Reviewerly/exHarmony huggingface dataset:
IDHashGAN: Deep Hashing With Generative Adversarial Nets for Incomplete Data Retrieval

Benefiting from low storage costs and high retrieval efficiency, hash learning has been a widely adopted technology for approximating nearest neighbor in large-scale data retrieval. Deep learning to hash greatly improves image retrieval performance by integrating feature learning and hash coding into an end-to-end framework. However, subject to application scope, most existing deep hashing methods only apply to retrieval of complete data and have undesirable results when retrieving incomplete but valuable data. In this paper we propose IDHashGAN, a novel deep hashing model with generative adversarial networks to retrieve incomplete data, in which feature restoration, feature learning and hash coding are integrated into an unified end-to-end framework. The proposed model consists of four key components: (1) reconstructive and generative loss are used to generate continuous feature of incomplete data in generative network; (2) supervised manifold similarity is proposed to improve retrieval accuracy and obtain good user acceptance; (3) adversarial and classified loss are designed to distinguish authenticity and similarity in discriminative network; and (4) encoding and quantization loss are adopted to preserve similarity and control hash quality. Extensive experiments on benchmark datasets show that IDHashGAN is competitive on complete dataset and yields substantial boosts of 70% on incomplete datasets compared to state-of-the-art hashing methods.
A Narrative Review of Design-Based Research in Engineering Education: Opportunities and Challenges

<strong>Background:</strong> Active engagement across a range of methodological frameworks is one hallmark of thriving scholarly disciplines. Design-based research is one newer approach to education research that holds promise for developing effective interventions that are iteratively theorized, designed, and tested within local engineering education contexts. <strong>Purpose:</strong> To promote engagement with diverse research frameworks, the purpose of this narrative literature review was to identify, describe, and critically examine emerging use of design-based research in engineering education. We addressed research questions focused on characterizing the use of design-based research in engineering education in terms of the a) problems studied, b) interventions designed, c) participant populations and learning contexts, d) research methods employed, e) form(s) of the research findings, and f) limitations of the literature. Furthermore, this work identified current opportunities and challenges of design-based research for the field of engineering education through analysis of review findings in light of the authors’ experiences conducting design-based research in engineering education. <strong>Scope/Method:</strong> Using established review procedures that included specified database search terms and inclusion criteria, we identified 24 empirical design-based research studies in engineering education. We used qualitative content analysis to code study characteristics including nationality, participant population, research methods, and learning context. We then synthesized and critiqued findings across studies. <strong>Conclusions:</strong> In synthesizing key aspects of empirical design-based research studies in engineering education, this review provides insights into the ways design-based research is being implemented to advance engineering education imperatives and provides a foundation for expanding and strengthening use of design-based research in future work in engineering education. Opportunities of design-based research for engineering education include developing local improvements to the field’s most persistent and vexing issues (i.e., “wicked” problems) and realizing the full potential of technology for 21st century engineering education. Challenges include developing interdisciplinary teams, the need for expertise across multiple research approaches and methods, funding emergent DBR projects, and disseminating DBR results across the project lifespan.
Predicting Molecular Subtypes of Breast Cancer with Mammography and Ultrasound Findings: Introduction of Sono-Mammometry Score

We studied the correlation of sonographic and digital mammographic features with molecular classification of breast cancer. Imaging features from 313 patients with preliminary ultrasound and digital mammogram between November 2017 and May 2020 were compared with histopathology and immunohistochemical analysis for the prediction of molecular classification of breast cancer. We also devised a score called “sono-mammometry” score consisting of few simple imaging features which can easily be performed in outpatient settings. We studied that non-triple-negative breast cancers are predominantly hypoechoic and strongly correlate with the presence of irregular spiculated margins along with peripheral echogenic halo, posterior shadowing, and microcalcifications, while there is considerable variation in imaging features of TNBC as some of its imaging features overlap with those of typical benign tumors. Although imaging characteristics are helpful in the prediction of molecular classification, the prognostication value of these imaging features is still weak. There is considerable variation in imaging features which warrants vigilance towards improved diagnostic performance. To help better understand these features, our sono-mammometry score can serve as straightforward test which is assumed to be functional and productive in resource-limited settings.
Sentiment Analysis for Zoning System Admission Policy Using Support Vector Machine and Naive Bayes Methods

