Reviewerly/exHarmony huggingface dataset:
Enhancing Feature Extraction through G-PLSGLR by Decreasing Dimensionality of Textual Data

The technology of big data has become highly popular in numerous industries owing to its various characteristics such as high value, large volume, rapid velocity, wide variety, and significant variability. Nevertheless, big data presents several difficulties that must be addressed, including lengthy processing times, high computational complexity, imprecise features, significant sparsity, irrelevant terms, redundancy, and noise, all of which can have an adverse effect on the performance of feature extraction. The objective of this research is to tackle these issues by utilizing the Partial Least Square Generalized Linear Regression (G-PLSGLR) approach to decrease the high dimensionality of text data. The suggested algorithm is made up of four stages: Firstly, gathering featured data in vector space model (VSM) and training it with bootstrap technique. Second, grouping trained feature samples using a Pearson correlation coefficient and graph-based technique. Third, getting rid of unimportant features by ranking significant group features using PLSGR. Lastly, choosing or extracting significant features using Bayesian information criterion (BIC). The G-PLSGLR algorithm surpasses current methods by achieving a high reduction rate and classification performance, while minimizing feature redundancy, time consumption, and complexity. Furthermore, it enhances the accuracy of features by 35%.
Interpolation of Low-Resolution Images for Improved Accuracy Using an ANN Quadratic Interpolator

The era of digital imaging has transitioned into a new one. Conversion to real-time, high-resolution images is considered vital. Interpolation is employed in order to increase the number of pixels per image, thereby enhancing spatial resolution. Interpolation's real advantage is that it can be deployed on user end devices. Despite raising the number of pixels per inch to enhances the spatial resolution, it may not improve the image's clarity, hence diminishing its quality. This strategy is designed to increase image quality by enhancing image sharpness and spatial resolution simultaneously. Proposed is an Artificial Neural Network (ANN) Quadratic Interpolator for interpolating 3-D images. This method applies Lagrange interpolating polynomial and Lagrange interpolating basis function to the parameter space using a deep neural network. The degree of the polynomial is determined by the frequency of gradient orientation events within the region of interest. By manipulating interpolation coefficients, images can be upscaled and enhanced. By mapping between low- and high-resolution images, the ANN quadratic interpolator optimizes the loss function. ANN Quadratic interpolator does a good work of reducing the amount of image artefacts that occur during the process of interpolation. The weights of the proposed ANN Quadratic interpolator are seeded by transfer learning, and the layers are trained, validated, and evaluated using a standard dataset. The proposed method outperforms a variety of cutting-edge picture interpolation algorithms..
FAIR degree assessment in agriculture datasets using the F-UJI tool

For the agricultural scientific community, data sharing is crucial both for the advancement of the discipline and ability to meet global challenges, such as the target no. 2, i.e., “Zero Hunger,” of the Sustainable Development Goals (SDG 2030). In this context, FAIR (Findable, Accessible, Interoperable and Reusable) principles play an important role, as they guarantee the findability, accessibility, interoperability, and reusability of shared data. To improve the practice of data sharing, institutions, funders, and publishers are increasingly demanding data be shared as well as be of an acceptable level of quality, including compliance with FAIR principles. Therefore, the objective of this work is twofold: first, this research aims to determine the degree of compliance with the FAIR principles exhibited by a number of datasets; and second, it aims to explore useful and valid methodologies and procedures that can be used to perform this evaluation quickly, automatically, and effectively. For this purpose, the Data Citation Index (DCI) was used to obtain many datasets in the field of agriculture, which were further grouped by repositories and evaluated using the automated assessment tool F-UJI provided by the FAIRsFAIR project. The results indicated that the principle that exhibited the highest scores was “Findable”, while “Reusable” received the lowest scores, as none of the analysed repositories achieved a 50% compliance score in this respect. The datasets published in the Zenodo and Dryad repositories exhibited better overall results in terms of the FAIR principles, and the AG Commons repository was the third best rated repository, representing only one of the first three repositories belonging to the agricultural sector. Regarding the use of F-UJI as an automated assessment tool and DCI as a source for obtaining datasets, we conclude that this methodology is useful, and that although it can be improved, it is easy to use and implement by other scientific groups and agents of interest.
A2OURSR: Adaptive adjustment based real MRI super-resolution via opinion-unaware measurements

