Reviewerly/exHarmony huggingface dataset:
Enforcing Fairness in Blockchain Transaction Ordering

In Blockchain networks involving multiple applications, the quality of service of an application is affected by the transaction ordering. For instance, upon issuing payment transactions, users of an application would like to be notified quickly on the transactions approval. The application can be a financial institution (such as a bank), sharing the blockchain with other such applications and is represented by a node. A node might attempt to prioritize its own transactions by including them early in blocks added to the blockchain. A fair block proposal of a node follows a random selection of the transactions among the set of pending transactions the node is aware of. On the contrary, a dishonest node includes more of its transactions at the expense of transactions of other applications. In this work, we propose a toolbox of techniques to enforce such a fair block selection. First, we design an accurate statistical test for the honesty of a proposal and explain it. We conduct experiments demonstrating the accuracy of the new validation scheme. We also describe a technique that enforces fair block selection through concise commitments on the set of pending transactions known to a node. We clarify the advantages of the new mechanisms over state-of-the-art methods.
Discovering and Measuring Malicious URL Redirection Campaigns from Fake News Domains

Malicious URLs are used to distribute malware and launch social engineering attacks. They often hide behind redirection networks to evade detection. Due to the difficulty in discovering redirection traffic in real-time, previous approaches to understanding redirection networks were reactive and passive. We propose a proactive algorithm that is able to uncover redirection networks in real-time given a small set of seed domains. Our method works in three steps: (1) collecting redirection paths, (2) clustering domains that share common nodes along redirection paths, and (3) searching for other domains co-hosted on similar IP addresses. We evaluate our method using real websites that we discovered while auditing 2,300 popular fake news sites. We seeded our algorithm with a subset of 276 fake news domains that redirect, and uncovered three large-scale redirection campaigns. We further verified that 91% of entry point domains were not new, but recently expired, re-registered, and parked on dedicated hosts. To mitigate this threat vector, we deployed our system to automatically collect newly re-registered domains and publish new redirection networks. During a five-month period, our threat intelligence reports have received over 50,000 Google Search impressions, and have been recommended by commercial vendor tools. We also reported findings to Google and Amazon Web Services, both of which have acted promptly to remove malicious artifacts. Our work offers a viable approach to continuously discover evasive redirection traffic from re-registered domains.
European Union Digital Education quality standard framework and companion evaluation toolkit

The Covid-19 pandemic positioned digital education in a new light. The need for educational institutions to develop strategies, standards and establish quality assurance across digital education became even more evident. This paper describes the four-step process of designing an interactive European Union (EU) Digital Education Quality Standard Framework and Companion Evaluation Toolkit to guide the design, delivery and evaluation of effective digital education. (1) A review of literature of existing digital education frameworks and models is presented. (2) Variables and sub-variables inherent in designing, delivering and evaluating effective digital education are identified. (3) Next the variables and sub-variables in the framework are defined. (4) The process of designing the interactive framework diagram is described with the companion evaluation toolkit outlined. The proposed framework is flexible and applicable to entities and audiences regardless of where they are in the online learning adoption process.
Precise 3-D microscopic profilometry using diffractive image microscopy and artificial neural network in single-exposure manner

• A novel method using single-exposure microscopic profilometry with artificial neural network (ANN) to measure and reconstruct accurate 3-D surfaces • The development is vital to automated optical inspection (AOI) and optical metrology for precise microscopic surface profilometry. A single-exposure microscopic profilometry using artificial neural network (ANN) was developed for 3-D profile reconstruction of precise surface geometry. Optical profilometry is an important technique widely applied in many fields. However, in addition to optical aberration of the microscope itself, tilting of a local testing surface can affect the direction of the reflected light, thus causing undesired measurement errors. To overcome these problems, various methods or optical strategies have been developed, but most of them suffer from undue measurement errors attributed to the local surface gradient formed by the measured point and its neighboring surface geometry. This study proposed a novel method that combined diffractive image microscopy (DIM) with ANN. To train the ANN model, diffractive images of various surface orientations were collected for learning the complex mapping relationships between diffractive images and their corresponding surface orientations. The proposed method could simultaneously measure the height, tilting angle and tilting direction of a local surface. 3-D surface reconstruction by the proposed method required no prior knowledge of neighboring geometric information adjacent to the measured point. Using the developed approach achieved a surface height repeatability of 0.257 μm and a surface inclined angle repeatability of 0.037°, evidencing the realization of precise 3-D microscopic profilometry.
Relative Order Analysis and Optimization for Unsupervised Deep Metric Learning

