noise in finite time intervals using the random observation delay model for strap-down missiles. Then, the distributed fusion technique without feedback is proposed based on the design of the best local filter for each strap-down sampling subsystem; To further enhance the robustness and stability of the cooperative target estimation system, the distributed fusion algorithm with feedback is suggested. Simulation in various cases and comparison studies are conducted to verify the effectiveness and robustness of the proposed scheme.


Monitoring and analysis of physical activity and health conditions based on smart wearable devices

Abstract: The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving.


Design and Practice of Japanese Interactive Teaching Systems in Colleges and Universities Under the Background of Big Data

Abstract: Relying on the background of big data, this paper introduces the blended teaching model into the secondary vocational Japanese oral classroom and explores whether the teaching model is conducive to the improvement of the secondary vocational Japanese oral learning effect and teaching effect. In order to make this research more scientific and effective, this paper refers to a large number of literature materials. First, the purpose, content, and methods of this research are clarified; secondly, the relevant literature is sorted out and summarized. The SPOC blended teaching design was carried out, and teaching experiments were carried out accordingly. Finally, the data collected during the experiment were compared and analyzed, and the research results were verified with objective data. Research has shown that the use of a Japanese language teaching system enhances students' learning experience, promotes effective use of time, and improves overall learning outcomes.


Process control of chemical dynamic system based on multi-strategy mayfly optimization algorithm

Abstract: Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem.


Teaching the asynchronous general physics course in a crisis context: a case study at Igor Sikorsky Kyiv Polytechnic Institute

Abstract: Abstract The imposition of martial law and power outages in Ukraine made the first semester of the 2022/2023 academic year at Igor Sikorsky Kyiv Polytechnic Institute a challenge for lecturers and students, especially in learning the general physics course. An asynchronous learning mode became the only possible form in such a situation, so the problem of increasing the efficiency of conducting a lecture, problem sessions, and laboratory work in general physics arose. This study aims to describe the implementation of the discipline of general physics (part 1) promoted in an asynchronous mode by lecturers of the Department of General Physics and Modeling of Physical Processes. We verified that in crisis conditions, it is essential to: (i) provide students with all the necessary material on Moodle course and YouTube channel, allowing them to learn the theoretical material, solve the homework problems, and analyse the data from laboratory work, (ii) give feedback and consultations to students via Zoom/Telegram chat upon request, (iii) give the possibility to perform the laboratory work both in the virtual regime and as YouTube demonstration. The results of the survey of 82 students who studied the general physics course taking into account the recommendations mentioned above revealed that the majority of the surveyed students were completely/to a greater extent satisfied with the level of organisation of asynchronous learning, as well as the level of knowledge obtained, which undoubtedly indicates the effectiveness and efficiency of the proposed approach.


Abstract: Cross-lingual event retrieval is an information retrieval task aimed at cross-lingual event retrieval among multiple languages to find text or documents related to a specific event. Specific to Chinese-Vietnamese cross-language event retrieval, it involves using Chinese as a query to retrieve Vietnamese documents related to the query event. The critical issue is how to efficiently align query and document representations with limited resources. Existing cross-language pre-training models are trained on large-scale multilingual corpora, but their training goals do not include explicit language alignment tasks. Due to the uneven distribution of training corpora between different languages, these models have The problem of language bias. Therefore, this linguistic bias is also inherited in cross-lingual retrieval based on these models. To solve this problem, this paper proposes a Chinese-Vietnamese cross-lingual event retrieval method based on knowledge distillation. This approach enables the model to learn good query-document matching features from monolingual retrieval by transferring knowledge from high-resource to low-resource languages. By enhancing the alignment between queries and documents in different languages in a shared semantic space, the method improves the performance of Chinese-Vietnamese cross-lingual event retrieval.


Abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition

Abstract: Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.


Context and Multi-Features-Based Vulnerability Detection: A Vulnerability Detection Frame Based on Context Slicing and Multi-Features

Abstract: With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we propose a framework called Context and Multi-Features-based Vulnerability Detection (CMFVD). CMFVD integrates source code graphs and textual sequences, using a novel slicing method called Context Slicing to capture contextual information. The framework combines graph convolutional networks (GCNs) and bidirectional gated recurrent units (BGRUs) with attention mechanisms to extract local semantic and syntactic information. Experimental results on Software Assurance Reference Datasets (SARDs) demonstrate CMFVD’s effectiveness, achieving the highest F1-score of 0.986 and outperforming other models. CMFVD offers a promising approach to identifying and rectifying security flaws in large-scale codebases.


