Reviewerly/exHarmony huggingface dataset:
Explainability in Automatic Short Answer Grading

Massive open online courses and other online study opportunities are providing easier access to education for more and more people around the world. To cope with the large number of exams to be assessed in these courses, AI-driven automatic short answer grading can recommend teaching staff to assign points when evaluating free text answers, leading to faster and fairer grading. But what would be the best way to work with the AI? In this paper, we investigate and evaluate different methods for explainability in automatic short answer grading. Our survey of over 70 professors, lecturers and teachers with grading experience showed that displaying the predicted points together with matches between student answer and model answer is rated better than the other tested explainable AI (XAI) methods in the aspects trust, informative content, speed, consistency and fairness, fun, comprehensibility, applicability, use in exam preparation, and in general.
AdaSmooth: An Adaptive Learning Rate Method Based on Effective Ratio

It is well known that we need to choose the hyper-parameters in Momentum, AdaGrad, AdaDelta, and other alternative stochastic optimizers. While in many cases, the hyper-parameters are tuned tediously based on experience becoming more of an art than science. We introduce a novel per-dimension learning rate method for stochastic gradient optimization called AdaSmooth. The method is insensitive to hyper-parameters thus it requires no manual tuning of the hyper-parameters like Momentum, AdaGrad, and AdaDelta methods. We show promising results compared to other methods on different convolutional neural networks, multi-layer perceptron, and alternative machine learning tasks. Empirical results demonstrate that AdaSmooth works well in practice and compares favourably to other stochastic optimization methods in neural networks.
Comparison of Cell Nuclei Classification in Cytological Breast Images Using Machine Learning Algorithms

Breast cancer is the most common cancer among Indian women. In every 4 min, one lady is diagnosed with breast cancers, and in every 13 min, one woman dies of breast cancer in India. Hence detection of the breast cancer at an early stage is very important. Fine needle aspiration cytology and histopathological biopsies are golden truth for detection of breast cancer. To increase the accuracy of detection, computer-aided diagnosis is used as a second opinion. In computer-assisted pathology analysis, breast cytological image classification is a most important task as it helps in understanding the structure and distribution of nucleus for the prediction of breast cancer. In this paper, comparative analysis is done using five different machine learning algorithms on publicly available Wisconsin diagnostic dataset from UCI machine learning repository. The accuracy of support vector machine, random forest, K-nearest neighbour, decision tree and logistic regression was found to be 97.36%, 95.61%, 96.49%, 94.73% and 97.36%, respectively. The authors conclude that support vector machine and logistic regression perform equally better in the terms of accuracy.
Analysis of Existing Algorithms for Verifying Gurmukhi Scripts and the Shortfall

Handwriting in context of writer identification and verification are important areas of Forensic Science. In English language, a considerable amount of technological support has been achieved but in Gurmukhi script a lot needs to be done. Some researchers have worked and achieved success in writer identification, but in the area of writer verification, the expert opinions before the courts of law are manual-based which can be named as conjectures. The courts do not feel convinced with such opinions and decide the case otherwise. This author has made a humble beginning to use computer technology for handwriting verification. For the purpose, ten unique characteristics of Gurmukhi script have been identified, and underscoring the need for computer methods. In implementing these methods, this author has suggested some characteristics like measurements of height and width of characters in the word, their angularity like slant measurement, proportional size of characters, etc. have been found applicable in computer methodology to make verification of Gurmukhi handwriting technology-based and capable of convincing to the judges handling cases involving document verification (of Gurmukhi script) as a crucial piece of evidence. The findings of the paper based on 30 samples are a beginning that needs to be carried forward to develop a full-fledged branch of forensics.
Ridge Regression for PSNR of Restored Images by Recursive Median Filter