In unsupervised learning of image features without labels, especially on datasets with fine-grained object classes, it is often very difficult to tell if a given image belongs to one specific object class or another, even for human eyes. However, we can reliably tell if image C is more similar to image A than image B. In this work, we propose to explore how this relative order can be used to learn discriminative features with an unsupervised metric learning method. Instead of resorting to clustering or self-supervision to create pseudo labels for an absolute decision, which often suffers from high label error rates, we construct reliable relative orders for groups of image samples and learn a deep neural network to predict these relative orders. During training, this relative order prediction network and the feature embedding network are tightly coupled, providing mutual constraints to each other to improve overall metric learning performance in a cooperative manner. During testing, the predicted relative orders are used as constraints to optimize the generated features and refine their feature distance-based image retrieval results using a constrained optimization procedure. Our experimental results demonstrate that the proposed relative orders for unsupervised learning (ROUL) method is able to significantly improve the performance ofunsupervised deep metric learning.
Measuring In-Building Spatial-Temporal Human Distribution through Monocular Image Data Considering Deep Learning–Based Image Depth Estimation

This research estimated the spatial-temporal distribution of humans in buildings through image sensing. Inputs were the in-building network, image sequences recording the movement of human, and camera parameters. Object detection and tracking models were utilized to discover humans in the images. Image depth estimation, clustering, and the camera model were integrated for the association of human and the in-building space in the image coordinates with the real world coordinates. The temporal human count for each in-building space was acquired. To validate the approach, two real cases in a school building, at a corridor and a hallway, were tested, and a synthesized case was carried out to exclude error from the detection and tracking steps. The proposed approach achieved results comparable to those of manual counting.
Self-supervised Regularization for Text Classification

Abstract Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose SSL-Reg, a data-dependent regularization approach based on self-supervised learning (SSL). SSL (Devlin et al., 2019a) is an unsupervised learning approach that defines auxiliary tasks on input data without using any human-provided labels and learns data representations by solving these auxiliary tasks. In SSL-Reg, a supervised classification task and an unsupervised SSL task are performed simultaneously. The SSL task is unsupervised, which is defined purely on input texts without using any human- provided labels. Training a model using an SSL task can prevent the model from being overfitted to a limited number of class labels in the classification task. Experiments on 17 text classification datasets demonstrate the effectiveness of our proposed method. Code is available at https://github.com/UCSD-AI4H/SSReg.
Improved Deep Learning Explanations for Prostate Lesion Classification through Grad-CAM and Saliency Map Fusion

Explainability techniques have been recently postulated as being effective for localizing medical regions of interest. However, there has been recent criticisms regarding the validity of these approaches in terms of robustness and accuracy. This study explores if combining two saliency methods, namely Saliency Maps and Grad-CAM, improves the robustness and accuracy of localization. The experiments are three-part. First, the accuracy of localization is measured and repeatability experiments are performed to determine if the results are repeatable across model architectures. Second, cascading randomization experiments are conducted to determine the robustness to changes in model parameters. Third, we measure the overlap between Saliency Maps and Grad-CAM and show there is a relationship between the overlap and accuracy of prediction.
Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks

Anatomical segmentation of brain scans is highly relevant for diagnostics and neuroradiology research. Conventionally, segmentation is performed on T 1 -weighted MRI scans, due to the strong soft-tissue contrast. In this work, we report on a comparative study of automated, learning-based brain segmentation on various other contrasts of MRI and also computed tomography (CT) scans and investigate the anatomical soft-tissue information contained in these imaging modalities. A large database of in total 853 MRI/CT brain scans enables us to train convolutional neural networks (CNNs) for segmentation. We benchmark the CNN performance on four different imaging modalities and 27 anatomical substructures. For each modality we train a separate CNN based on a common architecture. We find average Dice scores of 86.7 ± 4.1% ( T 1 -weighted MRI), 81.9 ± 6.7% (fluid-attenuated inversion recovery MRI), 80.8 ± 6.6% (diffusion-weighted MRI) and 80.7 ± 8.2% (CT), respectively. The performance is assessed relative to labels obtained using the widely-adopted FreeSurfer software package. The segmentation pipeline uses dropout sampling to identify corrupted input scans or low-quality segmentations. Full segmentation of 3D volumes with more than 2 million voxels requires &amp;lt;1s of processing time on a graphical processing unit.
Sentiment Analysis of Student Evaluations of Teaching Using Deep Learning Approach