Explainable data stream mining: Why the new models are better

Abstract: Explainable Machine Learning brings expandability, interpretability, and accountability to Data Mining Algorithms. Existing explanation frameworks focus on explaining the decision process of a single model in a static dataset. However, in data stream mining changes in data distribution over time, called concept drift, may require updating the learning models to reflect the current data environment. It is therefore important to go beyond static models and understand what has changed among the learning models before and after a concept drift. We propose a Data Stream Explanability framework (DSE) that works together with a typical data stream mining framework where support vector machine models are used. DSE aims to help non-expert users understand model dynamics in a concept drifting data stream. DSE visualizes differences between SVM models before and after concept drift, to produce explanations on why the new model fits the data better. A survey was carried out between expert and non-expert users on the effectiveness of the framework. Although results showed non-expert users on average responded with less understanding of the issue compared to expert users, the difference is not statistically significant. This indicates that DSE successfully brings the explanability of model change to non-expert users.


A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Abstract: Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.


Application of Perceptual Model-Driven Multimedia Information Retrieval Technology in English Teaching Management Systems

Abstract: The application of multimedia technology in college English classroom teaching is suitable for sports and mute with illustrations, excellent audio, and video. The application of multimedia in the field of education has become the focus of the current English education reform, and education has entered the industrial age and the information age. This research aims at teaching practice in order to obtain high-quality educational results. And gradually turn multimedia, a new way, into an effective teaching tool in English education. Based on the research on the demand of information perception for English teaching, this article studies the relationship between constant information perception and English teaching demand from the perspective of multimedia information perception. An evolving model of English teaching needs caused by the change of multimedia information perception due to the input of external resources is established. The study found that when the average consumer perception is low, the revenue of enterprises can be rapidly improved by investing external resources.


Braiding drive data processing through rote learning

Abstract: It is difficult for traditional data processing methods to make full use of the potential of braiding driven data, unable to quickly collect and preprocess data, and difficult to ensure the accuracy of data. Rote learning (RL) is part of the research field of artificial intelligence, which aims to enable computers to learn autonomously, just like humans. This allows understanding of relationships and patterns between data and helps computers process information quickly. In order to solve the problems of poor data integrity, slow data processing efficiency and poor information sharing in traditional data processing, and further optimize the braiding driven data processing technology, this paper combined RL with braiding driven data. Through the method of mechanical learning, the potential of weaving driving data is fully exerted, so that it can better cope with nonlinear relations and high-dimensional features. It used the effective method provided by the RL to process the braiding drive data, collect the data, and preprocessed the collected data to ensure the correctness of the data. It extracted the features of the data, which was convenient to classify the data according to its attributes. At the same time, this paper verified it by the steps of feature extraction, model training and data analysis. In order to test whether braiding drive data processing by RL can effectively solve the problems existing in traditional drive data technology, this paper tested the performance of compiled drive data processing, and the analysis results were as follows. The data integrity rate of braiding drive data was as low as 81%, which was much higher than that of traditional drive data processing. The recognition ability of data acquisition and matching was much higher than that of traditional drive data processing. Compared with the traditional drive data processing, the information sharing has been greatly improved. In terms of data processing efficiency, it is also much higher than the traditional drive data processing. It can be seen that the method of braiding drive data processing through RL effectively improves the accuracy of data processing. It strengthens the identification ability of data collection and matching, improves the sharing of information, enables users to obtain data and analyze it faster, and also improves the processing efficiency of data.


Early Perceptions of Teaching and Learning Using Generative AI in Higher Education

Abstract: This project explored perceptions of ChatGPT in higher education among students and faculty to assess teaching and learning implications of this Generative Artificial Intelligence (Generative AI)–based novel tool. Two theoretical frameworks inspired the project, including Diffusion of Innovation theory (Rogers, 1962) and Technology Acceptance Model (Davis, 1989). An online survey was completed by 380 participants ( N = 380). Participants indicated that they would not use ChatGPT to plagiarize but believed others would. When asked to rate the accuracy of ChatGPT's output, more than half took the incorrect output as correct/somewhat correct or could not tell whether it was correct or incorrect. Results varied based on participant demographics, including age, gender, and occupation. These findings support the need for data literacy. If Generative AI is to be used in higher education to aid in the learning process, it is imperative to continue teaching critical thinking.


