Multi-scale V-net architecture with deep feature CRF layers for brain extraction

Abstract: Abstract Background Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between. Methods We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods. Results Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults. Conclusions Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.


Enhanced Identification of Fracture Smectites and Other Alteration Minerals Via Short-Wave Infrared Reflectance at Two Finnish Crystalline Sites, Olkiluoto and Hyrkkölä

Abstract: Abstract Fracture filling minerals and the smectite-group clay minerals in particular play a significant role in the geotechnical stability of the fractured rock mass. Smectites have multiple uses as geomaterials in waste management solutions, of which the most technically demanding is the geological disposal of radioactive waste. Here, the stability and occurrence of smectites as fracture filling minerals were studied with two main objectives: 1) to test the capability of short-wave infrared spectroscopy as a tool to streamline the site assessment where clay mineralogy needs to be characterised in detail, and 2) to provide a first assessment of naturally occurring smectites in various sample types collected from two Finnish sites (Olkiluoto and Hyrkkölä) and their potential as natural analogues of smectite stability for bentonite used in the engineered barrier systems of geological disposal repositories. The short-wave infrared (SWIR) method has been validated to be a useful tool in fracture mineral characterisation, specifically due to its fast acquisition and interpretation time compared to the conventional methods. The study confirms the in situ occurrence of smectites at both sites studied showing that, especially in Hyrkkölä, the smectite-group member is close to montmorillonite, the main constituent of bentonite. At Olkiluoto, the smectites studied show more interstratified features. The differences in the mode of occurrence are related to the history of their formation, especially hydrothermal alteration, and parent rock composition.


SFL-MDrone: Synchronous federated learning enabled multi drones

Abstract: UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs’ communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn’t prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy.


Influence of Digital Skills on The Academic Performance of University Students: A Socioeconomic Approach

Abstract:
Purpose: Analyzing the influence of digital competencies on the academic performance of university students through a socioeconomic approach.

 Theoretical framework: Theories associated with digital competence, academic performance and the influence of socioeconomic factors were addressed.

 Method: A mixed Ditriac-type approach was applied, with a non-experimental and cross-sectional design, where the total population was 1215 university students and the probabilistic sample for the survey was 292 students and the non-probabilistic sample for the interview was 29 students who served to collect quantitative and qualitative data respectively. Quantitative data were obtained through the COMPDIG-PED instrument of digital competence in pedagogy, whose reliability was assessed by Cronbach's Alpha coefficient α = 0.911 and academic performance was evaluated through documentary analysis of the institution's academic records, which were then processed in SPSS, and qualitative data were analyzed with Atlas.ti software considering 15 codes, 10 sub categories and 6 categories, also taking into account the socioeconomic aspect throughout the process, from the selection of participants to the interpretation of the results.

 Results and conclusions: The results support a moderate degree of positive correlation of digital competencies and academic performance with an rs = 0.546 and with a significance level p = 0.000, where with respect to the coefficient of determination it is shown that digital competency influences 30.5% on the academic performance of university students, while the remaining 69.5% is affected by other socioeconomic factors. Concluding that there is positive correlation and 30.5% of influence of digital competencies with respect to academic performance, where the remaining 69.5% is due to socioeconomic factors such as educational level in parents, economic level, work, sentimental relationship and the management of time dedicated to study.

 Originality/value: To provide insight into students' perceptions on the management of digital competencies and their influence on academic performance, integrating socioeconomic aspects.


Stabilizer Subsystem Decompositions for Single- and Multimode Gottesman-Kitaev-Preskill Codes

Abstract: The Gottesman-Kitaev-Preskill (GKP) error-correcting code encodes a finite-dimensional logical space in one or more bosonic modes, and has recently been demonstrated in trapped ions and superconducting microwave cavities. In this work we introduce a new subsystem decomposition for GKP codes that we call the stabilizer subsystem decomposition, analogous to the usual approach to quantum stabilizer codes. The decomposition has the defining property that a partial trace over the nonlogical stabilizer subsystem is equivalent to an ideal decoding of the logical state, distinguishing it from previous GKP subsystem decompositions. We describe how to decompose arbitrary states across the subsystem decomposition using a set of transformations that move between the decompositions of different GKP codes. Besides providing a convenient theoretical view on GKP codes, such a decomposition is also of practical use. We use the stabilizer subsystem decomposition to efficiently simulate noise acting on single-mode GKP codes, and in contrast to more conventional Fock basis simulations, we are able to consider essentially arbitrarily large photon numbers for realistic noise channels, such as loss and dephasing.5 MoreReceived 9 December 2022Revised 28 July 2023Accepted 10 January 2024DOI:https://doi.org/10.1103/PRXQuantum.5.010331Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionQuantum harmonic oscillatorQuantum simulationQuantum Information, Science & Technology


Data-driven discovery of statistically relevant information in quantum simulators

Abstract: Quantum simulators offer powerful means to investigate strongly correlated quantum matter. However, interpreting measurement outcomes in such systems poses significant challenges. Here, we present a theoretical framework for information extraction in synthetic quantum matter, illustrated for the case of a quantum quench in a spinor Bose-Einstein condensate experiment. Employing nonparametric unsupervised learning tools that provide different measures of information content, we demonstrate a theory-agnostic approach to identify dominant degrees of freedom. This enables us to rank operators according to their relevance, akin to effective field theory. To characterize the corresponding effective description, we then explore the intrinsic dimension of data sets as a measure of the complexity of the dynamics. This reveals a simplification of the data structure, which correlates with the emergence of time-dependent universal behavior in the studied system. Our assumption-free approach can be immediately applied in a variety of experimental platforms.