One part of improving and developing lecturer pedagogical competence and evaluating the substance of the courses taught in the lecture process is to use Lecturer Evaluation Effectiveness by Students (EDOM). The EDOM is a feedback questionnaire that can be used by educators to assess and evaluate pedagogical competencies that they have and aims to make continuous improvements to various matters related to the teaching process in the classroom. In addition to questions in the form of a Likert scale, EDOM also accommodates open-ended questions. However, this type of question tends not to be considered in the feedback assessment and is not explored more deeply to gain insight. Therefore, in this study text mining was carried out to find out the sentiments given by students, whether positive or negative sentiments using Convolutional Neural Network (CNN). The experimental results showed that CNN was able to achieve accuracy, precision, recall, and F1-Score of 87.95%, 87%, 78%, and 81%, respectively.
Sliced Wasserstein based Canonical Correlation Analysis for Cross-Domain Recommendation

To solve the problem of data sparsity and cold start, the cross-domain recommendation is a promising research direction in the recommender system. The goal of cross-domain recommendation is to transfer learned knowledge from the source domain to the target domain by different means to improve the performance of the recommendation. But most approaches face the distribution misalignment. In this paper, we propose a joint learning cross-domain recommendation model that can extract domain-specific and common features simultaneously, and only use the implicit feedback data of users without additional auxiliary information. To the best of our knowledge, it is the first attempt to combine the sliced Wasserstein distance and canonical correlation analysis for the cross-domain recommendation scenario. Our one intuition is to reduce the reconstruction error caused by the variational inference based autoencoder model by the optimal transportation theory. Another attempt is to improve the correlation between domains by combining the idea of the canonical correlation analysis. With rigorous experiments, we empirically demonstrated that our model can achieve better performance compared with the state-of-the-art methods.
Evaluation of enhanced darkfield microscopy and hyperspectral imaging for rapid screening of <scp>TiO<sub>2</sub></scp> and <scp>SiO<sub>2</sub></scp> nanoscale particles captured on filter media

Here we report on initial efforts to evaluate enhanced darkfield microscopy (EDFM) and light scattering Vis-NIR hyperspectral imaging (HSI) as a rapid screening tool for the offline analysis of mixed cellulose ester (MCE) filter media used to collect airborne nanoparticulate from work environments. For this study, the materials of interest were nanoscale titanium dioxide (TiO2 ) and silicon dioxide (SiO2 ; silica), chosen for their frequent use in consumer products. TiO2 and SiO2 nanoscale particles (NPs) were collected on MCE filter media and were imaged and analyzed via EDFM-HSI. When visualized by EDFM, TiO2 and SiO2 NPs were readily apparent as bright spherical structures against a dark background. Moreover, TiO2 and SiO2 NPs were identified in hyperspectral images. EDFM-HSI images and data were compared to scanning transmission electron microscopy (STEM), a NIST-traceable technique for particle size analysis, and the current gold standard for offline analysis of filter media. As expected, STEM provided more accurate sizing and morphology data when compared to EDFM-HSI, but is not ideal for rapid screening of the presence of NPs of interest since it is a costly, low-throughput technique. In this study, we demonstrate the utility of EDFM-HSI in rapidly visualizing and identifying TiO2 and SiO2 NPs on MCE filters. This screening method may prove useful in expediting time-to-knowledge compared to electron microscopy. Future work will expand this evaluation to other industrially relevant NPs, other filter media types, and real-world filter samples from occupational exposure assessments.
Mixed‐methods exploration of students' motivation in using augmented reality in neuroanatomy education with prosected specimens

The use of augmented reality (AR) in teaching and studying neuroanatomy has been well researched. Previous research showed that AR-based learning of neuroanatomy has both alleviated cognitive load and was attractive to young learners. However, how the attractiveness of AR effects student motivation has not been discovered. Therefore, the motivational effects of AR were investigated in this research by the use of quantitative and qualitative methods. Motivation elicited by the GreyMapp-AR, an AR application, was investigated in medical and biomedical sciences students (n = 222; mean age: 19.7 ± 1.4 years) using the instructional measure of motivation survey (IMMS). Additional components (i.e., attention, relevance, confidence, and satisfaction) were also evaluated with motivation as measured by IMMS. Additionally, 19 students underwent audio-recorded individual interviews which were transcribed for qualitative analysis. Males regarded the relevance of AR significantly higher than females (P < 0.024). Appreciation of the GreyMapp-AR program was found to be significantly higher in students studying biomedical sciences as compared to students studying medicine (P < 0.011). Other components and scores did not show significant differences between student groups. Students expressed that AR was beneficial in increasing their motivation to study subcortical structures, and that AR could be helpful and motivating for preparing an anatomy examination. This study suggests that students are motivated to study neuroanatomy by the use of AR, although the components that make up their individual motivation can differ significantly between groups of students.
Competitive coevolution for defense and security