Multi-Modal Enhancement Transformer Network for Skeleton-Based Human Interaction Recognition

Abstract: Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods.


Securing DILITHIUM Against Physical Attacks: Performances Analysis

Abstract: Post-quantum cryptography will be the future standard of cryptography resistant to quantum and classical computers. The migration to quantum-safe products is not without a significant impact on hardware designs and capacities. In addition to mathematical security, the security of implementations regarding physical attacks: side-channel and faults, has to be taken into account. In this paper, we present a performances analysis of a full masked implementation in software of DILITHIUM. We provide a quantitative analysis of the bottlenecks introduced by the countermeasures in different configurations: from full software to implementation using assembly code for Keccak core, and, AES in hardware for seed extension. This work contributes to define the specifications of future hardware capabilities of components providing quantum-safe security services.


Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data

Abstract: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self-reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in-depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time-consuming and costly but also require systematic and cost-effective approaches.The main objective of this study was to investigate the feasibility of training an end-to-end deep learning (DL) model by directly inputting time-series activity tracking and sleep data obtained from consumer-grade wrist-worn activity trackers to identify comorbid depression and anxiety.To enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non-time-series data. The basic structure of the DL model was designed to process mixed-input data and perform multi-label-based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short-term memory (LSTM), were applied to process the time-series data, and model selection was conducted by comparing the performances of the hyperparameters.This study achieved significant results in the multi-label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed-input DL model based on activity tracking data. The comparison of hyper-parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results.This study can be considered as the first to develop a mixed-input DL model based on activity tracking data for the multi-label identification of late-life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer-grade wrist-worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi-label classification framework for identifying the complex symptoms of depression and anxiety.


Digital Transformation of Media: Challenges and Opportunities

Abstract: This paper is the result of the analysis of the evolution of media landscape in the context of the development and spread of digital technologies. The article examines the qualitative impact of digitalization on the business and operational models of traditional print media in the context of the obvious need for organizations in this field to adapt to the requirements of the digital age. The research emphasizes the importance of trust as a cultural phenomenon and its key role in decision-making under conditions of information overload. Using analytical and systemic approaches, the authors of the study explore the scale and multidimensionality of the evolution of the media industry in a multifaceted way. The article also analyzes the current state and prospects of AI integration in media activities, in particular, such technology products as predictive analytics and content personalization, which are significantly changing the processes of information content creation and distribution. The authors also consider the shift from hierarchical content distribution to a model of co-production and co-consumption, and highlight the importance of content personalization and the impact of the platform economy on the business models of modern media. The authors conclude that, despite the challenges, print media can remain relevant and successful if they can pay sufficient attention to the quality and uniqueness of their content to maintain the trust of an audience that needs to effectively navigate the competitive digital environment.


To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Abstract: Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other's working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality- derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.


Use of eye tracking in medical education

Abstract: Eye tracking has become increasingly applied in medical education research for studying the cognitive processes that occur during the performance of a task, such as image interpretation and surgical skills development. However, analysis and interpretation of the large amount of data obtained by eye tracking can be confusing. In this article, our intention is to clarify the analysis and interpretation of the data obtained from eye tracking. Understanding the relationship between eye tracking metrics (such as gaze, pupil and blink rate) and cognitive processes (such as visual attention, perception, memory and cognitive workload) is essential. The importance of calibration and how the limitations of eye tracking can be overcome is also highlighted.


Documental interactivo y visualización de datos

Abstract: Software development has influenced cultural products for years, and documentaries have not been an exception in this revolution. From non-linear and interactive webdocs to participatory, co-created, and transmedia projects, the documen-tary genre has given rise to diverse narrative structures. We analyze how webdocs have devel-oped different strategies when integrating data visualization into their narratives and interfaces. Multimedia content and data visualizations have been gaining prominence over film, and projects that don't use video have been considered docu-mentaries. This has given rise to what we consider the germ of a new genre and a new set of narrative structures.


