Reviewerly/exHarmony huggingface dataset:
A Novel Robust Topological Denoising Method Based on Homotopy Theory for Virtual Colonoscopy

Virtual colonoscopy plays an important role in polyp detection of colorectal cancer. Noise in the colon data acquisition process can result in topological errors during surface reconstruction. Topological denoising can be employed to remove these errors on surfaces for subsequent geometry processing, such as surface simplification and parameterization. Many methods have been proposed for this task. However, many existing methods suffer from failure in computation of all the non-trivial loops, due to high genus or complex topological structures. In this paper, we propose a novel robust topological denoising method for surfaces based on homotopy theory. The proposed method was evaluated on two datasets of colon meshes. We compared our method with the State-of-the-Art persistent-homology-based method. Our method can successfully compute the loops on all colon data for topological denoising, whereas the persistent homology method fails on some colon data. Moreover, our method detects all loops with shorter lengths than those detected by the persistent homology method. Our experimental results show that the proposed method is effective and robust in topological denoising, and that it has the potential for practical application to virtual colonoscopy.
SODCNN: A Convolutional Neural Network Model for Small Object Detection in Drone-Captured Images

Drone images contain a large number of small, dense targets. And they are vital for agriculture, security, monitoring, and more. However, detecting small objects remains an unsolved challenge, as they occupy a small proportion of the image and have less distinct features. Conventional object detection algorithms fail to produce satisfactory results for small objects. To address this issue, an improved algorithm for small object detection is proposed by modifying the YOLOv7 network structure. Firstly, redundant detection head for large objects is removed, and the feature extraction for small object detection advances. Secondly, the number of anchor boxes is increased to improve the recall rate for small objects. And, considering the limitations of the CIoU loss function in optimization, the EIoU loss function is employed as the bounding box loss function, to achieve more stable and effective regression. Lastly, an attention-based feature fusion module is introduced to replace the Concat module in FPN. This module considers both global and local information, effectively addressing the challenges in multiscale and small object fusion. Experimental results on the VisDrone2019 dataset demonstrate that the proposed algorithm achieves an mAP50 of 54.03% and an mAP50:90 of 32.06%, outperforming the latest similar research papers and significantly enhancing the model’s capability for small object detection in dense scenes.
Predictors of Engagement in Virtual Reality Storytelling Environments about Migration

Virtual reality (VR) environments provide a high level of immersion that expands the possibilities for perspective-taking so that people can be in the shoes of others. In that regard, VR storytelling environments are good for situating people in a real migration story. Previous research has investigated how users engage in narrative VR experiences. However, there is a lack of research on the predictors of engagement in VR storytelling environments. To fill this gap in the literature, this study aims to identify the predictors of engagement when VR is used as a medium to tell a migration story. A structural model based on hypotheses was validated using partial least squares structural equation modeling (PLS-SEM) with data from the interaction of 212 university students with a tailor-made VR experience developed in Unity to engage people in two migration stories. The results show that our model explains 55.2% of the variance in engagement because of the positive influence of immersion, presence, agency, usability, and user experience (UX).
Ultrabright source of non-degenerate polarization-entangled photon pairs based on off-the-shelf polarization optics

We report a Sagnac-type source based on a MgO:PPLN non-linear crystal pumped with a 532 nm continuous-wave laser, which generates spectrally non-degenerate, polarization-entangled photon pairs of 785 nm signal and 1651 nm idler wavelength, via the type-0 spontaneous parametric downconversion (SPDC) process. We use standard off-the-shelf components; hence, no custom-designed multi-wavelength polarization optics are required. At the same time, long-term phase stability can be achieved due to the geometry of our Sagnac-type design. The source's brightness is 6.17(4)×106 pairs/sec/mW, which is comparable to the brightest state-of-the-art non-degenerate polarization-entangled photon-pair source. The spectral bandwidth of the single photon is approximately 195 GHz (root mean square). The fidelity of the entangled state is measured to be 96.72(1)%, the concurrence is 94.7(2)%, and the Bell-CHSH value S=2.71(6). This source has the potential to be applied in long-distance quantum communication using both free-space and fiber links simultaneously.
Human Micro-Expressions in Multimodal Social Behavioral Biometrics

