Reviewerly/exHarmony huggingface dataset:
Resilience of navigation strategy and efficiency to the impact of acute stress

Increased cortisol may differentially impact navigation strategies. Three within-subjects experiments investigated how the Trier Social Stress Test (TSST), the Cold Pressor Test (CPT), and a prolonged bout of physical exercise (PE), all known to increase cortisol, affect navigation strategy and efficiency. Participants learned an environment, were stressed, and then navigated. Cortisol was higher in all stress sessions relative to a no-stress control session. TSST and CPT did not affect strategy or efficiency, while PE increased shortcut use. Results suggest navigation strategies are resilient to cortisol-based stress however questions remain as to how strategy shifts occur.
Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network

Abstract Background Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. Methods Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. Results Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P &amp;lt; .05). Conclusions Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.
Solving the Lipkin model using quantum computers with two qubits only with a hybrid quantum-classical technique based on the generator coordinate method

The possibility of using the generator coordinate method (GCM) using hybrid quantum-classical algorithms with reduced quantum resources is discussed. The task of preparing the basis states and calculating the various kernels involved in the GCM is assigned to the quantum computer, while the remaining tasks, such as finding the eigenvalues of a many-body problem, are delegated to classical computers for post-processing the generated kernels. This strategy reduces the quantum resources required to treat a quantum many-body problem. We apply the method to the Lipkin model. Using the permutation symmetry of the Hamiltonian, we show that, ultimately, only two qubits is enough to solve the problem regardless of the particle number. The classical computing post-processing leading to the full energy spectrum can be made using standard generalized eigenvalues techniques by diagonalizing the so-called Hill-Wheeler equation. As an alternative to this technique, we also explored how the quantum state deflation method can be adapted to the GCM problem. In this method, variational principles are iteratively designed to access the different excited states with increasing energies. The methodology proposed here is successfully applied to the Lipkin model with a minimal size of two qubits for the quantum register. The performances of the two classical post-processing approaches with respect to the statistical noise induced by the finite number of measurements and quantum devices noise are analyzed. Very satisfactory results for the full energy spectra are obtained once noise corrections techniques are employed.
ITC-net-audio-5: an audio streaming dataset for application identification in network traffic classification

Abstract Objectives An essential aspect of network traffic classification is application identification. This involves capturing and analyzing the traffic patterns of applications. There are a few publicly available datasets that specifically capture streaming data from network-based applications. Therefore, our objective is to generate an up-to-date dataset with a focus on audio streaming data. This dataset can be a valuable resource for identifying audio streaming applications in the field of network traffic classification. Data description The dataset contains network traffic captured during audio streaming communications on five trending applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud. It includes 500 files in PCAP format captured by Wireshark and PCAPdroid tools during voice calls and online music playback. The concurrent utilization of these tools facilitates the avoidance of capturing background traffic.
Data Sovereignty in Information Systems

Abstract Data has become a strategic asset for societal prosperity and economic competitiveness. There has long been an academic consensus that the value of data unfolds during its use. Consequently, many stakeholders have called for expanding the use and reuse of data, including the public and open variety, as well as that from private data providers. However, citizens and organizations want self-determination over their data use, that is, data sovereignty. This fundamentals paper applies a literature review to conceptualize the term in Information Systems (IS) research by summarizing current findings and definitions to add further structure to the field. It contributes to the current research streams by introducing a core conceptual model consisting of seven interacting core aspects, involving trust between data providers and consumers for data assets, supported by data infrastructure and contractual agreements on all data lifecycle stages. We evaluate and discuss this conceptual model through recent field examples and provide an overview of future research opportunities.
Big data challenges and opportunities in Internet of Vehicles: a systematic review

Purpose Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research. Design/methodology/approach This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis. Findings This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues. Research limitations/implications This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted. Originality/value This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.
People follow emotion display rules when choosing emoticons on social media