High-quality and high-resolution magnetic resonance (MR) images can provide more details for diagnosis and analyses. Recently, MR images guided neurosurgery has become an emerging technique in clinics. Unlike other medical imaging techniques, it is impossible to achieve both real-time imaging and high image quality in MR imaging. The real-time performance is closely related to the nuclear magnetic equipment itself as well as the collection strategy of the k space data. Optimizing the imaging time cost via the corresponding algorithm is harder than enhancing image quality. Further, in reconstructing low-resolution and noise-rich MR images, getting relatively high-definition and resolution MR images as references are difficult or impossible. In addition, the existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. As a result, severely bad results are inevitable when the modeling assumptions are far apart from the actual situation. To address these problems, we propose a novel adaptive adjustment method based on real MR images via opinion-unaware measurements for real super-resolution (A2OURSR). It can estimate the degree of blur and noise from the test image itself using two scores. These two scores can be considered pseudo labels to train the adaptive adjustable degradation estimation module. Then, the outputs of the above model are used as the inputs of the conditional network to tweak the generated results. Thus, the results can be automatically adjusted via the whole dynamic model. Extensive experimental results show that the proposed A2OURSR is superior to state-of-the-art methods on benchmarks quantitatively and visually.
EHR privacy preservation using federated learning with DQRE-Scnet for healthcare application domains

A distributed learning technique named Federated Learning (FL) is utilized by mobile devices, clinical research labs, and hospitals for secure healthcare data sharing. FL has been utilized by several researchers for preserving the privacy of Electronic Health Record (EHR) information while updating the global server. Still, the privacy and higher number of communication rounds limit the performance of the model. Hence, this research introduces the privacy-preserving EHR information sharing technique, FL, based on deep Q reinforcement learning with spectral clustering (FL+DQRE-SCnet). In this, the local healthcare information is trained using the DCNN in the respective hospitals based on FL criteria. After learning the local model, the information is shared with the global model for updating the EHR to enhance the accuracy of disease diagnosis. The global model aggregates the information from the local models over several communication rounds by randomly selecting the local models, increasing the need for more communication rounds. Therefore, DQRE-SCnet is introduced before data aggregation to minimize the communication rounds. In addition, including a homomorphic encryption technique ensures privacy protection for FL data sharing. The analysis of FL+DQRE-SCnet based on the assessment measures of accuracy, precision, recall, and F-measure acquired maximal values of 95%, 94.9%, 94.94%, and 93.94%, respectively. Also, the error rate of 0.46 and the execution time of 1400 sec are accomplished by the FL+DQRE-SCnet method. Here, for comparing the performance of the model, the baseline methods like FedAvg, LoAdaBoost, BOFRF, PP-FedAvg and AdaFed are considered, wherein the proposed method acquired superior performance.
Self-supervised monocular depth estimation for gastrointestinal endoscopy

Background and objective: Gastrointestinal (GI) endoscopy represents a promising tool for GI cancer screening. However, the limited field of view and uneven skills of endoscopists make it remains difficult to accurately identify polyps and follow up on precancerous lesions under endoscopy. Estimating depth from GI endoscopic sequences is essential for a series of AI-assisted surgical techniques. Nonetheless, depth estimation algorithm of GI endoscopy is a challenging task due to the particularity of the environment and the limitation of datasets. In this paper, we propose a self-supervised monocular depth estimation method for GI endoscopy. Methods: A depth estimation network and a camera ego-motion estimation network are firstly constructed to obtain the depth information and pose information of the sequence respectively, and then the model is enabled to perform self-supervised training by calculating the multi-scale structural similarity with L1 norm (MS-SSIM+L1) loss function between the target frame and the reconstructed image as part of the loss of the training network. The MS-SSIM+L1 loss function is good for reserving high-frequency information and can maintain the invariance of brightness and color. Our model consists of the U-shape convolutional network with the dual-attention mechanism, which is beneficial to capture muti-scale contextual information, and greatly improves the accuracy of depth estimation. We evaluated our method qualitatively and quantitatively with different state-of-the-art methods. Results and conclusions: The experimental results manifest that our method has superior generality, achieving lower error metrics and higher accuracy metrics on both the UCL dataset and the Endoslam dataset. The proposed method has also been validated with clinical GI endoscopy, demonstrating the potential clinical value of the model.
Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning

Large corporations, government entities and institutions such as hospitals and census bureaus routinely collect our personal and sensitive information for providing services. A key technological challenge is designing algorithms for these services that provide useful results, while simultaneously maintaining the privacy of the individuals whose data are being shared. Differential privacy (DP) is a cryptographically motivated and mathematically rigorous approach for addressing this challenge. Under DP, a randomized algorithm provides privacy guarantees by approximating the desired functionality, leading to a privacy–utility trade-off. Strong (pure DP) privacy guarantees are often costly in terms of utility. Motivated by the need for a more efficient mechanism with better privacy–utility trade-off, we propose Gaussian FM, an improvement to the functional mechanism (FM) that offers higher utility at the expense of a weakened (approximate) DP guarantee. We analytically show that the proposed Gaussian FM algorithm can offer orders of magnitude smaller noise compared to the existing FM algorithms. We further extend our Gaussian FM algorithm to decentralized-data settings by incorporating the CAPE protocol and propose capeFM. Our method can offer the same level of utility as its centralized counterparts for a range of parameter choices. We empirically show that our proposed algorithms outperform existing state-of-the-art approaches on synthetic and real datasets.
Deep Learning–Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise

BACKGROUND. Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. OBJECTIVE. The purpose of this study was to evaluate the usefulness of deep learning–based CAD software on the diagnostic performance of radiologists without breast ultrasound expertise at secondary or rural hospitals in the differentiation of benign and malignant breast lesions measuring up to 2.0 cm on ultrasound. METHODS. This prospective study included patients scheduled to undergo biopsy or surgical resection at any of eight participating secondary or rural hospitals in China of a breast lesion classified as BI-RADS category 3–5 on prior breast ultrasound from November 2021 to September 2022. Patients underwent an additional investigational breast ultrasound, performed and interpreted by a radiologist without breast ultrasound expertise (hybrid body/breast radiologists, either who lacked breast imaging subspecialty training or for whom the number of breast ultrasounds performed annually accounted for less than 10% of all ultrasounds performed annually by the radiologist), who assigned a BI-RADS category. CAD results were used to upgrade reader-assigned BI-RADS category 3 lesions to category 4A and to downgrade reader-assigned BI-RADS category 4A lesions to category 3. Histologic results of biopsy or resection served as the reference standard. RESULTS. The study included 313 patients (mean age, 47.0 ± 14.0 years) with 313 breast lesions (102 malignant, 211 benign). Of BI-RADS category 3 lesions, 6.0% (6/100) were upgraded by CAD to category 4A, of which 16.7% (1/6) were malignant. Of category 4A lesions, 79.1% (87/110) were downgraded by CAD to category 3, of which 4.6% (4/87) were malignant. Diagnostic performance was significantly better after application of CAD, in comparison with before application of CAD, in terms of accuracy (86.6% vs 62.6%, p < .001), specificity (82.9% vs 46.0%, p < .001), and PPV (72.7% vs 46.5%, p < .001) but not significantly different in terms of sensitivity (94.1% vs 97.1%, p = .38) or NPV (96.7% vs 97.0%, p > .99). CONCLUSION. CAD significantly improved radiologists' diagnostic performance, showing particular potential to reduce the frequency of benign breast biopsies. CLINICAL IMPACT. The findings indicate the ability of CAD to improve patient care in settings with incomplete access to breast imaging expertise.
Leaky FinFET for Reservoir Computing with Temporal Signal Processing

Reservoir computing can greatly reduce the hardware and training costs of recurrent neural networks with temporal data processing. To implement reservoir computing in a hardware form, physical reservoirs transforming sequential inputs into a high-dimensional feature space are necessary. In this work, a physical reservoir with a leaky fin-shaped field-effect transistor (L-FinFET) is demonstrated by the positive use of a short-term memory property arising from the absence of an energy barrier to suppress the tunneling current. Nevertheless, the L-FinFET reservoir does not lose its multiple memory states. The L-FinFET reservoir consumes very low power when encoding temporal inputs because the gate serves as an enabler of the write operation, even in the off-state, due to its physical insulation from the channel. In addition, the small footprint area arising from the scalability of the FinFET due to its multiple-gate structure is advantageous for reducing the chip size. After the experimental proof of 4-bit reservoir operations with 16 states for temporal signal processing, handwritten digits in the Modified National Institute of Standards and Technology dataset are classified by reservoir computing.
Testing Language Aptitude

The chapter will first provide an overview of the developmental stages of the LLAMA aptitude test battery since its inception in 2005; then, more importantly, the chapter moves on to discuss the potential limitations of the current format in terms of internal and external validity. Towards the end, it will outline some new features and changes that have recently been brought to this increasingly popular language aptitude test battery. Data from the new online LLAMA tests will be reported (from LLAMA to ALPACAA and back again). The new LLAMA tests are now available online on Meara’s lognostics website: http://www.lognostics.co.uk/tools/LLAMA_3/index.htm.
Automated Traffic Surveillance Using Existing Cameras on Transit Buses

