Reviewerly/exHarmony huggingface dataset:
Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches

An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people’s diverse handwriting styles. Therefore, this paper aims to develop Kannada handwritten script recognition framework by ensemble methods. Initially, the essential Kannada handwritten text images are collected. These collected images are used for pre-processing with the CLAHE and filtering techniques and followed by segmentation with the active contour approach. The segmented images are utilized using the Adaptive Local Tetra Pattern with freeman chain code histogram techniques. Here, the parameters in the LTrP are tuned with Hybrid Honey Badger Henry Gas Solubility Optimization. Finally, the handwritten recognition is performed by Hybrid Feature Extraction with Ensemble Deep Learning (HFE-EDH). Further, the recognition rate is elevated with the parameter optimization in deep learning approaches using the HGSO + HBA algorithm. Throughout the result analysis, the accuracy of the designed method attains 96%. Thus, the proposed method reveals better performance regarding various performance measures.
Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis

Deep learning-based diagnosis is becoming an indispensable part of modern healthcare. For high-performance diagnosis, the optimal design of deep neural networks (DNNs) is a prerequisite. Despite its success in image analysis, existing supervised DNNs based on convolutional layers often suffer from their rudimentary feature exploration ability caused by the limited receptive field and biased feature extraction of conventional convolutional neural networks (CNNs), which compromises the network performance. Here, we propose a novel feature exploration network named manifold embedded multilayer perceptron (MLP) mixer (ME-Mixer), which utilizes both supervised and unsupervised features for disease diagnosis. In the proposed approach, a manifold embedding network is employed to extract class-discriminative features; then, two MLP-Mixer-based feature projectors are adopted to encode the extracted features with the global reception field. Our ME-Mixer network is quite general and can be added as a plugin to any existing CNN. Comprehensive evaluations on two medical datasets are performed. The results demonstrate that their approach greatly enhances the classification accuracy in comparison with different configurations of DNNs with acceptable computational complexity.
Measurement‐Based Hyperentanglement Distillation for Lossy and Distortion Photon State

Abstract The information content of a photon system can be extended by hyperentanglement, but the quality of hyperentanglement will be decreased by the complicated transmission loss and channel noise in quantum information processing. Here, an efficient measurement‐based hyperentanglement distillation protocol (MB‐HDP) is presented for depressing the effects of complicated transmission loss and channel noise on hyperentanglement. In the MB‐HDP, the nonlocal lossy and distortion photon states are coupled to local hyperentangled Greenberger–Horne–Zeilinger (GHZ) states using parity measurement and qubit amplification device, and the decoherence caused by bit‐flip (phase‐flip) error, diverse transmission coefficients and transmission loss can be depressed by the successful measurement results, which can increase the quality of nonlocal hyperentangled photon state. This MB‐HDP broadens the application scope of hyperentanglement distillation to nonlocal lossy and distortion photon state with a lower degree of entanglement. In addition, the MB‐HDP can further improve the quality of nonlocal hyperentangled photon state by coupling multiple copies of lossy and distortion hyperentangled photon state with local hyperentangled GHZ states. This work demonstrates the ability of measurement‐based method for ensuring the quality of nonlocal hyperentanglement, which can improve the integrity and capacity of long‐distance quantum information processing.
Breast Cancer Detection Using Image Processing and Machine Learning