The study and development of image noise removal algorithm (INRA) are very important research area, where the goal is to remove unwanted noise from an image such that maximum significant information can be observed in noise removed image. Peak-signal-to-noise-ratio (PSNR) and execution time (ET) are the main criteria for INRA evaluation. In this paper, ridge regression models of degree three have been developed for prediction of PSNR of recursive median filter-based INRA without its implementation. Prediction of PSNR of INRA by regression model is a novel contribution which helps to save ET. Accurate prediction is a challenging problem due to randomness in noise, images, and sampled data. Theory based on Bias Variance Decomposition Theorem (BVDT) and minimum testing error has been used to select ‘good’ regression models. Experiments ensure computation time advantage by a factor of $$10^{-5}$$ at the cost of 1% and 10% error in PSNR values of the database with one and ten images, respectively.
Manual Transcription

The decisions about how, when, and through whom to get data transcribed can influence the quality of data, and what you are able to get out of your data, in the end. The purpose of this chapter is to bestow the reader with knowledge that will make those decisions easier.
On Density Extrema for Digital Discs

The act of characterizing and measuring different attributes of primitive shapes is of paramount importance in the subject of discrete geometry. A very rich collection of work can be found in this domain, which is predominantly focused on the Euclidean space. The digital space, on the contrary, is relatively unexplored, possibly because of the fact that it has evolved much later and it does not readily migrate to the continuous space. This work studies the unique problem of characterizing density extrema (of integer points) for discs moving on the integer plane. To the best of our knowledge, there has not been a significant study in this direction, which motivates us to look into this problem. As ‘density’ provides a notion of the relative concentration or rarefaction of a collection of points within a given shape or region, it has applications in image analysis and related areas, apart from different branches of physical science. We present some novel results, which are fundamental to understanding density minima and maxima for circular shapes in digital space. We have also pointed out a few more interesting problems that might advance this study further ahead.
Potential Implementations of Blockchain Technology in Patient Safety: A High-Level Overview

BlockchainBlockchain technologyTechnology (BCT), or more specifically its ability to maintain an immutableImmutable distributed ledgerDistributed ledger, is uniquely suited to a broad range of applications within patient safetyPatient safety. Potential use cases include, but are not limited to, medication safetySafety, patient identification, event trackingTracking, critical inventoryInventory management, internet-of-things (IoT)Internet-Of-Things (IOT), with many other potential uses to be explored. In this chapterChapter, we explore keyKey applications and considerations related to blockchain technologyTechnology use in the area of patient safetyPatient safety. In addition to outlining potential existing and future use cases, we will also dedicate a significant portion of the chapterChapter to known and proposed implementations of blockchainBlockchain technologyTechnology across healthcareHealthcare environmentsEnvironment, both clinical and non-clinicalNon-clinical.
AI Super-Resolution: Application to Turbulence and Combustion

Abstract This article summarizes and discusses recent developments with respect to artificial intelligence (AI) super-resolution as a subfilter model for large-eddy simulations. The focus is on the application of physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for subfilter closure in turbulence and combustion applications. A priori and a posteriori results are presented for various applications, ranging from decaying turbulence to finite-rate chemistry flows. The high accuracy of AI super-resolution-based subfilter models is emphasized, and advantages and shortcoming are described.
Use of Blockchain Technology for Implantable Medical Device Tracking

A blockchainBlockchain is an auspicious tool in the era of technological advancementsAdvancement. As a form of horizontal innovationInnovation, blockchain technologyTechnology will drastically change the healthcareHealthcare industryIndustry in consecutive years, addressing health information exchangeHealth information exchange's main problematic points, such as varying dataData standardsStandard, inconsistent rules and permissions, security breachesSecurity breach, and high data transmissionTransmission costsCosts [1]. While blockchainBlockchain certainly is not a one cure for all solution, it does offer a new distributed frameworkFramework to magnify and boost the integration of health care informationInformation amongst a range of uses and participants. This chapterChapter aims to review current blockchain technologyTechnology applications, particularly in implantable medical devicesMedical devices, raising awareness about this technologyTechnology and promoting future interest in that topic for the researchResearch community.
HBMD-FL: Heterogeneous Federated Learning Algorithm Based on Blockchain and Model Distillation