Abstract Indonesia have low quality of education. According Trend International Mathematics and Science Study, Indonesian student’s mathematical literacy is ranked 36 from 49 countries. For Science literacy, Indonesia is ranked 35 from 49 countries. To increase quality of education in Indonesia, Indonesia’s government make a new policy for new student admission called zoning system. Zoning system is a new student admission according distance from house to school. Zoning system is new policy in Indonesia and there are still many pros and cons of the zoning system. Sentiment analysis is used to know whether Indonesia’s people agree or disagree about zoning system policy. In this study, we use supervised statistical learning methods that are Support Vector Machine (SVM) and Naïve Bayes for sentiment analysis of zoning system admission policy. The results show that Indonesia’s people tend to disagree with the zoning system admission policy because negative opinion is greater than positive opinion. Furthermore, accuracy rates of SVM and Naïve Bayes are 92.93% and 79.86% respectively. So, SVM is better than Naïve Bayes for sentiment analysis of zoning system admission policy.
Distributed Ledger Technologies for Peer-to-Peer Energy Trading

The increasing integration of prosumers and smart metering devices into the energy distribution network, is transforming the traditional energy market to a community energy trading that requires peer-to-peer (P2P) interactions. Such P2P interactions result in complex data exchanges among prosumers, utility grid, and market operators. This inevitably introduces control complexity, security, and privacy challenges in the existing power system. The application of distributed ledger technology (DLT) has seen an increase in solving security challenges in the power network, specifically, in P2P energy exchanges. Thus, this study explores different DLT structures including blockchain and IOTA usage in energy P2P trading. A smart contract for managing trust and transactions is designed and implemented on IBM hyperledger fabric platform. In addition, we evaluated the performance of interconnected internet of things devices for energy transactions with IOTA protocol, which uses the directed acyclic graph as its DLT structure, against the Ethereum-based blockchain structure. It is shown that the end-to-end transaction delay with the IOTA DLT is lower than the Ethereum-based DLT implementation.
Real-time video super resolution network using recurrent multi-branch dilated convolutions

Recent developments of video super-resolution reconstruction often exploit spatial and temporal contexts from input frame sequence by making use of explicit motion estimation, e.g., optical flow, which may introduce accumulated errors and requires huge computations to obtain an accurate estimation. In this paper, we propose a novel multi-branch dilated convolution module for real-time frame alignment without explicit motion estimation, which is incorporated with the depthwise separable up-sampling module to formulate a sophisticated real-time video super-resolution network. Specifically, the proposed video super-resolution framework can efficiently acquire a larger receptive field and learn spatial–temporal features of multiple scales with minimal computational operations and memory requirements. Extensive experiments show that the proposed super-resolution network outperforms current state-of-the-art real-time video super-resolution networks, e.g., VESPCN and 3DVSRnet, in terms of PSNR values (0.49 dB and 0.17 dB) on average in various datasets, but requires less multiplication operations.
Provably Robust Node Classification via Low-Pass Message Passing

Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance on node classification. However, recent works have shown that GCNs are vulnerable to adversarial attacks, such as additions or deletions of adversarially-chosen edges in the graph, in order to mislead the node classification algorithms. How can we design robust GCNs that are resistant to such adversarial attacks? More challengingly, how can we do this in a way that is provably robust? We propose a robust node classification approach based on a low-pass `message passing' mechanism, that (a) reduces the effectiveness of adversarial attacks in experiments, and (b) provides theoretical guarantees against adversarial attacks. Our approach can be embedded into the existing GCN architectures to enhance their robustness. Empirical results show that our loss-pass method effectively improves the performance of multiple GCNs under miscellaneous perturbations and helps them to achieve superior performance on various graphs.
SoK: Algorithmic Incentive Manipulation Attacks on Permissionless PoW Cryptocurrencies

A long standing question in the context of cryptocurrencies based on Nakamoto consensus is whether such constructions are incentive compatible, i.e., the intended properties of the system emerge from the appropriate utility model for participants. Bribing and other related attacks, such as front-running or Goldfinger attacks, aim to directly influence the incentives of actors within (or outside) of the targeted cryptocurrency system. The theoretical possibility of bribing attacks on cryptocurrencies was discussed early on in the cryptocurrency community and various different techniques and approaches have since been proposed. Some of these attacks are designed to gain in-band profits, while others intend to break the mechanism design and render the cryptocurrency worthless. In this paper, we systematically expose the large but scattered body of research in this area which has accumulated over the years. We summarize these bribing attacks and similar techniques that leverage on programmatic execution and verification under the term algorithmic incentive manipulation (AIM) attacks, and show that the problem space is not yet fully explored. Based on our analysis we present several research gaps and opportunities that warrant further investigation. In particular, we highlight no- and near-fork attacks as a powerful, yet largely underestimated, AIM category that raises serious security concerns not only for smart contract platforms.
Characterization and selection of Japanese electronic health record databases used as data sources for non-interventional observational studies