Science 4.0 as a Model of Scientific Activity in an Innovative Environment of Industry 4.0

Abstract: Technological innovations in the generation, storage, transmission, and processing of data have transformed traditional scientific activities and contributed to the accelerated development of the concept of “data-driven science.” The article analyzes the image of Science 4.0 in a new format since organizing and managing scientific activities with the widespread use of the Industry 4.0 technological platform has become relevant here. A four-level classification of stages in the development of science and the use of e-Science as a technological platform for Science 4.0 is proposed. The essence of Science 4.0 is revealed in the analysis of works devoted to scientific research with broad involvement of the internet of things, cyber-physical systems, artificial intelligence, cloud computing, big data analytics, and other intelligent solutions. Implementation of the proposed concept contributes to an increase in the efficiency of scientific research and supports solutions for the operational management of science.


On the Application of Digitized Virtual Reality Technology in the Teaching of Landscape Architecture Design

Abstract: Landscape garden design is an important design foundation course for undergraduate students of landscape architecture in China, but the current classroom teaching has the problem of limited spatial dimension of the course. This paper proposes a virtual scene model construction method through the computer software with the creation of the need to display the scene. This virtual reality technology has immersive, interactive, and risqué characteristics. This paper focuses on the key points of virtual reality technology, discusses the specific implementation method of virtual reality technology in the “landscape garden design” course, and introduces the interactive control principle. The results of the research show that virtual reality technology to assist teaching can cultivate students' spatial thinking and enhance students' spatial simulation experience, greatly reducing the cost of traditional teaching.


Talking to Multi-Party Conversational Agents in Advisory Services: Command-based vs. Conversational Interactions

Abstract: Interacting with a conversational agent (CA) is becoming a major paradigm for human-technology interaction. Yet, ways for interacting with CAs are still forming, especially in situations involving more than one human. Starting an interaction with a CA might involve a wakeword and command. Alternatively, it could become active based on implicit requests and context information. Hence, CA designers face a serious dilemma: explicit commands disturb a natural conversation flow, while implicit requests might cause inadequate CA behavior. This study explores this dilemma and discusses observations from a project featuring a CA for financial advisory services. Advisors initially envisioned a CA that ''blends with the background'' and acts on context information. However, when engaging with a CA, they used conversational interactions in one part of the encounter and command-based interactions in another. We discuss this observation and contrast it against previous literature. This insight has implications for design and research.


Adaptive Shrink and Shard Architecture Design for Blockchain Storage Efficiency

Abstract: One of the problems in the blockchain is the formation of increasingly large data (big data) because each block must store all the transactions it makes. With the problem of the appearance of extensive data (big data), many studies aim to maintain the data in small amounts. This research combines a sorting data technique and a proper compression technique to obtain efficient data storage on the blockchain. The result of this research is a blockchain platform called Adaptive Shrink and Shard Blockchain (AS2BC), which conceptually and computationally can minimize the use of storage space in the blockchain up to 22 times smaller.


Semi-supervised medical image classification via distance correlation minimization and graph attention regularization

Abstract: We propose a novel semi-supervised learning method to leverage unlabeled data alongside minimal annotated data and improve medical imaging classification performance in realistic scenarios with limited labeling budgets to afford data annotations. Our method introduces distance correlation to minimize correlations between feature representations from different views of the same image encoded with non-coupled deep neural networks architectures. In addition, it incorporates a data-driven graph-attention based regularization strategy to model affinities among images within the unlabeled data by exploiting their inherent relational information in the feature space. We conduct extensive experiments on four medical imaging benchmark data sets involving X-ray, dermoscopic, magnetic resonance, and computer tomography imaging on single and multi-label medical imaging classification scenarios. Our experiments demonstrate the effectiveness of our method in achieving very competitive performance and outperforming several state-of-the-art semi-supervised learning methods. Furthermore, they confirm the suitability of distance correlation as a versatile dependence measure and the benefits of the proposed graph-attention based regularization for semi-supervised learning in medical imaging analysis.


Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation

Abstract: Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.