This study examined whether people use emoticons on Facebook in the same way they display emotions via facial expressions in face-to-face interaction. In an online experimental study (N = 124), participants completed the “Emoticon Display Rules on Facebook” (EDRF) scale adapted from the Display Rule Assessment Inventory (DRAI). People tend to use more positive emoticons in public settings, and more negative ones in private settings. They also use more and more intense emoticons with close friends and acquaintances than strangers. This study provides further support for the positivity bias on Facebook and shows that emoticons are simply used to express emotions in the same ways as in face-to-face interactions.
Multi-object tracking with adaptive measurement noise and information fusion

Multi-object tracking (MOT) is a challenging task in computer vision that aims to estimate the trajectories of multiple objects in a video sequence. Observation-Centric SORT (OCSORT) is a pure motion-based MOT algorithm that uses the Kalman filter as the motion model and three observation-centric techniques: Re-Update, Momentum and Recovery, to enhance the data association. However, OCSORT is limited by camera motion error, constant measurement noise and lack of appearance information. In this paper, we propose three methods to address these limitations and improve the performance of OCSORT. First, we use Enhanced Correlation Coefficient Maximization (ECC) to compensate for the camera motion between adjacent frames. Second, we adjust the measurement noise scale for the Kalman filter according to the detection confidence. Third, we introduce a deep visual feature model to extract appearance information and propose a method to effectively use both motion and appearance information. The proposed method first filters out the inappropriate appearance information based on motion information and then combines the filtered appearance information with the motion information by minimization. We evaluate our algorithm on three MOT benchmarks: MOT17, MOT20 and DanceTrack. The results show that our algorithm achieves state-of-the-art performance on all datasets, especially on DanceTrack, where the objects have highly nonlinear motion and frequent occlusion. Compared to OCSORT, our algorithm improves Higher Order Tracking Accuracy (HOTA) by 1.1%, 0.8%, and 3.5%, and ID F1 Score (IDF1) by 1.7%, 1.9%, and 4.3% on MOT17, MOT20 and DanceTrack, respectively.
The effectiveness of artificial intelligence (AI) on daily educational activities of undergraduates in a modern and diversified university environment

This study assessed the effectiveness of AI and AI-related technologies in the daily educational activities of undergraduate students in a modern and diversified university environment. The participants were 13 undergraduate Indonesian students participating in a mobility program for two weeks in Malaysia. A survey questionnaire designed with the help of the existing literature and ChatGPT, which includes ten (10) structured items and seven (7) open-ended questions, was used to collect the data. The relevant SPSS tests were used to analyze the data. Based on the results, of all 13 participants, 12 (92.3%) of them already experienced AI in their daily educational activities, and there were strong positive correlations between the attitudes toward AI and AI experiences, and attitudes toward AI and the effects of AI on education attributes, with correlation scores of .663 and .833 respectively. Based on the participant's answers to the qualitative questions, most of them believed that AI and AI technologies, such as ChatGPT, are helpful in daily educational activities and help them gain information regardless of time and space limitations and do their university-related assignments quickly. Based on the results, AI and AI-related technologies could transform different aspects of modern education.
Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence

Abstract The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.
Adaptive and fuzzy locality discriminant analysis for dimensionality reduction

Linear discriminant analysis (LDA) uses labeled samples for acquiring a discriminant projection direction, by which data of different categories are separated into distinct groups in a lower-dimensional subspace. In response to the issue that LDA lacks robustness to non-Gaussian data with rich local information, improvements on LDA explore the subspace manifold structure by building a fully connected similarity graph. However, these methods are vulnerable to the interference of noisy and redundant information. In this paper, we propose a new local LDA method, named adaptive and fuzzy locality discriminant analysis (AFLDA), which aims at extracting precise and compact features. Firstly, an adaptive and fuzzy k-means strategy is adopted, where the membership between data and corresponding subclass centers for each class is learned to establish a hybrid bipartite graph and capture local information. Secondly, we design a discrete and probability constraint imposed on the membership matrix to explore the intricate structure of multimodal data. Moreover, the subblock partition for each class makes data accommodate to multimodal subclasses. Thirdly, the maximum total scatter regularization term is introduced, which amply disperses the data to enhance the recognition of local structure and avoid trivial solutions. Finally, to eliminate the interference of noisy and redundant information, AFLDA learns an optimized subspace, where cluster centers and the membership matrix are updated alternately. Promising results in experiments illustrate the efficacy of the model.
Chasing snails: Automating the processing of EMCCD images of luminescence from opercula