Federated learning is a distributed machine learning framework that allows participants to keep their privacy data locally. Traditional federated learning coordinates participants collaboratively train a powerful global model. However, this process has several problems: it cannot meet the heterogeneous model’s requirements, and it cannot resist poisoning attacks and single-point-of-failure. In order to resolve these issues, we proposed a heterogeneous federated learning algorithm based on blockchain and model distillation. The problem of fully heterogeneous models that are hard to aggregate in the central server can be solved by leveraging model distillation technology. Moreover, blockchain replaces the central server in federated learning to solve the single-point-of-failure problem. The validation algorithm is combined with cross-validation, which helps federated learning to resist poison attacks. The extensive experimental results demonstrate that HBMD-FL can resist poisoning attacks while losing less than 3 $$\%$$ of model accuracy, and the communication consumption significantly outperformed the comparison algorithm.
Hierarchical Identity Based Inner Product Functional Encryption for Privacy Preserving Statistical Analysis Without q-type Assumption

Statistical analysis is a popular research tool used by scientists, industries, and governments to summarize data in a meaningful way. It is an interesting problem to explore how statistical analysis could be performed in a hierarchical environment while preserving the privacy of individuals' data. Hierarchical identity-based inner product functional encryption (HID-IPFE), introduced by Song et al. (Information Sciences 2021), is a promising cryptographic primitive with applications in the context of privacy-preserving statistical analysis in a hierarchical system. Song et al. provided the construction of a selective chosen-plaintext attack (CPA) secure HID-IPFE scheme in the public key setting based on the q-decisional bilinear Diffie-Hellman exponent (q-DBDHE) problem in the standard model. Motivated by the goal to design HID-IPFE with minimal security assumption, we remove stronger q-type non-standard assumption and propose a selective CPA secure HID-IPFE scheme from the standard decisional bilinear Diffie-Hellman (DBDH) assumption. Our scheme provides stronger security guarantees against selective CPA adversaries compared to the existing q-DBDHE assumption based HID-IPFE. We support the conjectured security of our construction by proper security analysis in the existing security model. Moreover, our scheme offers favorable results in terms of online computation expenses when contrasted with the previous work.
Incorporated Tri Pramana Philosophy on Learning Science in Elementary School Culture

Tri Pramana, which is constituted of two major components, denotes the three life strengths to comprehend and believe in a certain event in Bhuwana Alit with manacikapura as the three powers of living beings, including (1) Bayu as the breath power, (2) Sabda as the sound power, and (3) Idep as the mind power. As delineated in Widhi Tatwa, Tri Pramana encompasses Praktyaksa Pramana, Anumana Pramana, and Agama Pramana (Sabda Pramana), which are possessed by humans, the most flawless life forms to realistically and abstractly grasp objective truths. The current study aims to incorporate the Tri Pramana principles into science learning by conducting a descriptive qualitative methodology, including a literature review, observation, interviews, and documentation. The results demonstrated the high relevance and effectiveness of Tri Pramana in science learning in elementary schools by incorporating the Upadesa and Whraspati principles Kalpa in Widhi Tatwa. Specifically, the three Tri Pramana elements were significantly interrelated to facilitate elementary students’ science learning.
Nonsingularity of Trivium-like cascade FSRs over finite fields via semi-tensor product

