Reviewerly/exHarmony huggingface dataset:
Multimodal Sentiment Analysis in Realistic Environments Based on Cross-Modal Hierarchical Fusion Network

In the real world, multimodal sentiment analysis (MSA) enables the capture and analysis of sentiments by fusing multimodal information, thereby enhancing the understanding of real-world environments. The key challenges lie in handling the noise in the acquired data and achieving effective multimodal fusion. When processing the noise in data, existing methods utilize the combination of multimodal features to mitigate errors in sentiment word recognition caused by the performance limitations of automatic speech recognition (ASR) models. However, there still remains the problem of how to more efficiently utilize and combine different modalities to address the data noise. In multimodal fusion, most existing fusion methods have limited adaptability to the feature differences between modalities, making it difficult to capture the potential complex nonlinear interactions that may exist between modalities. To overcome the aforementioned issues, this paper proposes a new framework named multimodal-word-refinement and cross-modal-hierarchy (MWRCMH) fusion. Specifically, we utilized a multimodal word correction module to reduce sentiment word recognition errors caused by ASR. During multimodal fusion, we designed a cross-modal hierarchical fusion module that employed cross-modal attention mechanisms to fuse features between pairs of modalities, resulting in fused bimodal-feature information. Then, the obtained bimodal information and the unimodal information were fused through the nonlinear layer to obtain the final multimodal sentiment feature information. Experimental results on the MOSI-SpeechBrain, MOSI-IBM, and MOSI-iFlytek datasets demonstrated that the proposed approach outperformed other comparative methods, achieving Has0-F1 scores of 76.43%, 80.15%, and 81.93%, respectively. Our approach exhibited better performance, as compared to multiple baselines.
Color image encryption scheme based on quantum random walk and chaotic mapping system

In this study, the quantum random walk is used to generate the initial conditions and control parameters of the chaotic mapping system. The R, G, and B channels of the color image are sorted according to the chaotic sequence generated by the Arcsin–Rrccos–Tent chaotic mapping. At the same time, the chaotic sequence generated by the improved logistic chaotic mapping is used to match it with the rules of DNA encoding and decoding. The chaotic sequence generated by the improved Henon chaotic mapping is used to determine the DNA rules and the number of cycles when the image is encrypted and decrypted. The XOR operation is performed by each pixel of the color image and the DNA complementary rule, and the cyclic iterative processing is performed. Extensive experimental results and analysis show that the scheme performs well in protecting image privacy and resisting attacks.
Performance Analysis of Free Text Keystroke Authentication Using XGBoost

Authentication based on keystroke dynamics is a form of behavioral biometric authentication that uses the user typing patterns and keyboard interaction as a discriminatory input. This type of authentication can be coupled with a fixed text password in a traditional login system to contribute to a multifactor authentication or provide continuous user authentication in a usable security system, where the typing patterns are continuously analysed to validate the user at run time. This paper investigates the effectiveness of free text keystroke for continuous authentication in real-world systems. Evaluation is performed using XGBoost multiclass classification, applied to an unbalanced free-text keystroke dataset. The introduction of additional activity-based features and removal of inaccuracies in the timing between keys allowed a reduction of the EER for the Clarkson II dataset from 14–24%, as achieved by previous studies, to 8% when employing the proposed method.
Metadata stewardship in nanosafety research: learning from the past, preparing for an “on-the-fly” FAIR future

Introduction: Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this “silo” approach is hampering their general adoption by the broader research community and individual labs. Methods: State-of-the-art data/knowledge collection, curation management FAIrification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users. Results: The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable “on-the-fly” (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, nonstandard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos. Discussion: The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferrable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability.
A Spatial Data-Driven Approach for Mineral Prospectivity Mapping

Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence points and existing mineral prospectivity maps developed using the weights-of-evidence (WofE) method. The ability to capture training points accurately and the simplicity of the proposed approach make it advantageous over complex mineral prospectivity mapping methods, to serve as a preliminary evaluation technique. The methodology can be modified with different datasets and algorithms, facilitating the investigations of mineral prospectivity in other regions and providing guidance for more detailed, high-resolution geological investigations.
Measure Construction by Extension in Dependent Type Theory with Application to Integration

We report on an original formalization of measure and integration theory in the Coq proof assistant. We build the Lebesgue measure following a standard construction that had not yet been formalized in proof assistants based on dependent type theory: by extension of a measure over a semiring of sets. We achieve this formalization by leveraging on existing techniques from the Mathematical Components project. We explain how we extend Mathematical Components' iterated operators and mathematical structures for analysis to provide support for infinite sums and extended real numbers. We introduce new mathematical structures for measure theory and incidentally provide an illustrative, concrete application of Hierarchy-Builder, a generic tool for the formalization of hierarchies of mathematical structures. This formalization of measure theory provides the basis for a new formalization of the Lebesgue integration compatible with the Mathematical Components project.
Satellite-Relayed Global Quantum Communication without Quantum Memory

Long-distance quantum communication can usher in hack-proof communication, enable precision quantum sensing technologies, and ultimately yield a quantum Internet. Existing protocols for global-scale quantum communication demand high-performance quantum memories, which have limited communication distance. This study proposes a chain of satellites in low Earth orbit to directly transmit photonic qubits through space, using the satellites like optical lenses to counter diffraction loss. Simulations and analysis of different quantum communication protocols using this relay system show the feasibility of building a global quantum network of only satellites, without requiring quantum memories.
Improving the speed of variational quantum algorithms for quantum error correction

We consider the problem of devising suitable quantum error correction (QEC) procedures for a generic quantum noise acting on a quantum circuit. In general, there is no analytic universal procedure to obtain the encoding and correction unitary gates, and the problem is even harder if the noise is unknown and has to be reconstructed. The existing procedures rely on variational quantum algorithms (VQAs) and are very difficult to train since the size of the gradient of the cost function decays exponentially with the number of qubits. We address this problem using a cost function based on the quantum Wasserstein distance of order 1 ($Q{W}_{1}$). At variance with other quantum distances typically adopted in quantum information processing, $Q{W}_{1}$ lacks the unitary invariance property which makes it a suitable tool to avoid getting trapped in local minima. Focusing on a simple noise model for which an exact QEC solution is known and can be used as a theoretical benchmark, we run a series of numerical tests that show how, guiding the VQA search through the $Q{W}_{1}$, can indeed significantly increase both the probability of a successful training and the fidelity of the recovered state, with respect to the results one obtains when using conventional approaches.
Trust based access control with hybrid cryptographic algorithm based data security on cloud for e-learning application

Due to the COVID-19 virus, many educational institutions now encourage online learning. The National Program of Technology Enabled Learning (NPTEL) is a web portal that is used for e-learning applications. With this online course, students can access the lectures of all the respected experts from the best universities at any time and from any location. Due to privacy and security issues, many educational systems are hesitant to adopt the cloud. To avoid security issues, in this paper, a trust-based access control data hybrid cryptography model is proposed. The proposed system mainly focused on data confidentiality and the authentication process. For data security, the hybrid Attribute-Based Encryption and Elliptical Curve Cryptography (ABE-ECC) algorithm is presented. Besides, for authentication, trust-based access control is introduced. The trust of the user is calculated using three parameters: the number of successful/failed interactions, the service satisfaction index, and the level of dishonesty. The performance of the proposed method is analyzed based on different metrics, namely throughput, latency, successful rate, service utilization, encryption time, decryption time, and retrieval time.
Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance on normal images may result in the learning of ineffective low-dimensional image representations that are not sensitive enough to detect and segment unseen abnormal lesions of varying size, appearance, and shape. Pre-training UAD methods with self-supervised learning, based on computer vision techniques, can mitigate this challenge, but they are sub-optimal because they do not explore domain knowledge for designing the pretext tasks, and their contrastive learning losses do not try to cluster the normal training images, which may result in a sparse distribution of normal images that is ineffective for anomaly detection. In this paper, we propose a new self-supervised pre-training method for MIA UAD applications, named Pseudo Multi-class Strong Augmentation via Contrastive Learning (PMSACL). PMSACL consists of a novel optimisation method that contrasts a normal image class from multiple pseudo classes of synthesised abnormal images, with each class enforced to form a dense cluster in the feature space. In the experiments, we show that our PMSACL pre-training improves the accuracy of SOTA UAD methods on many MIA benchmarks using colonoscopy, fundus screening and Covid-19 Chest X-ray datasets.
An obstacle avoidance approach for UAV path planning