A bidirectional workflow scheduling approach with feedback mechanism in clouds

Abstract: Workflow scheduling poses a significant challenge in cloud computing environments, offering the potential to optimize execution time and reduce economic costs. In this paper, we present BFWS, a bidirectional workflow scheduling approach with a feedback mechanism, designed to address the deadline-constrained cost-aware workflow scheduling problem. BFWS is built upon a bidirectional scheduling framework that facilitates resource allocation from both the forward and the backward directions of the workflow. This framework incorporates a dynamic sub-deadline prediction method, leveraging prior knowledge and real-time scheduling progress to enhance the accuracy of task sub-deadline predictions. Additionally, a cost-aware resource selection operator is integrated into the framework, enabling the selection of appropriate virtual machines based on considerations of cost and time constraints. To further enhance decision-making and optimize performance, we introduce a feedback mechanism that enables BFWS to adapt and improve over time. Through extensive experiments using real-world workflows, the superior performance of BFWS is validated compared to the state-of-the-art algorithms. Moreover, the effectiveness of the bidirectional scheduling approach and the feedback mechanism is demonstrated individually through rigorous validation experiments. These findings highlight the potential of BFWS in achieving efficient and effective workflow scheduling in cloud computing environments.


YOLO-DCNet

Abstract: Enhanced processors empower edge devices like smartphones for human detection, yet their application is constrained by algorithmic efficiency and precision. This paper introduces YOLO-DCNet, a lightweight neural network detector built upon YOLOv7-tiny. Incorporating a dynamic multi-head structural re-parameterization (DMSR) module within its backbone network enables effective processing of the features utilized in the model. To improve multi-scale feature aggregation, the model integrates a channel information compression and linear mapping (CLM) module into its feature pyramid architecture. Moreover, the optimization of training and inference performance is achieved by employing RepVGG blocks between the main computational modules of the model. Experimental data reveal that the enhanced YOLOv7-tiny model achieves a 31.7% faster inference speed and marginal gains of 0.7% in mAP@0.5 and 0.5% in mAP@0.5:0.95 over the original. This underscores the model's improved performance and applicability for real-time human detection on edge devices across diverse applications.


I mediatori dei ‘testi in trascrizione’ (<i>Transkriptionstexte</i>) turchi tra Levante ed Europa

Abstract: The so‑called ‘Turkish transcriptional texts’, i.e. Ottoman texts in non‑Arabic scripts, are important sources for the knowledge of the historical grammar, especially phonology, of the Ottoman language. The term was created by Turkologists specifically for texts written between the sixteenth and eighteenth centuries in the Latin alphabet. The texts were usually written by European travellers with no or little knowledge of Turkish, relying on information from native speakers. This essay focuses on how the authors of the texts obtained their linguistic material. A detailed linguistic analysis of selected passages will show that the mediators were often of Greek origin.


Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection

Abstract: The proliferation of media-sharing platforms has led to issues with illegally edited content and the distribution of pornography. To protect personal information, de-identification technologies are being developed to prevent facial identification. Existing de-identification methods directly alter the pixel values in the face region, leading to reduced feature representation and identification accuracy. This study aims to develop a method that minimizes the possibility of personal identification while effectively preserving important features for image- and video-copy-detection tasks, proposing a new deep-learning-based de-identification approach that surpasses traditional pixel-based alteration methods. We introduce two de-identification models using different approaches: one emphasizing the contours of the original face through feature inversion and the other generating a blurred version of the face using D2GAN (Dual Discriminator Generative Adversarial Network). Both models were evaluated on their performance in image- and video-copy-detection tasks before and after de-identification, demonstrating effective feature preservation. This research presents new possibilities for personal-information protection and digital-content security, contributing to digital-rights management and law enforcement.


Robust noise-aware algorithm for randomized neural network and its convergence properties

Abstract: The concept of randomized neural networks (RNNs), such as the random vector functional link network (RVFL) and extreme learning machine (ELM), is a widely accepted and efficient network method for constructing single-hidden layer feedforward networks (SLFNs). Due to its exceptional approximation capabilities, RNN is being extensively used in various fields. While the RNN concept has shown great promise, its performance can be unpredictable in imperfect conditions, such as weight noises and outliers. Thus, there is a need to develop more reliable and robust RNN algorithms. To address this issue, this paper proposes a new objective function that addresses the combined effect of weight noise and training data outliers for RVFL networks. Based on the half-quadratic optimization method, we then propose a novel algorithm, named noise-aware RNN (NARNN), to optimize the proposed objective function. The convergence of the NARNN is also theoretically validated. We also discuss the way to use the NARNN for ensemble deep RVFL (edRVFL) networks. Finally, we present an extension of the NARNN to concurrently address weight noise, stuck-at-fault, and outliers. The experimental results demonstrate that the proposed algorithm outperforms a number of state-of-the-art robust RNN algorithms.