Style-Guided Adversarial Teacher for Cross-Domain Object Detection

Abstract: The teacher–student framework is widely employed for cross-domain object detection. However, it suffers from two problems. One is that large distribution discrepancies will cause critical performance drops. The other is that the samples that deviate from the overall distributions of both domains will greatly mislead the model. To solve these problems, we propose a style-guided adversarial teacher (SGAT) method for domain adaptation. Specifically, on the domain level, we generate target-like images based on source images to effectively narrow the gaps between domains. On the sample level, we denoise samples by estimating the probability density ratio of the ‘target-style’ and target distributions, which could filter out the unrelated samples and highlight the related ones. In this way, we could guarantee reliable samples. With these reliable samples, we learn the domain-invariant features through teacher–student mutual learning and adversarial learning. Extensive experiments verify the effectiveness of our method. In particular, we achieve 52.9% mAP on Clipart1k and 42.7% on Comic2k, which are 6.4% and 5.0% higher than the compared baselines.


Deep Machine Learning of MobileNet, Efficient, and Inception Models

Abstract: Today, specific convolution neural network (CNN) models assigned to specific tasks are often used. In this article, the authors explored three models: MobileNet, EfficientNetB0, and InceptionV3 combined. The authors were interested in investigating how quickly an artificial intelligence model can be taught with limited computer resources. Three types of training bases were investigated, starting with a simple base verifying five colours, then recognizing two different orthogonal elements, followed by more complex images from different families. This research aimed to demonstrate the capabilities of the models based on training base parameters such as the number of images and epoch types. Architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of the different models based on the TensorFlow and Keras libraries in the Jupiter environment based on the Python programming language. An artificial intelligence model with a combination of MobileNet, proposed by Siemens, and Efficient and Inception, selected by the authors, allows for further work to be conducted on image classification, but with limited computer resources for industrial implementation on a programmable logical controller (PLC). The study showed a 90% success rate, with a learning time of 180 s.


Improved Wildlife Recognition through Fusing Camera Trap Images and Temporal Metadata

Abstract: Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images alone is often limited by the size and quality of the dataset. To address the above issues, we propose the Temporal-SE-ResNet50 network, which aims to improve wildlife recognition accuracy by exploiting the temporal information attached to camera trap images. First, we constructed the SE-ResNet50 network to extract image features. Second, we obtained temporal metadata from camera trap images, and after cyclical encoding, we used a residual multilayer perceptron (MLP) network to obtain temporal features. Finally, the image features and temporal features were fused in wildlife identification by a dynamic MLP module. The experimental results on the Camdeboo dataset show that the accuracy of wildlife recognition after fusing the image and temporal information is about 93.10%, which is an improvement of 0.53%, 0.94%, 1.35%, 2.93%, and 5.98%, respectively, compared with the ResNet50, VGG19, ShuffleNetV2-2.0x, MobileNetV3-L, and ConvNeXt-B models. Furthermore, we demonstrate the effectiveness of the proposed method on different national park camera trap datasets. Our method provides a new idea for fusing animal domain knowledge to further improve the accuracy of wildlife recognition, which can better serve wildlife conservation and ecological research.


Adaptive weighted dictionary representation using anchor graph for subspace clustering

Abstract: Samples are commonly represented as sparse vectors in many dictionary representation algorithms. However, this method may result in loss of discriminatory information. Moreover, a redundant dictionary can increase the computational complexity of the algorithm. To tackle these challenges, we propose a novel method named Adaptive Weighted Dictionary Representation using Anchor Graph for Subspace Clustering (AWDR). First, AWDR constructs an anchor graph that encodes the classification information and establishes accurate connectivity components between anchors and clusters, thereby fully utilizing the discriminative information of the original samples. In addition, AWDR learns a complete-dictionary in the subspace to eliminate the noise and out-of-sample effects of the original sample space, while also improving computational efficiency. Finally, AWDR computes the coefficients for the samples in an adaptively weighted manner to find discriminative representation of the samples from the dictionary. Extensive experiments on real-world datasets demonstrate that our method is effective and efficient compared to the state-of-the-art methods.


Multi-Modal Sarcasm Detection with Sentiment Word Embedding

Abstract: Sarcasm poses a significant challenge for detection due to its unique linguistic phenomenon where the intended meaning is often opposite of the literal expression. Current sarcasm detection technology primarily utilizes multi-modal processing, but the connotative semantic information provided by the modality itself is limited. It is a challenge to mine the semantic information contained in the combination of sarcasm samples and external commonsense knowledge. Furthermore, as the essence of sarcasm detection lies in measuring emotional inconsistency, the rich semantic information may introduce excessive noise to inconsistency measurement. To mitigate these limitations, we propose a hierarchical framework in this paper. Specifically, to enrich the semantic information of each modality, our approach uses sentiment dictionaries to obtain the sentiment vectors by evaluating the words extracted from various modalities, and then combines them with each modality. Furthermore, in order to mine the joint semantic information implied in the modalities and improve measurement of emotional inconsistency, the emotional information representation obtained by fusing each modality’s data is concatenated with the sentiment vector. Then, cross-modal fusion is performed through cross-attention, and, finally, the sarcasm is recognized by fusing low-level information in the cross-modal fusion layer. Our model is evaluated on a public multi-modal sarcasm detection dataset based on Twitter, and the results demonstrate its superiority.


Efficiency optimization in quantum computing: balancing thermodynamics and computational performance

Abstract: We investigate the computational efficiency and thermodynamic cost of the D-Wave quantum annealer under reverse-annealing with and without pausing. Our demonstration on the D-Wave 2000Q annealer shows that the combination of reverse-annealing and pausing leads to improved computational efficiency while minimizing the thermodynamic cost compared to reverse-annealing alone. Moreover, we find that the magnetic field has a positive impact on the performance of the quantum annealer during reverse-annealing but becomes detrimental when pausing is involved. Our results, which are reproducible, provide strategies for optimizing the performance and energy consumption of quantum annealing systems employing reverse-annealing protocols.