The recent pandemic of COVID-19 has proven to be a test case for Unmanned Aerial Vehicles (UAVs). UAVs have shown great potential for plenty of applications in the face of this pandemic, but their scope of applications becomes limited due to the dependency on ground pilots. Irrespective of the application, it is imperative to have an autonomous path planning to utilize UAVs to their full potential. Collision-free trajectories are expected from the path planning process to ensure the safety of UAVs and humans on the ground. This work proposes a path planning technique where collision avoidance is mathematically proven under an uncertainty prerequisite, that the UAV follows its requested moving position within some threshold distance. This scheme ensures UAV safety by considering the underlying control’s system overshoots. Obstacles play a guiding role in selecting collision-free trajectories. These obstacles are modeled as rectangular shapes with interest points defined around their corners. These points further define collision-free permissible edges, and later we apply the Dijkstra algorithm to these edges before having the desired trajectory. Regardless of the size of deployment area, our proposed scheme incurs low computational load due to the dependency on pre-defined interest points only thereby making it suitable for real-time path planning. Simulation results obtained using MATLAB’s UAV Toolbox show that the proposed method succeeds in getting short collision-free trajectories.
A population-based approach for multi-agent interpretable reinforcement learning

Multi-Agent Reinforcement Learning (MARL) made significant progress in the last decade, mainly thanks to the major developments in the field of Deep Neural Networks (DNNs). However, DNNs suffer from a fundamental issue: their lack of interpretability. While this is true for most applications of DNNs, this is exacerbated in their applications in MARL. In fact, the mutual interactions between agents and environment, as well as across agents, make it particularly difficult to understand learned strategies in these settings. One possible way to achieve explainability in MARL is through the use of interpretable models, such as decision trees, that allow for a direct inspection and understanding of their inner workings. In this work, we make a step forward in this direction, proposing a population-based algorithm that combines evolutionary principles with RL for training interpretable models in multi-agent systems. We evaluate the proposed approach in a highly dynamic task where two teams of agents compete with each other. We test different variants of the proposed method in different settings, namely with/without coevolution and with/without initialization from a handcrafted policy. We find that, in most settings, our method is able to find fairly effective policies. Moreover, we show that the learned policies are easy to inspect and, possibly, interpreted based on domain knowledge.
Learning local graph from multiple kernels

Graph is a widely applied technique to characterize the relationship among data. Due to the excellent ability to handle nonlinear data and extract the useful information contained in base kernels, learning the connecting graph based on multiple kernel learning has been extensively discussed. Many existing algorithms construct the connecting graph based on the optimal kernel which is learned from base kernels. Observing these methods, we find they (1) ignore the local structure of data; (2) cannot assure that the optimal kernel is positive semi-definite; (3) cannot fully utilize the information contained in base kernels. Therefore, we introduce a novel local graph based on multiple kernel learning (LGMKL) in this paper. Specifically, LGMKL is constructed based on the optimal kernel which is automatically learned from base kernels with a nonlinear strategy and the information contained in different base kernels is also utilized in LGMKL. Then an iterative scheme with proven convergence is developed to optimize the objective function of LGMKL. Unlike most MKL-based graph learning methods, LGMKL focuses on the local structure of data. Finally, nine benchmark datasets and two synthetic datasets are adopted to test the performance of LGMKL. Extensive experiments demonstrate the superiorities of the proposed method.
Exploring the Intersection of Digital Innovation and Educational Reform