Interventional studies are the fundamental method for obtaining answers to clinical questions. However, these studies are sometimes difficult to conduct because of insufficient financial or human resources or the rarity of the disease in question. One means of addressing these issues is to conduct a non-interventional observational study using electronic health record (EHR) databases as the data source, although how best to evaluate the suitability of an EHR database when planning a study remains to be clarified. The aim of the present study is to identify and characterize the data sources that have been used for conducting non-interventional observational studies in Japan and propose a flow diagram to help researchers determine the most appropriate EHR database for their study goals.We compiled a list of published articles reporting observational studies conducted in Japan by searching PubMed for relevant articles published in the last 3 years and by searching database providers' publication lists related to studies using their databases. For each article, we reviewed the abstract and/or full text to obtain information about data source, target disease or therapeutic area, number of patients, and study design (prospective or retrospective). We then characterized the identified EHR databases.In Japan, non-interventional observational studies have been mostly conducted using data stored locally at individual medical institutions (663/1511) or collected from several collaborating medical institutions (315/1511). Whereas the studies conducted with large-scale integrated databases (330/1511) were mostly retrospective (73.6%), 27.5% of the single-center studies, 47.6% of the multi-center studies, and 73.7% of the post-marketing surveillance studies, identified in the present study, were conducted prospectively. We used our findings to develop an assessment flow diagram to assist researchers in evaluating and choosing the most suitable EHR database for their study goals.Our analysis revealed that the non-interventional observational studies were conducted using data stored local at individual medical institutions or collected from collaborating medical institutions in Japan. Disease registries, disease databases, and large-scale databases would enable researchers to conduct studies with large sample sizes to provide robust data from which strong inferences could be drawn.
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models

Let V be any vector space of multivariate degree- d homogeneous polynomials with co-dimension at most k, and S be the set of points where all polynomials in V nearly vanish. We establish a qualitatively optimal upper bound on the size of ε-covers for S, in the l2-norm. Roughly speaking, we show that there exists an ε-cover for S of cardinality M=(k/ε) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Od(k1/d</sup> ). Our result is constructive yielding an algorithm to compute such an ε-cover that runs in time poly(M). Building on our structural result, we obtain significantly improved learning algorithms for several fundamental high-dimensional probabilistic models with hidden variables. These include density and parameter estimation for k-mixtures of spherical Gaussians (with known common covariance), PAC learning one-hidden-layer ReLU networks with k hidden units (under the Gaussian distribution), density and parameter estimation for k-mixtures of linear regressions (with Gaussian covariates), and parameter estimation for k-mixtures of hyperplanes. Our algorithms run in time quasi-polynomial in the parameter k. Previous algorithms for these problems had running times exponential in k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω(1)</sup> . At a high-level our algorithms for all these learning problems work as follows: By computing the low-degree moments of the hidden parameters, we are able to find a vector space of polynomials that nearly vanish on the unknown parameters. Our structural result allows us to compute a quasi-polynomial sized cover for the set of hidden parameters, which we exploit in our learning algorithms.
On the performance of objective quality metrics for lightfields

Lightfield (LF) technology has attained significant attention in recent years due to its capability to capture much richer textural and geometric information in the scene compared to the classical 2D representation. The resampling and compression operations on LFs often lead to visual quality degradation, thus, sophisticated visual quality assessment methods play a crucial role to ensure a pleasant viewing experience. To this end, it is necessary to examine the performance of quality assessment methods for LF contents. The paper provides a comprehensive study on the reliability of various objective algorithms for LF quality prediction. Three subjectively-annotated LF data sets were selected and an extensive quality estimation analysis has been conducted using several objective quality assessment methods. In total, 250 LFs (more than 48000 perspective images) were evaluated. The results were compared against human opinion scores using various correlation indices and their statistical significance. Next, a decision-making strategy was adopted to choose the most reliable quality metrics for evaluation of LFs and finally, a metric fusion framework was proposed to further improve the quality prediction accuracy. To best of our knowledge, the benchmark and the analytical methodologies used in this paper are the most comprehensive study on the objective quality assessment methods for LF application.
Visually Analyzing Contextualized Embeddings