ViT-UperNet: a hybrid vision transformer with unified-perceptual-parsing network for medical image segmentation

Abstract: Abstract The existing image semantic segmentation models have low accuracy in detecting tiny targets or multi-targets at overlapping regions. This work proposes a hybrid vision transformer with unified-perceptual-parsing network (ViT-UperNet) for medical image segmentation. A self-attention mechanism is embedded in a vision transformer to extract multi-level features. The image features are extracted hierarchically from low to high dimensions using 4 groups of Transformer blocks with different numbers. Then, it uses a unified-perceptual-parsing network based on a feature pyramid network (FPN) and a pyramid pooling module (PPM) for the fusion of multi-scale contextual features and semantic segmentation. FPN can naturally use hierarchical features, and generate strong semantic information on all scales. PPM can better use the global prior knowledge to understand complex scenes, and extract features with global context information to improve segmentation results. In the training process, a scalable self-supervised learner named masked autoencoder is used for pre-training, which strengthens the visual representation ability and improves the efficiency of the feature learning. Experiments are conducted on cardiac magnetic resonance image segmentation where the left and right atrium and ventricle are selected for segmentation. The pixels accuracy is 93.85%, the Dice coefficient is 92.61% and Hausdorff distance is 11.16, which are improved compared with the other methods. The results show the superiority of Vit-UperNet in medical images segmentation, especially for the low-recognition and serious-occlusion targets.


Towards data privacy in a fog of things

Abstract: Abstract Data from the Internet of Things (IoT) devices enable the design of new business models and services, improving user experience and satisfaction. However, these devices also collect personal data and place them on centralized servers without transparency and user control, enhancing data privacy concerns (such as identification, localization and tracking, profiling, and linkage). We propose the Fog of Things (FoT)‐Personal Data Stores (PDS) paradigm, an extension of the (FoT) paradigm tailored to protect personal data privacy in the IoT, specifically in the FoT. We propose a data decentralization solution through Personal Data Stores, empowering users with control over their data. We present an architectural framework based on this concept and an implementation of a usage scenario.


Long Horizon Episodic Decision Making for Cognitively Inspired Robots

Abstract: The Human decision-making process works by recollecting past sequences of observations and using them to decide the best possible action in the present. These past sequences of observations are stored in a derived form which only includes important information the brain thinks might be useful in the future, while forgetting the rest. we propose an architecture that tries to mimic the human brain and improve the memory efficiency of transformers by using a modified Transformer XL architecture which uses Automatic Chunking which only attends to the relevant chunks in the transformer block. On top of this, we use Forget Span which is technique to remove memories that do not contribute to learning. We also theorize the technique of Similarity based forgetting to remove repetitive memories. We test our model in various tasks that test the abilities required to perform well in a human-robot collaboration scenario.


Technical language processing for Prognostics and Health Management: applying text similarity and topic modeling to maintenance work orders

Abstract: Abstract Modern manufacturing paradigms have incorporated Prognostics and Health Management (PHM) to implement data-driven methods for fault detection, failure prediction, and assessment of system health. The maintenance operation has similarly benefitted from these advancements, and predictive maintenance is now being used across the industry. Despite these developments, most of the approaches in maintenance rely on numerical data from sensors and field devices for any sort of analysis. Text data from Maintenance Work Orders (MWOs) contain some of the most crucial information pertaining to the functioning of systems and components, but are still regarded as ‘black holes’, i.e., they store valuable data without being used in decision-making. The analysis of this data can help save time and costs in maintenance. While Natural Language Processing (NLP) methods have been very successful in understanding and examining text data from non-technical sources, progress in the analysis of technical text data has been limited. Non-technical text data are usually structured and consist of standardized vocabularies allowing the use of out-of-the-box language processing methods in their analysis. On the other hand, records from MWOs are often semi-structured or unstructured; and consist of complicated terminologies, technical jargon, and industry-specific abbreviations. Deploying traditional NLP to such data can result in an imprecise and flawed analysis which can be very costly. Owing to these challenges, we propose a Technical Language Processing (TLP) framework for PHM. To illustrate its capabilities, we use text data from MWOs of aircraft to address two scenarios. First, we predict corrective actions for new maintenance problems by comparing them with existing problems using syntactic and semantic textual similarity matching and evaluate the results with cosine similarity scores. In the second scenario, we identify and extract the most dominant topics and salient terms from the data using Latent Dirichlet Allocation (LDA). Using the results, we are able to successfully link maintenance problems to standardized maintenance codes used in the aviation industry.


A multi-task fusion model based on a residual–multi-layer perceptron network for mammographic breast cancer screening

Abstract: Deep learning approaches are being increasingly applied for medical computer-aided diagnosis (CAD). However, these methods generally target only specific image-processing tasks, such as lesion segmentation or benign state prediction. For the breast cancer screening task, single feature extraction models are generally used, which directly extract only those potential features from the input mammogram that are relevant to the target task. This can lead to the neglect of other important morphological features of the lesion as well as other auxiliary information from the internal breast tissue. To obtain more comprehensive and objective diagnostic results, in this study, we developed a multi-task fusion model that combines multiple specific tasks for CAD of mammograms.We first trained a set of separate, task-specific models, including a density classification model, a mass segmentation model, and a lesion benignity-malignancy classification model, and then developed a multi-task fusion model that incorporates all of the mammographic features from these different tasks to yield comprehensive and refined prediction results for breast cancer diagnosis.The experimental results showed that our proposed multi-task fusion model outperformed other related state-of-the-art models in both breast cancer screening tasks in the publicly available datasets CBIS-DDSM and INbreast, achieving a competitive screening performance with area-under-the-curve scores of 0.92 and 0.95, respectively.Our model not only allows an overall assessment of lesion types in mammography but also provides intermediate results related to radiological features and potential cancer risk factors, indicating its potential to offer comprehensive workflow support to radiologists.