Different breast cancer detection systems have been developed to help clinicians analyze screening mammograms. Breast cancer has been increasing gradually so scientists work to develop new methods to reduce the risks of this life-threatening disease. Convolutional Neural Networks (CNNs) have shown much promise In the field of medical imaging because of recent developments in deep learning. However, CNN’s based methods have been restricted due to the small size of the few public breast cancer datasets. This research has developed a new framework and introduced it to detect breast cancer. This framework utilizes Convolutional Neural Networks (CNNs) and image processing to achieve its goal because CNNs have been an important success in image recognition, reaching human performance. An efficient and fast CNN pre-trained object detector called RetinaNet has been used in this research. RetinaNet is an uncomplicated one-stage object detector. A two-stage transfer learning has been used with the selected detector to improve the performance. RetinaNet model is initially trained with a general-purpose dataset called COCO dataset. The transfer learning is then used to apply the RetinaNet model to another dataset of mammograms called the CBIS-DDSM dataset. Finally, the second transfer learning is used to test the RetinaNet model onto a small dataset of mammograms called the INbreast dataset. The results of the proposed two-stage transfer learning (RetinaNet → CBIS-DDSM → INbreast) are better than the other state-of-the-art methods on the public INbreast dataset. Furthermore, the True Positive Rate (TPR) is 0.99 ± 0.02 at 1.67 False Positives per Image (FPPI), which is better than the one-stage transfer learning with a TPR of 0.94 ± 0.02 at 1.67 FPPI.
Modified YOLO Module for Efficient Object Tracking in a Video

In the proposed work, initially, the YOLO algorithm is used to extract and classify objects in a frame. In the sequence of frames, due to various reasons the confidence measure suddenly drops. This changes the class of an object in consecutive frames which affects the object tracking and counting process severely. To overcome this limitation of the YOLO algorithm, it is modified to enable and track the same object efficiently in the sequence of frames. This will in turn increase object tracking and counting accuracy. In the proposed work drastic change in confidence scores and class change of an object in consecutive frames are identified by tracking the confidence of a particular object in the sequence of frames. These outliers are detected and removed using the RANSAC algorithm. After the removal of the outliers, interpolation is applied to get the new confidence score at that point. By applying the proposed method a smooth confidence measure variation is obtained across the frames. Using this, average counting accuracy has been increased from 66 % to 87 % and overall average object classification accuracy is in the range of 94 - 96 % for various standard dataset.
Adding Knock Code Technology as a Third Authentication Element to a Global Two-factor Authentication System

Cybersecurity relies on authentication, integrity, and privacy. Most users can increase password-based login security. It often uses Two-Factor Authentication. Two-factor authentication ensures that you’re using a combination of proven methods. Today’s technology uses Two-Factor Authentication to protect user privacy and security. Customized, easy-to-implement security solutions sell better and are bought more. Three-factor authentication improves website and mobile app security. This article will explain our three-factor authentication smartphone app.
Conversational agents: a survey on culturally informed design practices

The development and use of conversational agents grow every day. However, those systems still need to meet users' expectations. As a result, new design practices for conversational agents are emerging. One facet is the culturally informed design of those systems. Tailoring conversational agents culturally can increase engagement, trustworthiness, and acceptance. While an emerging trend, literature still needs to discuss research and application of design practices related to culture and conversational agents. The present study investigates 5398 articles involving conversational agents and culture over the last seven years. In this context, we selected 23 articles based on their approaches and objectives related to our main subject. The findings indicate that most studies in the field are mainly focused on the feasibility of cultural markers tailored to those systems. The main contribution is the identification of the main challenges, contexts and design practices in the field. These results contributed to discussions regarding conversational agents and highlighted some research gaps for future study.
Neuro-oncological augmented reality planning for intracranial tumor resection

Background Before starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows for the planning of the optimal skin incision, craniotomy, and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR) allows displaying of the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation. Methods We developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II, which exploits the built-in infrared-camera for tracking the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons and trainees with varying degrees of experience. After patient registration, tumor outlines were marked on the patient's skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Their performance in both registration and delineation was measured in terms of accuracy and duration and compared. Results During phantom testing, registration errors remained below 2.0 mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. The overall planning time (AR = 119 ± 44 s, conventional = 187 ± 56 s) was significantly reduced through the adoption of the AR workflow ( p &amp;lt; 0.001), with an average time reduction of 39%. Conclusion By providing a more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.
ECIR 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval

This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine symbolic and neural approaches, what kind of symbolic/neural approaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval.
Generalized echo squeezing protocol with near-Heisenberg-limit sensitivity and strong robustness against detection noise and variation in squeezing parameter