Balanced self-distillation for long-tailed recognition

Abstract: In long-tailed recognition tasks, the knowledge distillation technology is widely adopted for improving performance of deep neural networks. These methods distill the knowledge from the pretrained teacher model to the student model, which enables higher long-tailed recognition accuracy. However, the dependence on accompanying assistive models complicates the single network’s training process in the need for large memory and time costs. In this work, we present Balanced Self-Distillation (BSD) to distill tail knowledge by a single network without the assistive models. Specifically, BSD distills knowledge between different distortions of the same samples to stimulate the representation learning potential of the single network and adopts a balanced class weight for shifting the distillation focus from head- to-tail classes. Comprehensive experimentation across diverse datasets, including CIFAR-10-LT, CIFAR-100-LT and TinyImageNet-LT, consistently outperforms robust baseline methods. Specifically, BSD achieves improvements of 8.13% on CIFAR-100-LT with an mbalance ratio of 100 compared to the baseline (cross entropy). Furthermore, the proposed method enables seamless integration with contemporary techniques like re-sampling, meta-learning, and cost-sensitive learning. It emerges as a versatile tool capable of effectively addressing the challenges of long-tailed scenarios.


How identity is evolving in a digital-first world

Abstract: The use of biometric-based digital IDs is growing worldwide. This article analyses the latest developments in digital identification, the likely next steps, and how to deal with the core concerns this evolution raises: ensuring the security and ethical use of the personal data involved.


Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems

Abstract: Partial differential equations (PDEs) have become an essential tool for modeling complex physical systems. Such equations are typically solved numerically via mesh-based methods, such as finite element methods, with solutions over the spatial domain. However, obtaining these solutions are often prohibitively costly, limiting the feasibility of exploring parameters in PDEs. In this paper, we propose an efficient emulator that simultaneously predicts the solutions over the spatial domain, with theoretical justification of its uncertainty quantification. The novelty of the proposed method lies in the incorporation of the mesh node coordinates into the statistical model. In particular, the proposed method segments the mesh nodes into multiple clusters via a Dirichlet process prior and fits Gaussian process models with the same hyperparameters in each of them. Most importantly, by revealing the underlying clustering structures, the proposed method can provide valuable insights into qualitative features of the resulting dynamics that can be used to guide further investigations. Real examples are demonstrated to show that our proposed method has smaller prediction errors than its main competitors, with competitive computation time, and identifies interesting clusters of mesh nodes that possess physical significance, such as satisfying boundary conditions. An R package for the proposed methodology is provided in an open repository.


Exploratory Analysis of Distributional Data Using the Quantile Method

Abstract: The quantile method transforms each complex object described by different histogram values to a common number of quantile vectors. This paper retraces the authors’ research, including a principal component analysis, unsupervised feature selection using hierarchical conceptual clustering, and lookup table regression model. The purpose is to show that this research is essentially based on the monotone property of quantile vectors and works cooperatively in the exploratory analysis of the given distributional data.


Universal quantum computing with qubits embedded in trapped-ion qudits

Abstract: Recent developments in qudit-based quantum computing, in particular with trapped ions, open interesting possibilities for scaling quantum processors without increasing the number of physical information carriers. Here, we propose a method for compiling quantum circuits in the case, where qubits are embedded into qudits of experimentally relevant dimensionalities, $d=3,...,8$, for the trapped-ion platform. In particular, we demonstrate how single-qubit, two-qubit, and multiqubit gates can be realized using single-qudit operations and the $\text{M}\o{}\text{lmer-S}\o{}\text{rensen}$ (MS) gate as a basic two-particle operation. We expect that our findings are directly applicable to the current generation of trapped-ion-based qudit processors.