Employing Statistical Algorithms and Clustering Techniques to Assess Lithological Facies for Identifying Optimal Reservoir Rocks: A Case Study of the Mansouri Oilfields, SW Iran

Abstract: The crucial parameters influencing drilling operations, reservoir production behavior, and well completion are lithology and reservoir rock. This study identified optimal reservoir rocks and facies in 280 core samples from a drilled well in the Asmari reservoir of the Mansouri field in SW Iran to determine the number of hydraulic flow units. Reservoir samples were prepared, and their porosity and permeability were determined by measuring devices. The flow zone index (FZI) was calculated for each sample using MATLAB software; then, a histogram analysis was performed on the logarithmic data of the FZI, and the number of hydraulic flow units was determined based on the obtained normal distributions. Electrical facies were determined based on artificial neural network (ANN) and multi-resolution graph-based clustering (MRGC) approaches. Five electrical facies with dissimilar reservoir conditions and lithological compositions were ultimately specified. Based on described lithofacies, shale and sandstone in zones three and five demonstrated elevated reservoir quality. This study aimed to determine the Asmari reservoir’s porous medium’s flowing fluid according to the C-mean fuzzy logic method. Furthermore, the third and fourth flow units in the Asmari Formation have the best flow units with high reservoir quality and permeability due to determining the siliceous–clastic facies of the rock units and log data. Outcomes could be corresponded to the flow unit determination in further nearby wellbores without cores.


Comparing reusable, atomic feedback with classic feedback on a linear equations task using text mining and qualitative techniques

Abstract: Abstract In this crossover experiment, we investigated the impact of a statement bank, enabling the reuse of previously written feedback (SA condition), on 45 math teachers' feedback for 60 completed linear equation tests, compared to traditional pen‐and‐paper feedback (PP condition). In the SA condition, teachers were encouraged to use atomic feedback, a set of formulation requirements that makes feedback items significantly more reusable. A previous study found that significantly more feedback was written in the SA condition but did not investigate the content of the feedback. To address this gap, we employed a novel approach of combining text mining with qualitative methods. Results indicate similar wording and sentiments in both conditions. However, SA feedback was more elaborate yet general, focusing on major and minor strengths and deficits, while PP feedback was shorter but more concrete, emphasising main issues. Despite low feedback quality in both conditions, the statement bank led to less effective diagnostic activities, implying that teachers' careless use of statement banks, although convenient, might lead to lower‐quality feedback. Practitioner notes What is already known about this topic High‐quality feedback should strike a balance between the volume and focus on the main issues, as more feedback does not necessarily equate to better feedback. Feedback should analyse a student's solution whenever possible: interpreting mistakes and communicating that interpretation as feedback. Text mining identifies meaningful patterns and new insights in text using computer algorithms. When teachers can reuse already given feedback using a software tool (statement bank), they tend to write more feedback instead of saving time. What this paper adds Feedback is compared when teachers could use a tool to reuse already given feedback (referred to as ‘statement banks’) versus a scenario without such a tool. Both approaches observed similar word frequencies, sentiments and amounts of erroneous, descriptive and corrective feedback. However, feedback with a statement bank tended to be more elaborate yet less specific to individual student solutions. In contrast, feedback without the tool was shorter but more concrete, focusing on main issues. Overall, the tool for reusing feedback directed teachers towards less effective diagnostic activities. The paper introduces a novel methodological approach by combining text mining with qualitative techniques in educational research. While text mining provides an overall understanding of differences and similarities in feedback approaches, qualitative methods are essential for in‐depth analysis of content characteristics and feedback quality. Implications for practice and/or policy Statement banks can support teachers by giving more feedback, but in order to improve feedback quality, further measures are necessary (eg, improving pedagogical content knowledge). Teachers may not confuse handiness with quality: statement banks can help, but when used carelessly, teachers tend to describe and correct students' work instead of analysing underlying (mis‐)conceptions using it. Continued attention to feedback quality remains necessary when using such tools.


Photonic neuromorphic architecture for tens-of-task lifelong learning

Abstract: Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design. Benefiting from the inherent sparsity and parallelism in massive photonic connections, L2ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field, while incrementally acquiring expertise on various tasks by gradually enlarging the activation. The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths. Extensive evaluations on free-space and on-chip architectures confirm that for the first time, L2ONN avoided the catastrophic forgetting issue of photonic computing, owning versatile skills on challenging tens-of-tasks (vision classification, voice recognition, medical diagnosis, etc.) with a single model. Particularly, L2ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks, and 14× larger capacity than existing optical neural networks while maintaining competitive performance on each individual task. The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme, permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.


Tutorials and mobile learning in higher education: Enhancing and accessibility

Abstract: This comprehensive paper delves into two vital facets of contemporary higher education. The first segment investigates the transformative force of mobile learning (m-learning), elucidating its far-reaching impact on the educational landscape. Offering unprecedented flexibility and accessibility, m-learning redefines the dynamics between students and educators. The exploration navigates through potential benefits, challenges, and broader implications, providing a nuanced understanding of the evolving landscape of higher education in the digital age. The second segment focuses on the impact of active teaching in a first-year class at the National Engineering School of Carthage (ENICarthage) in Tunisia. Active teaching, recognized for engaging students in the learning process, is scrutinized for its effectiveness in enhancing theoretical understanding and graded assignment performance. This research introduces a dynamic dimension by incorporating mobile learning and tutorials as integral components of active teaching strategies. With its technological leverage, mobile learning aims to enhance student engagement, while advanced tutorials feature simulation functionality for an immersive learning experience. Through statistical analysis, the paper contributes to a nuanced comprehension of the positive influence of active teaching on academic achievements, offering practical insights into the application of active teaching methods and their potential benefits for enhancing student learning outcomes. This synthesis thus presents a holistic view of the dynamic interplay between mobile learning and active teaching in shaping the contemporary higher education landscape.


Resilience of navigation strategy and efficiency to the impact of acute stress

Abstract: 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: 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 &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

Abstract: 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: 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: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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 & 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

Abstract: 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.