AbstractIn stream cipher designing, nonsingularity is a crucial requirement to ensure that the feedback shift registers (FSRs) do not produce keys that are equivalent to one another. This study uses a semi-tensor product to examine the nonsingularity of Trivium-like cascade FSRs over a finite field. The Trivium-like cascade FSRs are expressed algebraically using the semi-tensor product, allowing them to be viewed as logical networks and introducing a novel state transition matrix. Several necessary and sufficient conditions for the nonsingularity of FSRs and Trivium-like cascade FSRs over a finite field are established by dividing the structural matrices of feedback functions into different parts. These findings are also applicable to binary FSRs and binary Trivium-like cascade FSRs.Keywords: Logical networksnonsingularitysemi-tensor productTrivium-like cascade FSRs Additional informationFundingThis document is the results of the research project funded by the National Natural Science Foundation (NNSF) of China [grant number 62273201] and Taishan Scholar Project of Shandong Province.
DIFFERENTIATING TOURMALINE SPECIES VIA CHEMISTRY AND REFLECTANCE SPECTROSCOPY AT THE GIANT COPPER PORPHYRY DEPOSIT AND ASSOCIATED TOURMALINE BRECCIA PIPES: TESTING TOURMALINE AS A MINERAL VECTOR

Abstract The A.M. breccia is part of the Giant Copper porphyry deposit in southern British Columbia. It is the only well-defined zoned tourmaline breccia pipe in the Canadian Cordillera. Tourmaline is a common alteration mineral within the A.M. breccia and is spatially associated with Cu mineralization. Observed changes in tourmaline chemistry range from alkali (schorlitic-dravitic) to calcic (feruvitic-uvitic). Tourmaline subspecies vary based on their spatial location within the A.M. breccia. Tourmaline outside of the pipe contains higher concentrations of Mg, whereas tourmaline preferentially incorporates Fe within the pipe. These chemical variations are indistinguishable in hand specimens. Spectral reflectance data were collected from 587 tourmaline grains to determine if discerning chemical changes in tourmaline can be made field-based and thus more cost-effective. Spectral reflectance differentiates tourmaline associated with mineralization and breccia textures from tourmaline occurring distal to the pipe contact or within barren tourmaline breccia pipes. Fe-rich tourmaline within the A.M. breccia shows spectral characteristics of end-member schorl (Fe-rich) spectra. Tourmaline distal to the A.M. breccia and within barren pipes demonstrates spectra of end-member dravite (Mg-rich). This grouping suggests that tourmaline subspecies can be inferred by spectral reflectance, enhancing the efficiency of tourmaline as a mineral vector. Tourmaline was also identified via airborne spectral surveys. However, the airborne spectral survey did not identify the end-member spectral properties identified by in situ analysis. Airborne spectral surveys can rapidly identify tourmaline breccia pipe exposures and expedite early stages of exploration in ore districts where tourmaline is a known gangue mineral.
Privacy and everyday users of machine translation

Abstract The concept of privacy is central to technology use, but in machine translation (MT) research the meaning of privacy, and what it represents to everyday MT users, are both often left unquestioned. This article examines user conceptualisations of privacy and their implications for the understanding of human-MT interaction. It draws on the privacy literature, on an online forum and on a survey of MT users. The findings show that while users have concerns about sharing information such as passwords, images, and contact details with MT providers, some of them can conversely see MT as a technology that provides privacy. The article discusses these findings in relation to the informational and relational dimensions of privacy. It argues that relational privacy, and the interpersonal exchanges for which MT can be used as a substitute, should be considered more explicitly in discussions of the societal implications of MT technology.
Wavelet based tone mapping (TM) enhancement to a detection system for faint and compact sources in HDR and large FOV radio scenes

