Reviewerly/exHarmony huggingface dataset:
Analysis of Breast Cancer Detection Using Different Machine Learning Techniques

Data mining algorithms play an important role in the prediction of early-stage breast cancer. In this paper, we propose an approach that improves the accuracy and enhances the performance of three different classifiers: Decision Tree (J48), Naïve Bayes (NB), and Sequential Minimal Optimization (SMO). We also validate and compare the classifiers on two benchmark datasets: Wisconsin Breast Cancer (WBC) and Breast Cancer dataset. Data with imbalanced classes are a big problem in the classification phase since the probability of instances belonging to the majority class is significantly high, the algorithms are much more likely to classify new observations to the majority class. We address such problem in this work. We use the data level approach which consists of resampling the data in order to mitigate the effect caused by class imbalance. For evaluation, 10 fold cross-validation is performed. The efficiency of each classifier is assessed in terms of true positive, false positive, Roc curve, standard deviation (Std), and accuracy (AC). Experiments show that using a resample filter enhances the classifier’s performance where SMO outperforms others in the WBC dataset and J48 is superior to others in the Breast Cancer dataset.
Open Set Domain Adaptation for Image and Action Recognition

Since annotating and curating large datasets is very expensive, there is a need to transfer the knowledge from existing annotated datasets to unlabelled data. Data that is relevant for a specific application, however, usually differs from publicly available datasets since it is sampled from a different domain. While domain adaptation methods compensate for such a domain shift, they assume that all categories in the target domain are known and match the categories in the source domain. Since this assumption is violated under real-world conditions, we propose an approach for open set domain adaptation where the target domain contains instances of categories that are not present in the source domain. The proposed approach achieves state-of-the-art results on various datasets for image classification and action recognition. Since the approach can be used for open set and closed set domain adaptation, as well as unsupervised and semi-supervised domain adaptation, it is a versatile tool for many applications.
Measuring the similarity of graphs with a Gaussian boson sampler

Gaussian boson samplers (GBSs) have initially been proposed as a near-term demonstration of classically intractable quantum computation. We show here that they have a potential practical application: Samples from these devices can be used to construct a feature vector that embeds a graph in Euclidean space, where similarity measures between graphs---so-called graph kernels---can be naturally defined. This is crucial for machine learning with graph-structured data, and we show that the GBS-induced kernel performs remarkably well in classification benchmark tasks. We provide a theoretical motivation for this success, linking the extracted features to the number of $r$ matchings in subgraphs. Our results contribute to a new way of thinking about kernels as a quantum hardware-efficient feature mapping, and lead to a promising application for near-term quantum computing.
Multi-Label Image Classification by Feature Attention Network

Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improve multi-label classification performance. However, they have ignored two more realistic problems: object scale inconsistent and label tail (category imbalance). These two problems will impact the bad influence on the classification model. To tackle these two problems and learn the label correlations, we propose feature attention network (FAN) which contains feature refinement network and correlation learning network. FAN builds top-down feature fusion mechanism to refine more important features and learn the correlations among convolutional features from FAN to indirect learn the label dependencies. Following our proposed solution, we achieve performed classification accuracy on MSCOCO 2014 and VOC 2007 dataset.
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation

The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal. In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress. We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components. Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set). Code is available at this https URL .
Universal Adversarial Perturbations for Speech Recognition Systems

Author(s): Neekhara, Paarth; Hussain, Shehzeen; Pandey, Prakhar; Dubnov, Shlomo; McAuley, Julian; Koushanfar, Farinaz | Abstract: In this work, we demonstrate the existence of universal adversarial audio perturbations that cause mis-transcription of audio signals by automatic speech recognition (ASR) systems. We propose an algorithm to find a single quasi-imperceptible perturbation, which when added to any arbitrary speech signal, will most likely fool the victim speech recognition model. Our experiments demonstrate the application of our proposed technique by crafting audio-agnostic universal perturbations for the state-of-the-art ASR system -- Mozilla DeepSpeech. Additionally, we show that such perturbations generalize to a significant extent across models that are not available during training, by performing a transferability test on a WaveNet based ASR system.
Traveling the token world: A graph analysis of Ethereum ERC20 token ecosystem