Marketness and Governance: A Typology of Illicit Online Markets

Abstract: The preceding decade has seen an explosive growth in illicit online commerce, and scholars are increasingly occupied with the study of illicit products, their buyers and sellers, and the platforms and markets in which they are found. Despite sharing one basic characteristic, the exchange of illicit goods and services, there is extensive variation between illicit online markets: Some markets employ currencies, others are based on barter. Some markets exhibit strong social control, others appear anarchic. Traditional theoretical explanations to the organization of illicit markets, which assume that optimization or evolution drives their organization, find it difficult to explain these variations. I propose a typology of illicit online markets that appreciates this heterogeneity and uses it as a theoretical point of departure. Drawing on social control theory and economic sociology, I argue that illicit online markets can be separated across two axes: administrative governance and ”marketness.” This conceptual framework is not prone to functionalist inference and instead implies a less deterministic understanding of how illicit online markets come to be. In turn, this framework encourages the classification and comparison of individual markets, as well as comparative analyses of marketplaces.


Transformer‐based reranking for improving Korean morphological analysis systems

Abstract: Abstract This study introduces a new approach in Korean morphological analysis combining dictionary‐based techniques with Transformer‐based deep learning models. The key innovation is the use of a BERT‐based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first‐stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary‐based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.


Dual‐scale BERT using multi‐trait representations for holistic and trait‐specific essay grading

Abstract: Abstract As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi‐trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT‐Trans‐CNN model, which combines transformer‐based representations with a novel dual‐scale bidirectional encoder representations from transformers (BERT) encoding approach at the document‐level. By explicitly leveraging multi‐trait representations in a multi‐task learning (MTL) framework, our DualBERT‐Trans‐CNN emphasizes the interrelation between holistic and trait‐based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single‐task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi‐trait performance on the ASAP++ dataset.


Spoken‐to‐written text conversion for enhancement of Korean–English readability and machine translation

Abstract: Abstract The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule‐based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data‐driven Korean spoken‐to‐written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.


CR‐M‐SpanBERT: Multiple embedding‐based DNN coreference resolution using self‐attention SpanBERT

Abstract: Abstract This study introduces CR‐M‐SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding‐based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost‐effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR‐M‐SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high‐recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach.


Towards a small language model powered chain‐of‐reasoning for open‐domain question answering

Abstract: Abstract We focus on open‐domain question‐answering tasks that involve a chain‐of‐reasoning, which are primarily implemented using large language models. With an emphasis on cost‐effectiveness, we designed EffiChainQA , an architecture centered on the use of small language models. We employed a retrieval‐based language model to address the limitations of large language models, such as the hallucination issue and the lack of updated knowledge. To enhance reasoning capabilities, we introduced a question decomposer that leverages a generative language model and serves as a key component in the chain‐of‐reasoning process. To generate training data for our question decomposer, we leveraged ChatGPT, which is known for its data augmentation ability. Comprehensive experiments were conducted using the HotpotQA dataset. Our method outperformed several established approaches, including the Chain‐of‐Thoughts approach, which is based on large language models. Moreover, our results are on par with those of state‐of‐the‐art Retrieve‐then‐Read methods that utilize large language models.


Unsupervised domain adaptation for histopathology image segmentation with incomplete labels

Abstract: Stain variations pose a major challenge to deep learning segmentation algorithms in histopathology images. Current unsupervised domain adaptation methods show promise in improving model generalization across diverse staining appearances but demand abundant accurately labeled source domain data. This paper assumes a novel scenario, namely, unsupervised domain adaptation based segmentation task with incompletely labeled source data. This paper propose a Stain-Adaptive Segmentation Network with Incomplete Labels (SASN-IL). Specifically, the algorithm consists of two stages. The first stage is an incomplete label correction stage, involving a reliable model selection process and label correction to rectify false-negative regions in incomplete labels. The second stage is the unsupervised domain adaptation stage, achieving segmentation on the target domain. In this stage, we introduce an adaptive stain transformation module to adjust the degree of transformation based on segmentation performance. We evaluate our method on a gastric cancer dataset, demonstrating significant improvements, with a 10.01% increase in Dice coefficient compared to the baseline and competitive performance relative to existing methods.


Survey of Spectral Clustering Based on Graph Theory

Abstract: Spectral clustering converts the data clustering problem to the graph cut problem. It is based on graph theory. Due to the reliable theoretical basis and good clustering performance, spectral clustering has been successfully applied in many fields. Although spectral clustering has many advantages, it faces the challenges of high time and space complexity when dealing with large scale complex data. Firstly, this paper introduces the basic concept of graph theory, reviews the properties of Laplacian matrix and the traditional graph cuts method. Then, it focuses on four aspects of the realization process of spectral clustering, including the construction of similarity matrix, the establishment of Laplacian matrix, the selection of eigenvectors and the determination of the number of clusters. In addition, some successful applications of spectral clustering are summarized. In each aspect, the shortcomings of spectral clustering and some representative improved algorithms are emphatically analyzed. Finally, the paper comprehensively analyzes some research on spectral clustering that has not yet been in-depth, and gives prospects on some valuable research directions.


Creating Autonomous Multi-Object Safe Control via Different Forms of Neural Constraints of Dynamic Programming

Abstract: The aim of this work, which is an extension of previous research, is a comparative analysis of the results of the dynamic optimization of safe multi-object control, with different representations of the constraints of process state variables. These constraints are generated with an artificial neural network and take movable shapes in the form of a parabola, ellipse, hexagon, and circle. The developed algorithm allows one to determine a safe and optimal trajectory of an object when passing other multi-objects. The obtained results of the simulation tests of the algorithm allow for the selection of the best representation of the motion of passing objects in the form of neural constraints. Moreover, the obtained characteristics of the sensitivity of the object’s trajectory to the inaccuracy of the input data make it possible to select the best representation of the motion of other objects in the form of an excessive approximation area as neural constraints of the control process.


Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Abstract: The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver's feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.