Opercula of the gastropod Bithynia tentaculata are composed of calcite, and are typically 2–4 mm in length. They emit a thermoluminescence (TL) signal that can be used for dose reconstruction, and spatially resolved TL data from them can be obtained using an electron multiplying charge coupled device (EMCCD). However, when multiple measurements are made of the same sample with imaging detectors such as the EMCCD, registering the different images is crucial so that when regions of interest (ROI) are defined they consistently relate to the same part of the specimen. Previous work on opercula has undertaken this registration by hand, but this is prohibitively time consuming and is also potentially prone to human error. An automated registration process is described, and its use is illustrated using a dose recovery experiment. Without registration more than half of the regions of interest defined across the operculum failed the recycling test, and for those ROIs which did pass recycling the dose recovery ratio varied from 0.7 to 1.2. After registration more than 97% of ROIs passed recycling and all these ROIs gave dose recovery ratios within two sigma of unity. The automated registration process described here has potential for application to other types of solid sample such as rock slices provided they are not perfectly circular.
Efficient vacuum-state preparation for quantum simulation of strongly interacting local quantum field theories

The authors propose a method to traverse the path in the parameter space of a Hamiltonian for preparing ground states. It is particularly efficient for problems in which the path length in parameter space is long, such as quantum field theories with large volumes.
Transformational Application of Artificial Intelligence and Machine Learning in Financial Technologies and Financial Services: A Bibliometric Review

In this study, I employ a multifaceted comprehensive scientometric approach to explore the intellectual underpinnings of AI and ML in financial research by examining the publication patterns of articles, journals, authors, institutions, and nations by leveraging quantitative techniques, that transcend conventional systematic literature reviews, enabling the effective analysis of vast scientometric and bibliographic data. By applying these approaches, I identify influential works, seminal contributions, thought leaders, topical clusters, research streams, and new research frontiers, ultimately fostering a deeper understanding of the knowledge structure in AI and ML finance research by considering publication records from 2010 to 2022 from several search engines and database sources. The present study finds a marked increase in publications from 2017 to 2022, which highlights a growing interest and expanding research activity in the field, indicating its potential significance and relevance in the contemporary academic landscape.
Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work.
Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation

Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, overparameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training data set can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make overparameterized models cost sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known data sets and a pharmacy medication image (PMI) data set, made publicly available, show that our method can effectively minimize the overall cost and reduce critical errors while achieving comparable overall accuracy. Funding: Research reported in this publication was supported by the National Library of medicine of the National Institutes of Health in the United States under award number R01LM013624. Data Ethics &amp; Reproducibility Note: This paper abides by data ethics requirements. Data used are publicly available online at https://deepblue.lib.umich.edu/data/concern/data_sets/6d56zw997 . Codes to replicate the results of this paper are available at https://doi.org/10.24433/CO.2139841.v1 .
Unraveling metastable Markovian open quantum systems

We analyze the dynamics of metastable Markovian open quantum systems by unraveling their average dynamics into stochastic trajectories. We use quantum reset processes as examples to illustrate metastable phenomenology, including a simple three-state model whose metastability is of classical type, and a two-qubit model that features a metastable decoherence-free subspace. In the three-state model, the trajectories exhibit classical metastable phenomenology: fast relaxation into distinct phases and slow transitions between them. This extends the existing correspondence between classical and quantum metastability. It enables the computation of committors for the quantum phases, and the mechanisms of rare transitions between them. For the two-qubit model, the decoherence-free subspace appears in the unraveled trajectories as a slow manifold on which the quantum state has a continuous slow evolution. This provides a classical (nonmetastable) analog of this quantum effect. We discuss the general implications of these results, and we highlight the useful role of quantum reset processes for analysis of quantum trajectories in metastable systems.