For high dynamic range (HDR) and large field-of-view (FOV) radio scenes, detection of faint sources with compact support and tiny sizes in comparatively extensive celestial areas is a challenging task. In the era of the Square Kilometer Array (SKA), it will be of particular concern, and data-targeted systematic methods are put on the agenda. This paper proposes a systematic method to solve this problem. From the perspective of image processing, a multiscale image contrast enhancement (ICE) is proposed to normalize the HDR. Source representation is realized by a starlet framework, where a two-step tone mapping (TM) including an arc-tangent function compression and an aggregated gain function enhancement are embedded. According to two bionic principles, the gain function is designed to highlight faint sources, remove artifacts (e.g., halos), and avoid feature distortions. For source detection, an integration method is proposed for projections between the celestial and the planar coordinate systems. It serves for coordinate transform and source location. A detection indicator system is built, and elaborate discrimination principles are drawn up. In the experiments, this method is dedicated to the dataset of the TIFR GMRT Sky Survey (TGSS) alternative data release 1 (ADR1) to facilitate the scientific goal of discovering new sources. Results show its advantages over the state of the art methods in visual inspections and detection ratios, and preliminarily prove that it has prospects for future HDR and large FOV radio data.
Prior-combined dehazing network based on mutual learning

Single-image dehazing is an important problem for high-level computer vision tasks since the existence of haze severely degrades the recognition ability of computers. Most recent works tend to combine prior-based dehazing method with a convolutional neural network to improve the dehazing effect in real scenes. However, these methods do not tackle with the color shifts caused by prior-based methods effectively. In this paper, we propose a prior-combined dehazing network based on mutual learning. Specifically, we build two sub-networks to achieve dehazing by both supervised and unsupervised ways. The supervised sub-network is optimized by ground truth, which provides color fidelity but may acquire under-dehazed images when applied to real scenes. The unsupervised sub-network is optimized by the dehazed images of dark channel prior, which improves the generalization ability but introduces some color shifts or artifacts. Since the dehazing of these two sub-networks shows complementary advantages, a mutual learning mechanism is built for the joint optimization. And we propose a feature fusion module based on the perceptual differences to acquire the final results. The experimental results demonstrate that our method surpasses previous state-of-the-arts on both synthetic and real-world datasets.
Asymptotic Nash Equilibrium for the <i>M</i>-Ary Sequential Adversarial Hypothesis Testing Game

In this paper, we consider a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary sequential hypothesis testing problem in which an adversary is present and perturbs the distributions of the samples before the decision maker observes them. This problem is formulated as a sequential adversarial hypothesis testing game played between the decision maker and the adversary. This game is a zero-sum and strategic one. We assume the adversary is active under all hypotheses and knows the underlying distribution of observed samples. We adopt this framework as it is the worst-case scenario from the perspective of the decision maker. The goal of the decision maker is to minimize the expectation of the stopping time to ensure that the test is as efficient as possible; the adversary’s goal is, instead, to maximize the stopping time. We derive a pair of strategies under which the asymptotic Nash equilibrium of the game is attained. We also consider the case in which the adversary is not aware of the underlying hypothesis and hence is constrained to apply the same strategy regardless of which hypothesis is in effect. Numerical results corroborate our theoretical findings.
Rotation axis calibration of a 3D scanning system based on dual-turntable angle cancellation

Rotation axis calibration is crucial for high-precision automatic point cloud stitching in turntable-based 3D scanning systems. To achieve a 360° sampling with a 2D calibrator in rotation axis calibration, this paper proposes a dual-turntable angle cancellation (DTAC) method. DTAC introduces an auxiliary turntable to keep a proper relative angle between the 3D sensor and the calibrator during the calibration process. The auxiliary turntable rotates at the same and opposite angle as the main turntable and cancels the increment of the relative angle. By projecting the feature points on the planar calibrator from real-world space to virtual calibration space, the projected points all share the same rotation axis of the main turntable. Further, a layered circle center extraction (LCCE) algorithm is applied to deal with outlier data points. The algorithm uses a two-step robust estimation strategy combining RANSAC circle fitting with a median noise filter for circle center selection. The standard ball reconstruction experiment shows that the 3D system calibrated by the method achieves a mean absolute error of 0.022 mm and root mean square error of 0.025 mm within the measurement distance of 60-70 cm. Point cloud stitching experiments of different types of objects show that our method outperforms other state-of-the-art methods in stitching accuracy. The DTAC method and LCCE algorithm can improve turntable-based 3D scanning systems.