HQ3DAvatar: High Quality Implicit 3D Head Avatar

Abstract: Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A common approach to capture full head dynamic performances is to track the underlying geometry using a mesh-based template or 3D cube-based graphics primitives. While these model-based approaches achieve promising results, they often fail to learn complex geometric details such as the mouth interior, hair, and topological changes over time. This paper presents a novel approach to building highly photorealistic digital head avatars. Our method learns a canonical space via an implicit function parameterized by a neural network. It leverages multiresolution hash encoding in the learned feature space, allowing for high-quality, faster training and high-resolution rendering. At test time, our method is driven by a monocular RGB video. Here, an image encoder extracts face-specific features that also condition the learnable canonical space. This encourages deformation-dependent texture variations during training. We also propose a novel optical flow based loss that ensures correspondences in the learned canonical space, thus encouraging artifact-free and temporally consistent renderings. We show results on challenging facial expressions and show free-viewpoint renderings at interactive real-time rates for a resolution of 480 x 270. Our method outperforms related approaches both visually and numerically. We will release our multiple-identity dataset to encourage further research.


The usage of virtual reality in engineering education

Abstract: The state of virtual reality (VR) development is mature enough to be explored for application in various fields including higher education teaching, research, and training including engineering education. Traditionally, engineering education relied on diagrams and images to describe various systems and objects, sometimes accompanied by laboratory experiments involving existing systems for hands-on practice. However, these traditional approaches have limited capability. Visualization for effective illustration using the traditional approaches is challenging for topics such as electromagnetic which discusses abstract concepts of electric and magnetic fields. VR is a promising solution. It offers a more engaging and immersive visualization experience, leading to a greater understanding of the subject matter. The immersiveness of VR eliminates distracting external stimuli, and allows better user engagement with the lesson. Therefore, this study explores the application of VR in engineering education. An in-house developed VR laboratory named Merlin’s Playground is incorporated as part of assignment activities for a group of first-year students taking bachelor degree in electronics engineering. The students’ perspectives and attitudes towards the VR activities are studied. The student population finds this approach to be an appealing technology. The VR activity achieved the course aims by allowing the topic’s conception more successfully. The VR approach boosted the students’ understanding and problem-solving skills. This is demonstrated by the cohort of students achieving higher average marks on end-of-topic assessments compared to their seniors when VR activities were not employed.


Demonstrating Bayesian quantum phase estimation with quantum error detection

Abstract: Quantum phase estimation (QPE) serves as a building block of many different quantum algorithms and finds important applications in computational chemistry problems. Despite the rapid development of quantum hardware, experimental demonstration of QPE for chemistry problems remains challenging due to its large circuit depth and the lack of quantum resources to protect the hardware from noise with fully fault-tolerant protocols. In the present work, we take a step towards fault-tolerant quantum computing by demonstrating a QPE algorithm on a Quantinuum trapped-ion computer. We employ a Bayesian approach to QPE and introduce a routine for optimal parameter selection, which we combine with a $[[n+2,n,2]]$ quantum error detection code carefully tailored to the hardware capabilities. As a simple quantum chemistry example, we take a hydrogen molecule represented by a two-qubit Hamiltonian and estimate its ground state energy using our QPE protocol. In the experiment, we use the quantum circuits containing as many as 920 physical two-qubit gates to estimate the ground state energy within $6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$ hartree of the exact value.


Medical long-tailed learning for imbalanced data: Bibliometric analysis

Abstract: In the last decade, long-tail learning has become a popular research focus in deep learning applications in medicine. However, no scientometric reports have provided a systematic overview of this scientific field. We utilized bibliometric techniques to identify and analyze the literature on long-tailed learning in deep learning applications in medicine and investigate research trends, core authors, and core journals. We expanded our understanding of the primary components and principal methodologies of long-tail learning research in the medical field.Web of Science was utilized to collect all articles on long-tailed learning in medicine published until December 2023. The suitability of all retrieved titles and abstracts was evaluated. For bibliometric analysis, all numerical data were extracted. CiteSpace was used to create clustered and visual knowledge graphs based on keywords.A total of 579 articles met the evaluation criteria. Over the last decade, the annual number of publications and citation frequency both showed significant growth, following a power-law and exponential trend, respectively. Noteworthy contributors to this field include Husanbir Singh Pannu, Fadi Thabtah, and Talha Mahboob Alam, while leading journals such as IEEE ACCESS, COMPUTERS IN BIOLOGY AND MEDICINE, IEEE TRANSACTIONS ON MEDICAL IMAGING, and COMPUTERIZED MEDICAL IMAGING AND GRAPHICS have emerged as pivotal platforms for disseminating research in this area. The core of long-tailed learning research within the medical domain is encapsulated in six principal themes: deep learning for imbalanced data, model optimization, neural networks in image analysis, data imbalance in health records, CNN in diagnostics and risk assessment, and genetic information in disease mechanisms.This study summarizes recent advancements in applying long-tail learning to deep learning in medicine through bibliometric analysis and visual knowledge graphs. It explains new trends, sources, core authors, journals, and research hotspots. Although this field has shown great promise in medical deep learning research, our findings will provide pertinent and valuable insights for future research and clinical practice.


Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

Abstract: The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.


DIFFERENCES IN THE WISHES OF STUDENTS, TEACHERS, AND PARENTS ON INTEGRATION OF SMARTPHONES AND TABLETS IN BIOLOGY LESSONS

Abstract: Smartphones and tablets have permeated various aspects of life. This study explores the differences in wishes between students, parents, and teachers regarding the use of smartphones and tablets in biology classes in the upcoming school year. An online questionnaire was used for the study, which provided eight different scenarios for the use of smartphones for teaching purposes. The data were collected from 934 participants, including 465 students, 282 parents, and 188 biology teachers from various Slovenian lower secondary schools. The principal component analysis revealed the unidimensional structure of the instrument, explaining 59.7% of the variance (alpha = .91). The results showed that the use of smartphones and tablets for distance learning, teaching purposes, schoolwork and homework is generally desirable. There was less consensus on their use for laboratory and field work, evaluation of knowledge, and biology lessons. The main finding was that the differences between the groups were small or even negligible in terms of effect sizes. Statistically significant differences were found between the focus groups, with students and teachers expressing greater agreement than parents. These findings emphasize the importance of addressing parents' concerns and understanding the perspectives of stakeholders in order to effectively integrate smartphones and tablets into the classroom. Keywords: differences in wishes, lower secondary school biology, mobile learning, smartphones and tablets integration, students and parents and teachers