Millions of commuters face congestion as a part of their daily routines. Mitigating traffic congestion requires effective transportation planning, design, and management. Accurate traffic data are needed for informed decision making. As such, operating agencies deploy fixed-location and often temporary detectors on public roads to count passing vehicles. This traffic flow measurement is key to estimating demand throughout the network. However, fixed-location detectors are spatially sparse and do not cover the entirety of the road network, and temporary detectors are temporally sparse, providing often only a few days of measurements every few years. Against this backdrop, previous studies proposed that public transit bus fleets could be used as surveillance agents if additional sensors were installed, and the viability and accuracy of this methodology was established by manually processing video imagery recorded by cameras mounted on transit buses. In this paper, we propose to operationalize this traffic surveillance methodology for practical applications, leveraging the perception and localization sensors already deployed on these vehicles. We present an automatic, vision-based vehicle counting method applied to the video imagery recorded by cameras mounted on transit buses. First, a state-of-the-art 2D deep learning model detects objects frame by frame. Then, detected objects are tracked with the commonly used SORT method. The proposed counting logic converts tracking results to vehicle counts and real-world bird’s-eye-view trajectories. Using multiple hours of real-world video imagery obtained from in-service transit buses, we demonstrate that the proposed system can detect and track vehicles, distinguish parked vehicles from traffic participants, and count vehicles bidirectionally. Through an exhaustive ablation study and analysis under various weather conditions, it is shown that the proposed method can achieve high-accuracy vehicle counts.
Gaussian Process-based calculation of look-elsewhere trials factor

Abstract In high-energy physics it is a recurring challenge to efficiently and precisely (enough) calculate the global significance of, e.g., a potential new resonance. We propose a new method that models the significance in the search region as a Gaussian Process. The kernel of the Gaussian Process is approximated with a covariance matrix and is calculated with a carefully designed set of background-only data sets, comparable in number to the random background-only data sets used in a typical analysis that relies on the average upcrossings of the significance. The trials factor for both low and moderate significances can subsequently be calculated to the desired precision with a computationally inexpensive random sampling of the Gaussian Process. In addition, once the covariance of the Gaussian Process is determined, the average number of upcrossings can be computed analytically. In our work we give some highlights of the analytic calculation and also discuss some peculiarities of the trials factor estimation on a finite grid. We illustrate the method with studies of three complementary statistical models.
Concealed Quantum Telecomputation for Anonymous 6G URLLC Networks

Distributed learning and multi-tier computing are the key ingredients to ensure ultra-reliable and low-latency communication (URLLC) in 6G networks. The distinct transition from connected things in 5G URLLC networks to connected intelligence in 6G URLLC networks requires ultra-secure communication due to the massive amount of private data. However, it is a challenging task to ensure stringent 6G URLLC requirements along with user privacy and data security in distributed networks. In this paper, we devise a distributed quantum computation protocol to perform a nonlocal controlled unitary operation on a bipartite input state in concealed and counterfactual manner and integrate it with anonymous quantum communication networks. This distributed protocol allows Bob to apply an arbitrary singlequbit unitary operator on Alice’s qubit in a controlled and probabilistic fashion, without revealing the operator to her and without transmitting any physical particle over the quantum channel-called the counterfactual concealed telecomputation (CCT). It is shown that the CCT protocol neither requires the preshared entanglement nor depends on the bipartite input state and that the single-qubit unitary teleportation is a special case of CCT. The quantum circuit for CCT can be implemented using the (chained) quantum Zeno gates. The protocol becomes deterministic with simplified circuit implementation if the initial composite state of Alice and Bob is a Bell-type state. Furthermore, we provide numerical examples of quantum anonymous broadcast networks using the CCT protocol and show their degrees of anonymity in the presence of malicious users.
Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling

Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of what precisely common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD. We simulated FTD-like narratives using GPT-2 by either increasing word selection stochasticity or limiting the model’s memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and ‘tangentiality’ (how quickly meaning drifts away from the topic) in detecting, and dissociating, the two underlying impairments. Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were non-monotonic, in that ‘forgetting’ the global context resulted in sentences that were semantically closer to their local, intermediate, context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance. This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.