In this paper we introduce a method for visually analyzing contextualized embeddings produced by deep neural network-based language models. Our approach is inspired by linguistic probes for natural language processing, where tasks are designed to probe language models for linguistic structure, such as parts-of-speech and named entities. These approaches are largely confirmatory, however, only enabling a user to test for information known a priori. In this work, we eschew supervised probing tasks, and advocate for unsupervised probes, coupled with visual exploration techniques, to assess what is learned by language models. Specifically, we cluster contextualized embeddings produced from a large text corpus, and introduce a visualization design based on this clustering and textual structure - cluster co-occurrences, cluster spans, and cluster-word membership- to help elicit the functionality of, and relationship between, individual clusters. User feedback highlights the benefits of our design in discovering different types of linguistic structures.
Inhomogeneous image segmentation based on local constant and global smoothness priors

In this article, we propose a new variational model for segmenting images with intensity inhomogeneity. The proposed model applies simultaneously the local constant and global smoothness priors to describe the bias part such that our model can obtain more precise segmentation results. This is different from the existing models in which either of such two priors is considered. Also, our method is developed to segment the multiphase inhomogeneous image with noise. Theoretically, we show the existence of minimizers of the proposed variational model. Moreover, by using the alternating minimization method, we design an effective algorithm to numerically solve the solution of the model. Numerical experimental results are provided to verify the better performance of our method than other test methods.
Improved Speaker and Navigator for Vision-and-Language Navigation

Prior works in vision-and-language navigation (VLN) focus on using long short-term memory (LSTM) to carry the flow of information on either the navigation model (navigator) or the instruction generating model (speaker).The outstanding capability of LSTM to process intermodal interactions has been widely verified; however, LSTM neglects the intramodel interactions, leading to negative effect on either navigator or speaker. The performance of attention-based Transformer is satisfactory in sequence-to-sequence translation domains, but Transformer structure implemented directly in VLN has yet been satisfied. In this article, we propose novel Transformer-based multimodal frameworks for the navigator and speaker, respectively. In our frameworks, the multihead self-attention with the residual connection is used to carry the information flow. Specially, we set a switch to prevent them from directly entering the information flow in our navigator framework. In experiments, we verify the effectiveness of our proposed approach, and show significant performance advantages over the baselines.
Private Blockchain Networks: A Solution for Data Privacy

The widespread adoption of blockchain technology has had a big impact on how people transact in the digital world. Individuals can transact in an anonymous but transparent manner. Their identities remain hidden but the records of their transactions are publicly available. This has had its benefits in certain application areas but might not be suited for transactions where it is important to know who you are dealing with and in circumstances where the data in the blockchain might be confidential. Private blockchain networks are better suited for such transactions as only authorized users can transact on the network. Sensitive data can also be stored on the blockchain as it is possible to restrict the users that can see the details of the transactions. This paper describes how to create a private blockchain network and how other users can join the network. It also details the benefits of using a private blockchain network with regards to data privacy as opposed to a public network.
Robust weighted principal components analysis demodulation algorithm for phase-shifting interferometry

We present an asynchronous phase-shifting demodulation approach based on the principal component analysis demodulation method that is robust to typical problems as turbulence, vibrations, and temporal instabilities of the optical setup. The method brings together a two-step and a phase-shifting asynchronous demodulation method to share their benefits while reducing their intrinsic limitations. Thus, the proposed approach is based on a two-fold process. First, the modulating phase is estimated from a two-step demodulation approach. Second, this information is used to compute weights to each phase-shifted pattern of the interferogram sequence, which are used in a novel weighted principal component demodulation approach. The proposed technique has been tested with simulated and real interferograms affected by turbulence and vibrations providing very satisfactory results in challenging cases.
Validating Sentiment Analysis on Opinion Mining Using Self-reported Attitude Scores

This paper aims to validate the use of sentiment analysis of text on opinion mining, using IBM Watson's Natural Language Understanding. An online survey examining attitude towards genomic editing using a validated scale and a text entry to comment on their thoughts of this technology was given to four populations-scientists, policymakers, farmers and the general public. The results indicate that sentiment scores of texts are significantly related to attitude scores in three of the four populations. This study shows that sentiment analysis is a reliable tool to understand opinions.