We present a generalized echo squeezing protocol (GESP) as a generalization of the Schr\"odinger cat state protocol with the value of the squeezing parameter being an arbitrary number rather than $\ensuremath{\pi}/2$. We show analytically that over a very broad range of the squeezing parameter the sensitivity of the GESP reaches the Heisenberg limit within a factor of $\sqrt{2}$. For a large number of atoms, $N$, this plateau interval is almost the whole range from zero to $\ensuremath{\pi}/2$, and the sensitivity is independent of the parity of $N$. Therefore, it is possible to operate a sensor over a wide interval of the squeezing parameter without changing the sensitivity. This is to be contrasted with the conventional echo squeezing protocol (CESP) which only works for a very small interval of the squeezing parameter. We also show that, in contrast to the CESP, the sensitivity of the GESP is close to the quantum Cram\'er-Rao bound over the whole range of the squeezing parameter, indicating that the phase-shift information contained in the quantum state is near-optimally extracted. We find that the enhancement in sensitivity in the case of the GESP is due to a combination of two parameters: the phase magnification factor (PMF) and the noise amplification factor (NAF). As the value of the squeezing parameter increases, both PMF and NAF increase, while keeping the ratio of the PMF to the NAF essentially constant, yielding a net enhancement of sensitivity at the Heisenberg limit within a factor of $\sqrt{2}$ over the whole plateau interval. An important consequence of this behavior is that the robustness of the GESP against detection noise easily exceeds that of the CESP for a broad range of values of the squeezing parameter. As such, in the context of an experimental study, it should be possible to achieve a net enhancement in sensitivity higher than that for the CESP, under typical conditions where detection noise exceeds the quantum projection noise of an unsqueezed state with the same number of atoms. Finally, we consider the fragility of the GESP against decoherence mechanisms, and show how a balance between the fragility against the decoherence mechanisms and the robustness against detection noise would in practice determine the optimal choice of parameters for the GESP.
Effect of Specimen Processing Technique on Cell Detection and Classification by Artificial Intelligence

Abstract Objectives Cytomorphology is known to differ depending on the processing technique, and these differences pose a problem for automated diagnosis using deep learning. We examined the as-yet unclarified relationship between cell detection or classification using artificial intelligence (AI) and the AutoSmear (Sakura Finetek Japan) and liquid-based cytology (LBC) processing techniques. Methods The “You Only Look Once” (YOLO), version 5x, algorithm was trained on the AutoSmear and LBC preparations of 4 cell lines: lung cancer (LC), cervical cancer (CC), malignant pleural mesothelioma (MM), and esophageal cancer (EC). Detection and classification rates were used to evaluate the accuracy of cell detection. Results When preparations of the same processing technique were used for training and detection in the 1-cell (1C) model, the AutoSmear model had a higher detection rate than the LBC model. When different processing techniques were used for training and detection, detection rates of LC and CC were significantly lower in the 4-cell (4C) model than in the 1C model, and those of MM and EC were approximately 10% lower in the 4C model. Conclusions In AI-based cell detection and classification, attention should be paid to cells whose morphologies change significantly depending on the processing technique, further suggesting the creation of a training model.
Particle Swarm Optimization-Based Convolutional Neural Network for Handwritten Chinese Character Recognition

Recently, handwritten Chinese character recognition has become an important research field in computer vision. With the development of deep learning, convolutional neural networks (CNNs) have demonstrated excellent performance in computer vision. However, CNNs are typically designed manually, which requires extensive experience and may lead to redundant computations. To solve these problems, in this study, the particle swarm optimization approach is incorporated into the design of a CNN for handwritten Chinese character recognition, reducing redundant computations in the network. In this approach, each network architecture is represented by a particle, and the optimal network architecture is determined by continuously updating the particles until a global particle is identified. The experimental validation resulted in a network accuracy of 97.24% with only 1.43 million network parameters. Therefore, it is demonstrated that the proposed particle swarm optimization method can quickly and accurately find the optimal network architecture.
The Design and Implementation of the Cloud-Based System of Open Science for Teachers’ Training