Unleashing the benefits of smart grids by overcoming the challenges associated with low-resolution data

Abstract: Smart meters have been widely deployed worldwide, but there is an often-overlooked problem that remains unresolved: the data collected from these meters is of relatively low time resolution, hindering the realization of smart grid benefits. This perspective unfolds the roadblocks to achieving high-resolution data from a smart metering infrastructure. We highlight the loss of critical information, essential for many smart grid applications, due to low-resolution readings of residential consumer data. We then outline the main reasons behind the lack of high-resolution data, the tetralemma on balancing data collection, transmission, warehousing, and privacy concerns. Finally, we hypothesize a framework for data collection, maintenance, communication, and storage of smart utility meters data to obtain high-resolution records by tackling the challenges using a dictionary-based compression method and separately maintaining the compressed products at the user ends and data center.


Fast and reliable entanglement distribution with quantum repeaters: Principles for improving protocols using reinforcement learning

Abstract: Future quantum technologies such as quantum communication, quantum sensing, and distributed quantum computation, will rely on networks of shared entanglement between spatially separated nodes.In this work, we provide improved protocols/policies for entanglement distribution along a linear chain of nodes, both homogeneous and inhomogeneous, that take practical limitations into account.For a wide range of parameters, our policies improve upon previously known policies, such as the "swap-as-soon-as-possible" policy, with respect to both the waiting time and the fidelity of the end-to-end entanglement.This improvement is greatest for the most practically relevant cases, namely, for short coherence times, high link losses, and highly asymmetric links.To obtain our results, we model entanglement distribution using a Markov decision process, and then we use the Q-learning reinforcement learning (RL) algorithm to discover new policies.These new policies are characterized by dynamic, state-dependent memory cutoffs and collaboration between the nodes.In particular, we quantify this collaboration between the nodes.Our quantifiers tell us how much "global" knowledge of the network every node has, specifically, how much knowledge two distant nodes have of each other's states.In addition to the usual figures of merit, these quantifiers add an extra important dimension to the performance analysis and practical implementation of quantum repeaters.Finally, our understanding of the performance of large quantum networks is currently limited by the computational inefficiency of simulating them using RL or other optimization methods.The other main contribution of our work is to address this limitation.We present a method for nesting policies in order to obtain policies for large repeater chains.By nesting our RL-based policies for small repeater chains, we obtain policies for large repeater chains that improve upon the swap-as-soon-as-possible policy, and thus we pave the way for a scalable method for obtaining policies for long-distance entanglement distribution under practical constraints.


Towards Efficient Risky Driving Detection: A Benchmark and a Semi-Supervised Model

Abstract: Risky driving is a major factor in traffic incidents, necessitating constant monitoring and prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack of suitable data for detecting risky driving in traffic surveillance settings remains a significant challenge. To address this issue, Bayonet-Drivers, a pioneering benchmark for risky driving detection, is proposed. The unique challenge posed by Bayonet-Drivers arises from the nature of the original data obtained from intelligent monitoring and recording systems, rather than in-vehicle cameras. Bayonet-Drivers encompasses a broad spectrum of challenging scenarios, thereby enhancing the resilience and generalizability of algorithms for detecting risky driving. Further, to address the scarcity of labeled data without compromising detection accuracy, a novel semi-supervised network architecture, named DGMB-Net, is proposed. Within DGMB-Net, an enhanced semi-supervised method founded on a teacher–student model is introduced, aiming at bypassing the time-consuming and labor-intensive tasks associated with data labeling. Additionally, DGMB-Net has engineered an Adaptive Perceptual Learning (APL) Module and a Hierarchical Feature Pyramid Network (HFPN) to amplify spatial perception capabilities and amalgamate features at varying scales and levels, thus boosting detection precision. Extensive experiments on widely utilized datasets, including the State Farm dataset and Bayonet-Drivers, demonstrated the remarkable performance of the proposed DGMB-Net.


Research on data security in industry 4.0 manufacturing industry against the background of privacy protection challenges

Abstract: As the manufacturing industry advances into the era of Industry 4.0, data security has emerged as a critical concern in the face of growing privacy protection challenges. This research paper explores data security challenges in the Industry 4.0 manufacturing industry and the associated privacy protection concerns. It highlights the risks of interconnected systems and data-driven processes while emphasizing the need for a comprehensive framework. The proposed framework encompasses technological, organizational, and regulatory measures to safeguard data at all stages. The study also examines legal and ethical considerations and suggests strategies to enhance privacy protection. The research aims to provide insights for stakeholders in the manufacturing industry, policymakers, and researchers to strengthen data security practices and uphold privacy rights in the Industry 4.0 landscape.