This study provides a comprehensive exploration of augmented reality (AR) in education, examining its benefits, applications, social implications, ethical considerations, and future directions. The objective of the study is to highlight the potential of AR as a transformative technology in educational settings. The findings reveal that AR offers numerous benefits in education, including enhanced student engagement, improved learning outcomes, personalized learning experiences, and collaborative interaction. The social implications of AR in education encompass equality, accessibility, and social engagement, while ethical considerations involve privacy, security, and pedagogical adaptation. Despite challenges like the digital divide and teacher training, AR offers opportunities and positive outcomes in education. Collaboration and research investment are crucial for advancing AR integration in education. AR has the potential to create immersive learning environments that cater to diverse student needs, enhancing their educational experiences.
Aligning Benchmark Datasets for Table Structure Recognition

Benchmark datasets for table structure recognition (TSR) must be carefully processed to ensure they are annotated consistently. However, even if a dataset's annotations are self-consistent, there may be significant inconsistency across datasets, which can harm the performance of models trained and evaluated on them. In this work, we show that aligning these benchmarks—removing both errors and inconsistency between them—improves model performance significantly. We demonstrate this through a data-centric approach where we adopt one model architecture, the Table Transformer (TATR), that we hold fixed throughout. Baseline exact match accuracy for TATR evaluated on the ICDAR-2013 benchmark is 65% when trained on PubTables-1M, 42% when trained on FinTabNet, and 69% combined. After reducing annotation mistakes and inter-dataset inconsistency, performance of TATR evaluated on ICDAR-2013 increases substantially to 75% when trained on PubTables-1M, 65% when trained on FinTabNet, and 81% combined. We show through ablations over the modification steps that canonicalization of the table annotations has a significantly positive effect on performance, while other choices balance necessary trade-offs that arise when deciding a benchmark dataset's final composition. Overall we believe our work has significant implications for benchmark design for TSR and potentially other tasks as well. Dataset processing and training code will be released at https://github.com/microsoft/table-transformer .
Zero-shot Generation of Training Data with Denoising Diffusion Probabilistic Model for Handwritten Chinese Character Recognition

There are more than 80,000 character categories in Chinese while most of them are rarely used. To build a high performance handwritten Chinese character recognition (HCCR) system supporting the full character set with a traditional approach, many training samples need be collected for each character category, which is both time-consuming and expensive. In this paper, we propose a novel approach to transforming Chinese character glyph images generated from font libraries to handwritten ones with a denoising diffusion probabilistic model (DDPM). Training from handwritten samples of a small character set, the DDPM is capable of mapping printed strokes to handwritten ones, which makes it possible to generate photo-realistic and diverse style handwritten samples of unseen character categories. Combining DDPM-synthesized samples of unseen categories with real samples of other categories, we can build an HCCR system to support the full character set. Experimental results on CASIA-HWDB dataset with 3,755 character categories show that the HCCR systems trained with synthetic samples perform similarly with the one trained with real samples in terms of recognition accuracy. The proposed method has the potential to address HCCR with a larger vocabulary.
Fine-Tuning is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition

In many machine learning tasks, a large general dataset and a small specialized dataset are available. In such situations, various domain adaptation methods can be used to adapt a general model to the target dataset. We show that in the case of neural networks trained for handwriting recognition using CTC, simple fine-tuning with data augmentation works surprisingly well in such scenarios and that it is resistant to overfitting even for very small target domain datasets. We evaluated the behavior of fine-tuning with respect to augmentation, training data size, and quality of the pre-trained network, both in writer-dependent and writer-independent settings. On a large real-world dataset, fine-tuning on new writers provided an average relative CER improvement of 25% for 16 text lines and 50% for 256 text lines.