The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems.
Variational Amplitude Amplification for Solving QUBO Problems

We investigate the use of amplitude amplification on the gate-based model of quantum computing as a means for solving combinatorial optimization problems. This study focuses primarily on quadratic unconstrained binary optimization (QUBO) problems, which are well-suited for qubit superposition states. Specifically, we demonstrate circuit designs which encode QUBOs as ‘cost oracle’ operations UC, which distribute phases across the basis states proportional to a cost function. We then show that when UC is combined with the standard Grover diffusion operator Us, one can achieve high probabilities of measurement for states corresponding to optimal and near optimal solutions while still only requiring O(π42N/M) iterations. In order to achieve these probabilities, a single scalar parameter ps is required, which we show can be found through a variational quantum–classical hybrid approach and can be used for heuristic solutions.
Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis

Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.
Efficient Object Placement via FTOPNet

Image composition involves the placement of foreground objects at an appropriate scale within a background image to create a visually realistic composite image. However, manual operations for this task are time-consuming and labor-intensive. In this study, we propose an efficient method for foreground object placement, comprising a background feature extraction module (BFEM) designed for background images and a foreground–background cross-attention feature fusion module (FBCAFFM). The BFEM is capable of extracting precise and comprehensive information from the background image. The fused features enable the network to learn additional information related to foreground–background matching, aiding in the prediction of foreground object placement and size. Our experiments are conducted using the publicly available object placement assessment (OPA) dataset. Both quantitative and visual results demonstrate that FTOPNet effectively performs the foreground object placement task and offers a practical solution for image composition tasks.
Distributed System of Methodological Support for Teachers: Analytical and Prognostic Review

The article examines the multidimensionality of the concept of "system of methodological support of teachers", describes the modern system of methodological support of teachers as a distributed system with certain structural elements and principles of their functioning. The "growth points" of the distributed system of methodological support of teachers are determined.
Teaching Metacognitive Skills and Methods of Assessing Their Formation in Connection with Academic Success

This paper examines the form of teaching metacognitive skills in the teaching sphere in a secondary school, and studies methods for assessing the formation of these skills in relation to academic success. The author identifies the specifics of methods for assessing the formation of these skills as component for the formation of competencies in students. Teaching these skills is considered as part of the modern educational process and a factor in the formation of academic success. In this context, the object of the study is metacognitive skills, and the subject is the relationship of methods for assessing these skills with academic success. Metacognitive skills are characterized as components of universal competencies, as well as an educational conception for the formation of educational competencies in teaching and competence in professional activity. The author attempts to study the relationship of the level of metacognitive skills with academic success and its significance in the learning process.
Effectiveness of quantum annealing for continuous-variable optimization

The application of quantum annealing to the optimization of continuous-variable functions is a relatively unexplored area of research. We test the performance of quantum annealing applied to a one-dimensional continuous-variable function with a rugged energy landscape. After domain-wall encoding to map a continuous variable to discrete Ising variables, we first benchmark the performance of the real hardware, the D-Wave 2000Q, against several state-of-the-art classical optimization algorithms designed for continuous-variable problems to find that the D-Wave 2000Q matches the classical algorithms in a limited domain of computation time. Beyond this domain, classical global optimization algorithms outperform the quantum device. Next we examine several optimization algorithms that are applicable to the Ising formulation of the problem, such as the time-evolving block decimation (TEBD) to simulate ideal coherent quantum annealing, simulated annealing, simulated quantum annealing, and spin-vector Monte Carlo. The data show that TEBD's coherent quantum annealing achieves far better results than the other approaches, demonstrating the effectiveness of coherent tunneling. From these two types of benchmarks, we conclude that the hardware realization of quantum annealing has the potential to significantly outperform the best classical algorithms if thermal noise and other imperfections are sufficiently suppressed and the device operates coherently, as demonstrated in recent short-time quantum simulations.
Sample-efficient model-based reinforcement learning for quantum control