Competitive coevolution is an important technique for fields such as defense and security which inherently involve adversarial games. One advantage that the field of computer security has in particular is that games in this space are often naturally able to be simulated at a high fidelity by interacting with the involved software or hardware directly. However, such high-fidelity evaluations are typically slow, so it is especially important in these cases to get as much useful information out of as few evaluations as possible. This paper proposes a new competitive coevolutionary evaluation method of Similar-Strength Opponent Sampling, which selects opponent pairings of similar skill levels so that evaluations can more efficiently distinguish the performances of similar individuals. This is enabled through the use of Elo ratings as a surrogate fitness function that prevents bias against individuals assigned stronger opponents. Care is taken to ensure that this technique is applicable to complex games where there is no explicit winner or loser, allowing ratings to be based on relative fitness. Mixed results are presented, showing that significant benefits are gained from pairing similar-strength opponents, but finding that the use of Elo rating instead of raw fitness harms evolution for intransitive games.
Marginal multi-object Bayesian filter with multiple hypotheses

This paper proposes a marginal multi-object Bayesian filter with multiple hypotheses to track multiple objects in the presence of object appearing and object disappearing, missed detection and clutter. This filter delivers the probability of existence and probability density function of each object. A mathematical model for searching K-best hypotheses is set up by the maximization of the generalized joint likelihood ratios of hypotheses, which results in a 2-dimensional assignment problem. The K-best hypotheses can be acquired by using the Murty algorithm to solve the 2-dimensional assignment problem. According to the K-best hypotheses, the existence probabilities and probability density functions of objects are formed. Furthermore, an implementation of this filter for a linear Gaussian system is developed and is extended to nonlinear observations. Experimental result demonstrates that the proposed filter outperforms other available filters at various numbers of clutter and different detecting probabilities.
A Real-Time and High Performance Posture Estimation System Based on Millimeter-Wave Radar

mmWave radar is a technique popular in industrial settings for applications such as production line tracking. It has recently been used for human detection and tracking within home health care as a potential application area. In this paper, we present a novel posture estimation system. We focus on categorisation of postures (standing, sitting, lying) for a single person in indoor environments. We establish a new point cloud dataset and show that, with simple algorithms employed, the system has over 99% accuracy for the field of view obtained when a single radar is placed above the participant.
Beyond Low-Pass Filtering: Graph Convolutional Networks With Automatic Filtering

Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings. First, they are essentially low-pass filters, thus the potentially useful middle and high frequency band of graph signals are ignored. Second, the bandwidth of existing graph convolutional filters is fixed. Parameters of a graph convolutional filter only transform the graph inputs without changing the curvature of a graph convolutional filter function. In reality, we are uncertain about whether we should retain or cut off the frequency at a certain point unless we have expert domain knowledge. In this paper, we propose Automatic Graph Convolutional Networks (AutoGCN) to capture the full spectrum of graph signals and automatically update the bandwidth of graph convolutional filters. While it is based on graph spectral theory, our AutoGCN is also localized in space and has a spatial form. Experimental results show that AutoGCN achieves significant improvement over baseline methods which only work as low-pass filters.
An automatic and efficient technique for tumor location identification and classification through breast MR images

The basic objective of this paper is to develop a single structured algorithm to classify the breast tissues into normal or abnormal. For this study, the breast MR images dataset of 448 images collected from Healthmap diagnostics centre, PGIMS, Rohtak, India. The proposed algorithm consists of several steps i.e. an integrated (Median Wiener & Median) filtering technique is used for de-noising; breast boundary region extraction via selection of nipple and mid- sternum points to make the image rotation invariant; determined the tumor region intensity by using morphological operations & hole filling; classify the normal and abnormal breast tissues by SVM using 14 texture features extracted through GLCM & 13 morphological or kinetic features; evaluated the exact location as well as area of abnormal tissues. The proposed algorithm has been evaluated statistically as well as visually. The quality parameters achieved are accuracy, sensitivity and specificity with values 0.937, 0.956 and 0.872 respectively. The Jaccard Index coefficient achieved is 0.921, which indicates promising overlap between the predicted tumor and the manually done image by the radiologist so called ground truth image. This work may be taken as a second opinion by the radiologists. The evaluated results may give a basic foundation for optimization by selecting the features more precisely and also different evolutionary algorithms using multi-classifiers can be designed in future.