Comparative study: enhancing legibility of ancient Indian script images from diverse stone background structures using 34 different pre-processing methods

Abstract: Abstract In recent times, there has been a proactive effort by various institutions and organizations to preserve historic manuscripts as repositories of traditional knowledge and cultural heritage. Leveraging digital media and emerging technologies has proven to be an efficient way to safeguard these invaluable documents. Such technologies not only facilitate the extraction of knowledge from historic manuscripts but also hold promise for global applications. However, transforming inscribed stone artifacts into binary formats presents significant challenges due to angle distortion, subtle differences between foreground and background, background noise, variations in text size, and related issues. A pivotal aspect of effective image processing in preserving the rich information and wisdom encoded in stone inscriptions lies in employing appropriate pre-processing methods and techniques. This research paper places a special focus on elucidating various preprocessing techniques, encompassing resizing, grayscale conversion, enhancement of brightness and contrast, smoothening, noise removal, morphological operations, and thresholding. To comprehensively assess these techniques, we undertake a study involving stone inscription images extracted from the Tanjore Brihadeeswar Temple, dating back to the eleventh century during the reign of Raja Raja Chola. This choice is informed by the manifold challenges associated with image correction, such as distortion and blurring. We undertake an evaluation encompassing a diverse array of stone background structures, including types like flawless-bright-moderately legible, dark-illegible, flawless-bright-illegible, flawless-dull, flawless-irregular-moderate, highly impaired-dark-legible, highly impaired-irregular-illegible, impaired-dark-moderate, impaired-dull-moderately legible, impaired-dusky dark-moderate, and very impaired-dusky dark-legible. Subsequently, the processed outputs are subjected to character recognition and information extraction, with a focus on comparing the outcomes of various pre-processing methods, including binarization and grayscale conversion. This study seeks to contribute insights into the most effective pre-processing strategies for enhancing the legibility and preservation of ancient Indian script images etched onto diverse stone background structures.


Network topology facilitates internet traffic control in autocracies

Abstract: Recent years have seen an increase in governmental interference in digital communication. Most research on this topic has focused on the application level, studying how content is manipulated or removed on websites, blogs, or social media. However, in order for governments to obtain and maintain control of digital data flows, they need to secure access to the network infrastructure at the level of Internet service providers. In this paper, we study how the network topology of the Internet varies across different political environments, distinguishing between control at the level of individual Internet users (access) and a higher level in the hierarchy of network carriers (transit). Using a novel method to estimate the structure of the Internet from network measurements, we show that in autocratic countries, state-owned (rather than privately owned) providers have a markedly higher degree of control over transit networks. We also show that state-owned Internet providers often provide Internet access abroad, with a clear focus on other autocratic countries. Together, these results suggest that in autocracies, the network infrastructure is organized in a way that is more susceptible to the monitoring and manipulation of Internet data flows by state-owned providers both domestically and abroad.


Particle Swarm Optimization in Biomedical Technologies

Abstract: This chapter explores particle swarm optimization (PSO) in the rapidly evolving landscape of biomedical technologies. The study begins by introducing the fundamental principles of PSO, emphasizing its advantages in addressing complex optimization problems common in biomedical applications. The authors delve into innovative uses of PSO in various biomedical fields, including image enhancement, data clustering, and drug development, highlighting how PSO contributes to more accurate diagnoses, efficient treatment plans, and streamlined research methodologies. Significantly, this chapter identifies emerging opportunities where PSO can be further leveraged, particularly in personalized medicine and predictive health analytics, suggesting a roadmap for future research and development. By combining theoretical insights with practical examples, this work aims to provide a comprehensive overview of PSO's role in advancing biomedical technologies, offering valuable perspectives for researchers, practitioners, and policymakers in the field.


A comprehensive survey on deep graph representation learning

Abstract: Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.


Fractional Approximation of Broad Learning System

Abstract: Approximation ability is of much importance for neural networks. The broad learning system (BLS) (Chen and Liu, 2018), widely used in the industry with good performance, has been proved to be a universal approximator from the aspect of density. This kind of approximation property is very important, which proves the existence of the desired network but does not provide a means of construction that is commonly implemented through complexity aspect. Thus, such an approach lacks the advantage of determining constructively the network architecture and its weights. To the best of our knowledge, for a BLS, there is a few theory providing a constructive approach to obtain the network structure along with weights ensuring the approximation properties. By virtue of the long-term memory and nonlocality properties, fractional calculus has observed many distinctive applications. The purpose of this article is to study the BLS approximation ability constructively, which is valid for fractional case as well. Specifically, first we introduce two simplified BLSs by means of extending functions. For each of the simplified BLSs, an upper bound of error is derived through the modulus of continuity of Caputo fractional derivatives. As a result, two types of fractional convergent behaviors of BLS, that is: 1) pointwise and 2) uniform convergence, have been rigorously proved as well. Finally, some numerical experiments are conducted to demonstrate the approximation capabilities of BLSs.


Application of augmented reality in automotive industry

Abstract: Introduction: Augmented reality is defined as a direct or indirect vision of a physically real environment, parts of which they are enriched with additional digital information relevant to the object that is being looked at. In the field of engineering design, there is a wide range of industries that use this technology, such as automotive, aircraft manufacturing, electronics, engineering; so that it has gained popularity in assembly, maintenance and inspection tasks. The objective was to characterize the use of augmented reality in the automotive industry.Methods: a total of 20 articles in Spanish and English were reviewed, from Scopus, Science and Dialnet; Using as keywords: augmented reality, automotive industry, manufacturing, being more than 50 % of the last five years.Result: its main advantage is considered its potential as an interactive and intuitive interface. It promises to provide the correct information to the human operator at the right time and place. If it is considered an ideal environment in which the RA is applied safely, in adequate balance between automated processes and human control over them; The level of production and its quality will be positively affected.Conclusions: Augmented reality is applied in areas of the automotive industry such as logistics design, assembly, maintenance, evaluation, diagnosis, repair, inspection, quality control, instruction and marketing; in order to guarantee better work performance, productivity and efficiency, mainly mediated by portable devices. Its degree of acceptance, although growing, is not yet clear.