We propose a model-based reinforcement learning (RL) approach for noisy time-dependent gate optimization with reduced sample complexity over model-free RL. Sample complexity is defined as the number of controller interactions with the physical system. Leveraging an inductive bias, inspired by recent advances in neural ordinary differential equations (ODEs), we use an autodifferentiable ODE, parametrized by a learnable Hamiltonian ansatz, to represent the model approximating the environment, whose time-dependent part, including the control, is fully known. Control alongside Hamiltonian learning of continuous time-independent parameters is addressed through interactions with the system. We demonstrate an order of magnitude advantage in sample complexity of our method over standard model-free RL in preparing some standard unitary gates with closed and open system dynamics, in realistic computational experiments incorporating single-shot measurements, arbitrary Hilbert space truncations, and uncertainty in Hamiltonian parameters. Also, the learned Hamiltonian can be leveraged by existing control methods like GRAPE (gradient ascent pulse engineering) for further gradient-based optimization with the controllers found by RL as initializations. Our algorithm, which we apply to nitrogen vacancy (NV) centers and transmons, is well suited for controlling partially characterized one- and two-qubit systems.
FPGA-Based True Random Number Generator Architecture Using 15-Bit LFSR and ADPLL

A true random number generator (TRNG) based on a linear feedback shift register (LFSR) and an all-digital phase-locked loop (ADPLL) is a type of hardware-based random number generator that uses the principles of both LFSR and an ADPLL to generate random numbers. We extend an approach that uses on-chip jitter and metastability state in this study. 15-bit LFSR with ADPLL-based TRNG (15-LAT) architecture is created with the help of ring oscillators, flip-flops, ADPLL, 15-bit LFSR, and other physical devices that generate various sources of entropy. When compared to other existing TRNG designs, we established that our novel design lower the complexity as well as decreased the power usage (0.072W) due to the use of minimum FPGA hardware resources. This architecture offers better performance over conventional TRNGs and opens up possibilities to design a more efficient, low-power producing highly reliable TRNG. The architecture was tested on an Artrix-7 FPGAs evaluation board with positive results. A digital storage oscilloscope (DSO) is used to record the resulting pattern and FFT pattern of the corresponding wave. We provide the results of statistical analyses performed on the output bit sequence generated by our design using the MATLAB tool. The NIST SP 800-22 assessment demonstrates that the output TRNG bitstreams are unpredictable and stochastic, suggesting that the suggested architecture is better suited for cryptographic applications.
Efficient and quantum-adaptive machine learning with fermion neural networks

Application of machine learning to quantum data and models has been held back by the lack of adaptability and efficiency in present-day neural networks. This study proposes fermion neural networks for quantum-adaptive machine learning with direct applications to complex quantum systems, and offers in situ analysis without preprocessing or presumption. An efficient optimization comparable to back propagation is established, enabling competitive performance in challenging machine-learning benchmarks. Fermion neural networks' quantum properties, such as entanglement and correlation, also bring various advantages and insights.
Intelligent forecasting system for rooftop solar plants

The dependency of the off-grid customer on a non-conventional energy sources like solar energy is always not suggested due to the intermittent nature of the power generated. Forecasting the output power helps even the amateur solar roof top consumers to make plan of their self-consumption strategies and also make necessary arrangements for alternate source of energy while minimizing their dependencies on energy storage devices to a great extent. One of the major difficulties faced during the forecasting is procurement of the authentic and accurate digital data since the accuracy of the implemented algorithm depends on many parameters. This work presents a low cost solution for roof top solar plants using intelligent technique. This implemented system has two parts, first one for acquisition and digitization of data with Internet of Things (IoT) and Raspberry Pi and the other part is building a day ahead forecasting model using the deep learning 2D convolution approach with the temporal resolution of 1 h. The data acquisition system collects the power related parameters, such as voltage, current, frequency, active power, and power factor and the related weather parameters, such as temperature, light intensity, and humidity and then appends into Google Sheets using IoT. The error in forecasting the solar power output using these data for 12 months is found to be 11.55%.
Real-Time Object Detection for Unmanned Underwater Vehicles Using Movidius Neural Compute Stick