Perspective Chapter: Experimental Analysis of Black Hole Algorithm with Heuristic Algorithms in Traveling Salesman Problem

Abstract: Black hole algorithm (BHA) is a popular metaheuristic algorithm proposed and applied for data clustering in 2013. BHA was applied to continuous and discrete problems; it is also hybridized with some algorithms in the literature. The pure BHA shows better performance than others in discrete optimization, such as traveling salesman problems. However, it requires improving the algorithm with competitive heuristics. Many heuristics have often been used to construct the initial tour of a salesman, such as the nearest neighbor algorithm (NN), nearest insertion algorithm (NI), cheapest insertion algorithm (CI), random insertion algorithm (RI), furthest insertion algorithm (FI), and minimal spanning tree algorithm (MST). In addition, the black hole algorithm is combined with popular heuristics, such as swap/or insert, reverse/or 2-opt swap, and swap-reverse/or 3-opt swap, and tested with proper parameters in this study. In the experimentation, classical datasets are used via TSP-library. The experimental results are given as best, average solutions/or deviations, and CPU time for all datasets. Besides, the hybrid algorithms demonstrate a better performance rate to get optimality. Finally, hybrid algorithms solve the discrete optimization problem in a short computing time for all datasets.


Bayesian multi-proxy reconstruction of early Eocene latitudinal temperature gradients

Abstract: Abstract. Accurately reconstructing large-scale palaeoclimatic patterns from sparse local records is critical for understanding the evolution of Earth's climate. Particular challenges arise from the patchiness, uneven spatial distribution, and disparate nature of palaeoclimatic proxy records. Geochemical data typically provide temperature estimates via transfer functions derived from experiments. Similarly, transfer functions based on the climatic requirements of modern taxa exist for some fossil groups, such as pollen assemblages. In contrast, most ecological and lithological data (e.g. coral reefs and evaporites) only convey information on broad climatic requirements. Historically, most large-scale proxy-based reconstructions have used either geochemical or ecological data, but few studies have combined multiple proxy types into a single quantitative reconstruction. Large spatial gaps in existing proxy records have often been bridged by simple averaging, without taking into account the spatial distribution of samples, leading to biased temperature reconstructions. Here, we present a Bayesian hierarchical model to integrate ecological data with established geochemical proxies into a unified quantitative framework, bridging gaps in the latitudinal coverage of proxy data. We apply this approach to the early Eocene climatic optimum (EECO), the interval with the warmest sustained temperatures of the Cenozoic. Assuming the conservation of thermal tolerances of modern coral reefs and mangrove taxa, we establish broad sea surface temperature ranges for EECO coral reef and mangrove sites. We integrate these temperature estimates with the EECO geochemical shallow marine proxy record to model the latitudinal sea surface temperature gradient and global average temperatures of the EECO. Our results confirm the presence of a flattened latitudinal temperature gradient and unusually high polar temperatures during the EECO, which is supported by high-latitude ecological data. We show that integrating multiple types of proxy data, and adequate prior information, has the potential to enhance quantitative palaeoclimatic reconstructions, improving temperature estimates from datasets with limited spatial sampling.


Doubly Flexible Estimation under Label Shift

Abstract: In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q which only has partial data. We consider the setting when both outcome Y and covariate X are available from P but only X is available from Q, under the label shift assumption; i.e., the conditional distribution of X given Y is the same in the two populations. To estimate the parameter of interest in Q by leveraging information from P, three ingredients are essential: (a) the common conditional distribution of X given Y, (b) the regression model of Y given X in P, and (c) the density ratio of the outcome Y between the two populations. We propose an estimation procedure that only needs some standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the model misspecifications of both (b) and (c). This is conceptually different from the well-known doubly robust estimation in that, double robustness allows at most one model to be misspecified whereas our proposal can allow both (b) and (c) to be misspecified. This is of particular interest in label shift because estimating (c) is difficult, if not impossible, by virtue of the absence of the Y-data from Q. While estimating (b) is occasionally off-the-shelf, it may encounter issues related to the curse of dimensionality or computational challenges. We develop the large sample theory for the proposed estimator, and examine its finite-sample performance through simulation studies as well as an application to the MIMIC-III database.