Toward Efficient Deep Learning for Graph Drawing (DL4GD)

Abstract: Due to their great performance in many challenges, Deep Learning (DL) techniques keep gaining popularity in many fields. They have been adapted to process graph data structures to solve various complicated tasks such as graph classification and edge prediction. Eventually, they reached the Graph Drawing (GD) task. This paper is an extended version of the previously published <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(DNN)^{2}$</tex-math></inline-formula> and presents a framework to leverage DL techniques for graph drawing (DL4GD). We demonstrate how it is possible to train a Deep Learning model to extract features from a graph and project them into a graph layout. The method proposes to leverage efficient Convolutional Neural Networks, adapting them to graphs using Graph Convolutions. The graph layout projection is learned by optimizing a cost function that does not require any ground truth layout, as opposed to prior work. This paper also proposes an implementation and benchmark of the framework to study its sensitivity to certain deep learning-related conditions. As the field is novel, and many questions remain to be answered, we do not focus on finding the most optimal implementation of the method, but rather contribute toward a better understanding of the approach potential. More precisely, we study different learning strategies relative to the models training datasets. Finally, we discuss the main advantages and limitations of DL4GD.


Enabling Data Sharing Through Data Trusts in LEO Satellite Internet

Abstract: Due to the advantages of low latency, small propagation loss, and global coverage, LEO satellite network becomes a cost-effective long-distance communication solution for IoT devices in remote areas. Nevertheless, the LEO satellite IoT devices deployed for specific tasks are idle most of the time, raising the obvious question for future research: how to effectively share these idle sensing resources. Fortunately, data trusts emerge as a promising strategy to leverage the idle sensing resources. However, the data trust model faces privacy and security challenges in LEO satellite IoT scenarios to achieve trusted data trading. Therefore, this paper proposes a blockchainenabled data trust model and a trusted data trading scheme for device providers, data trustees, and general users. Furthermore, exploiting the homomorphic encryption technology, we realize the fine-grained data query and device match in the premise of privacy protection for device providers and general users. Lastly, considering the openness of satellite-terrestrial communication links and the limited computing power of LEO satellite IoT devices, we design a non-interactive lightweight authentication protocol based on zero-knowledge proof. The simulation results verify the feasibility, effectiveness, and security performance of the proposed data sharing for LEO satellite IoT.


FADS: Fourier-Augmentation Based Data-Shunting for Few-Shot Classification

Abstract: Collecting a substantial number of labeled samples is infeasible in many real-world scenarios, thereby bringing out challenges for supervised classification. The research on Few-Shot Classification (FSC) aims to address this issue. Current FSC methods mainly leverage ideas such as meta-learning, self-supervised learning, and data augmentation. Among them, data augmentation appears to be an extremely efficient approach to alleviate the aforementioned data-deficiency problem. Here, we propose a novel data augmentation based FSC method termed Fourier-Augmentation based Data-Shunting (FADS). FADS mainly contains two operations, namely Fourier-based data augmentation (FDA) and data shunting. (i) Fourier transform has a desirable property for classification tasks: the image’s phase and amplitude components in the frequency domain correspond to its high-level structure ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e ., semantic) and low-level style ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e ., statistic) information, which do not interfere with each other. Inspired by this observation, we design the FDA operation, which changes the amplitude spectrum of the to-be-augmented images to obtain new images of the same category. (ii) Then we design the data shunting operation to cooperate with the FDA to accomplish FSC. Specifically, it splits the augmented data into different groups to get independent, weak decisions and then fuses them to obtain a unified, strong decision. We conduct experiments on four benchmark datasets. Results show that utilizing our method brings a performance gain of 0.3%-2% in terms of classification accuracy, compared with the classical methods.


Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system

Abstract: Multiple sclerosis (MS) is a commonly seen neurodegenerative disorder, and early diagnosis of MS is a crucial issue to promote patient health. Since MS diagnosis is a computer vision problem, machine learning can be utilized for this purpose. Important research has used transfer learning (TL) to rapidly apply the advantages of deep learning models. Therefore, TL has developed a wide usage in computer vision applications. Herein, we describe a new algorithm in this regard, termed transfer-transfer (TT). To implement the algorithm, a multi-result machine learning model is required. In order to determine efficacy, we use transfer learning-based and hybrid feature engineering. The goal is to demonstrate the classifiability of the TT model. A new magnetic resonance image dataset containing three classes were collected to obtain TT model results, i.e.: (1) multiple sclerosis (MS), (2) myelitis, and (3) control patients. We have designed this model for MS detection. Thus, we named it MSNet. For deep feature engineering, MSNet with two layers of pretrained DenseNet201 and two layers of ResNet50 was incorporated into the system since these networks are highly accurate. By deploying these four layers, four feature vectors were calculated. ReliefF (RF), Chi2, and Neighborhood Component Analysis (NCA) were utilized in the feature selection phase, and the number of the feature vectors is increased from 4 to 12 (=4 × 3). By using k-nearest neighbor (kNN) and support vector machine (SVM) classifiers, 24 (=12 × 2) outputs were calculated, with the best result created by applying information fusion. TT incorporates the information fusion findings to construct a new feature vector. The most salient features were selected by deploying an iterative feature selector, and the features chosen were then classified. The TT-based MSNet was applied to the magnetic resonance (MR) image dataset, yielding a 97.63% classification accuracy. The findings and computed results demonstrate that the TT model with MSNet outperforms existing systems with increased transfer learning classifiability.