The cloud-based system for training the educational personnel at different levels was designed, modelled and implemented. The European Open Science Cloud (EOSC) tools as well as other kinds of cloud-based tools have been used as a platform for organizing and conducting collaborative learning. A significant increase in the open science competencies of teachers has been established. In particular, the wider use of EOSC services by teachers, improving skills and experience of collaboration in their disciplinary community and beyond; research data management, analysis/use/reuse, and dissemination. Various indicators for determining the teachers’ competencies of open science were selected, and appropriate measurement tools were developed. Among the criteria there are such as certain skills and experience of open access publication; research data management, processing, analysis/use/reuse, dissemination and the paradigm shift from “protected default data” to “open default data”, collaborative learning and research within the scientific and professional community and outside it. A study on the effectiveness of implementing the special training methods for a wide contingent of teachers was conducted the experimental groups included four groups of learners (a total of 395 respondents), the control groups included 141 respondents, and the statistical analysis of data obtained from the experiment was verified using Fisher’s test.
From Black Boxes to Transparent Machines: The Quest for Explainable AI

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Bayesian cluster analysis

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering structure and patterns within each cluster. An overview of Bayesian cluster analysis is provided, including both model-based and loss-based approaches, along with a discussion on the importance of the kernel or loss selected and prior specification. Advantages are demonstrated in an application to cluster cells and discover latent cell types in single-cell RNA sequencing data to study embryonic cellular development. Lastly, we focus on the ongoing debate between finite and infinite mixtures in a model-based approach and robustness to model misspecification. While much of the debate and asymptotic theory focuses on the marginal posterior of the number of clusters, we empirically show that quite a different behaviour is obtained when estimating the full clustering structure. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.
Sentence-BERT and k-means Based Clustering Technology for Scientific and Technical Literature

The bibliography of scientific and technical literature is a collection of all the key information in scientific and technical literature. The clustering technology for scientific and technical literature includes feature extraction and clustering analysis. In this paper, based on the traditional sentence embedding model, we propose a method combining the SentenceBERT model with the improved k-means algorithm to improve the efficiency of clustering of the information in the bibliography of scientific and technical literature. Clustering experiments were conducted using the information in the bibliography of scientific and technical literature collected from the China National Knowledge Infrastructure (CNKI) database, and the adjusted mutual information (AMI) values and silhouette coefficients (SC) were significantly improved, which verified the effectiveness of the method in this paper.
Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced composites

This dataset includes four large field-of-view scanning electron microscopy (SEM) images together with associated Matlab scripts aimed for the analysis used in the joint publication. Each of the four stitched images is generated from a large number (between 15500 and 24500) high-resolution (195nm/pixel) scans, which have been stitched into four images stored as tiff-files. The images show the cross-section of fiber bundles in composite laminate and are well-suited for local fiber volume determination. The image resolution corresponds to between 600 and 2000 pixels covering each fiber. The imaged samples are from composite laminates with an overall fiber volume fraction in the range of 55% to 60%. The local fiber volume fraction is found both for the full cross-section, as an average fiber volume fraction over the individual bundles, and as a local fiber volume fraction found in a moving averaging box with a size corresponding to 5×5 fiber diameter (80×80 µm2) areas.
Stability analysis of stochastic gradient descent for homogeneous neural networks and linear classifiers

We prove new generalization bounds for stochastic gradient descent when training classifiers with invariances. Our analysis is based on the stability framework and covers both the convex case of linear classifiers and the non-convex case of homogeneous neural networks. We analyze stability with respect to the normalized version of the loss function used for training. This leads to investigating a form of angle-wise stability instead of euclidean stability in weights. For neural networks, the measure of distance we consider is invariant to rescaling the weights of each layer. Furthermore, we exploit the notion of on-average stability in order to obtain a data-dependent quantity in the bound. This data-dependent quantity is seen to be more favorable when training with larger learning rates in our numerical experiments. This might help to shed some light on why larger learning rates can lead to better generalization in some practical scenarios.