Deep learning-based object detection methods have demonstrated promising results in controlled environments. Due to specific limitations, these methods are insufficient for unstructured environments, such as underwater exploration or espionage using unmanned underwater vehicles (UUV). Poor image quality is the primary issue limiting UUV’s functionality. Underwater objects are typically small and blurry as a result of severe noise that confuses the detectors. This study presents a graph convolution neural network (GCNN)-based real-time object tracking system in an unstructured environment. On the algorithm side, object’s regions are categorized based on the pixel population density. Resultantly, the regions are disintegrated recursively into an image patch tree. Moreover, the surfaces are subsequently and proficiently stored, later retrieved as patches. To keep the power consumption low, a PYNQ Z2 FPGA is used as development platform in connection with a Movidius NCS stick for the outsourcing of the GCNN. The proposed technique is generic and reducing high computational complexity and power consumption.
Fortified MapReduce Layer: Elevating Security and Privacy in Big Data

In today's digital landscape, the widespread sharing and utilization of raw data are integral in social, medical, agricultural, and academic domains. The surge of open platforms has led to exponential growth in data, transforming it into what we now call Big Data (BD). However, the traditional BD model lacks a specific mechanism for capturing the sensitivity of data, leaving it vulnerable to potential breaches. To address this, a privacy and security layer is crucial. This paper propose a novel solution called the Fortified Secured Map Reduce (FSMR) Layer, which serves as an intermediary between the HDFS (Hadoop Distributed File System) and MR (Map Reduce) Layer. The FSMR model is designed to foster data sharing for knowledge mining while ensuring robust privacy and security guarantees. It effectively resolves scalability issues concerning privacy and strikes a balance between privacy and utility for data miners. By implementing the FSMR model, we achieve remarkable improvements in running time and information loss compared to existing approaches. Furthermore, storage and CPU utilization are minimized, enhancing the overall efficiency and effectiveness of the data processing pipeline. The outcome of our work lies in promoting data sharing while safeguarding sensitive information, making it a significant step towards secure and privacy-conscious BD processing.
Automated brain segmentation for guidance of ultrasonic transcranial tissue pulsatility image analysis

Tissue pulsatility imaging is an ultrasonic technique that can be used to map regional changes in blood flow in the brain. Classification of regional differences in pulsatility signals can be optimized by restricting the analysis to brain tissue. For 2D transcranial ultrasound imaging, we have implemented an automated image analysis procedure to specify a region of interest in the field of view that corresponds to brain. Our segmentation method applies an initial K-means clustering algorithm that incorporates both echo strength and tissue displacement to identify skull in ultrasound brain scans. The clustering step is followed by processing steps that use knowledge of the scan format and anatomy to create an image mask that designates brain tissue. Brain regions were extracted from the ultrasound data using different numbers of K-means clusters and multiple combinations of ultrasound data. Masks generated from ultrasound data were compared with reference masks derived from Computed Tomography (CT) data. A segmentation algorithm based on ultrasound intensity with two K-means clusters achieves an accuracy better than 80% match with the CT data. Some improvement in the match is found with an algorithm that uses ultrasound intensity and displacement data, three K-means clusters, and addition of an algorithm to identify shallow sources of ultrasound shadowing. Several segmentation algorithms achieve a match of over 80% between the ultrasound and Computed Tomography brain masks. A final tradeoff can be made between processing complexity and the best match of the two data sets.
Examining librarians’ practices of facilitating learning in makerspaces

The purpose of this project is to understand how practitioners conceptualize learning and facilitate learning that takes place in makerspaces in public libraries. While research has highlighted the educational benefits of learning through making in makerspaces, library practitioners may lack the specialized knowledge required to support informal learning, including using emerging equipment and software, and scaffolding activities that promote learning (Koh &amp; Abbas, 2016; Li &amp; Todd, 2019). This qualitative study employs individual semi-structured interviews with makerspace managers and facilitators from public libraries to understand their perspectives. Five interviews have been conducted so far, with additional interviews expected. Preliminary data analysis indicates that practitioners perceive learning as predominantly occurring during demonstrations of equipment and software use. Project-based learning is also noted as a key aspect of makerspaces. Practitioners identified challenges associated with facilitating learning in these environments, including limited time available to work with individual patrons. This study’s findings will have practical implications for librarians and library science educators to better equip future makerspace practitioners.