The birth of Bitcoin ushered in the era of cryptocurrency, which has now become a financial market attracted extensive attention worldwide. The phenomenon of startups launching Initial Coin Offerings (ICOs) to raise capital led to thousands of tokens being distributed on blockchains. Many studies have analyzed this phenomenon from an economic perspective. However, little is know about the characteristics of participants in the ecosystem. To fill this gap and considering over 80% of ICOs launched based on ERC20 token on Ethereum, in this paper, we conduct a systematic investigation on the whole Ethereum ERC20 token ecosystem to characterize the token creator, holder, and transfer activity. By downloading the whole blockchain and parsing the transaction records and event logs, we construct three graphs, namely token creator graph, token holder graph, and token transfer graph. We obtain many observations and findings by analyzing these graphs. Besides, we propose an algorithm to discover potential relationships between tokens and other accounts. The reported case shows that our algorithm can effectively reveal entities and the complex relationship between various accounts in the token ecosystem.
The blockchain as a sustainable business model innovation

Purpose The purpose of this paper is to strive to close the current research gap pertaining to potential implications of the blockchain (BC) for sustainable business models (SBMs) in the agri-food industry. Design/methodology/approach To answer the research question, the authors utilised the Value Triangle framework by Biloslavo et al. (2018) in order to explore the potential innovation of BC for SBMs in the agri-food industry. Then, the authors apply it to an in-depth exploratory case study of the Placido Volpone winery. The authors draw data from strategic plans, annual reports, corporate website and a semi-structured interview with the winery's founder. Findings The authors show how BC technology could be a source of SBM innovation in the agri-food industry. Research limitations/implications BC technology has the potential to significantly change SBMs. Given the huge set-up investments by the industry, academic research investigating potential implications and supporting companies in their application of BC is needed. This paper explores how the implications of BC as source of innovation on SBMs can be investigated. Practical implications The research results of this study can be used by company leaders and managers to support the development of SBMs through the introduction of BC technology in their business activities. Originality/value The paper is novel because it investigates the relationship between SBM innovation and BC providing theoretical justification to SBM technological innovation in an agri-food setting. Additionally, the paper provides an empirical application of the framework by Biloslavo et al. (2018) for understanding the development of SBM through BC in the agri-food industry.
Differentially Private SQL with Bounded User Contribution

Abstract Differential privacy (DP) provides formal guarantees that the output of a database query does not reveal too much information about any individual present in the database. While many differentially private algorithms have been proposed in the scientific literature, there are only a few end-to-end implementations of differentially private query engines. Crucially, existing systems assume that each individual is associated with at most one database record, which is unrealistic in practice. We propose a generic and scalable method to perform differentially private aggregations on databases, even when individuals can each be associated with arbitrarily many rows. We express this method as an operator in relational algebra, and implement it in an SQL engine. To validate this system, we test the utility of typical queries on industry benchmarks, and verify its correctness with a stochastic test framework we developed. We highlight the promises and pitfalls learned when deploying such a system in practice, and we publish its core components as open-source software.
SpEx+: A Complete Time Domain Speaker Extraction Network

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively.
Multi-User Redirected Walking and Resetting Using Artificial Potential Fields

Head-mounted displays (HMDs) and large area position tracking systems can enable users to navigate virtual worlds through natural walking. Redirected walking (RDW) imperceptibly steers immersed users away from physical world obstacles allowing them to explore unbounded virtual worlds while walking in limited physical space. In cases of imminent collisions, resetting techniques can reorient them into open space. This work introduces categorically new RDW and resetting algorithms based on the use of artificial potential fields that “push” users away from obstacles and other users. Data from human subject experiments indicate that these methods reduce potential single-user resets by 66% and increase the average distance between resets by 86% compared to previous techniques. A live multi-user study demonstrates the viability of the algorithm with up to 3 concurrent users, and simulation results indicate that the algorithm scales efficiently up to at least 8 users and is effective with larger groups.
Quantum algorithm for solving linear differential equations: Theory and experiment

Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been proposed to be capable of speeding up the calculation. However, they are yet to be realized in experiment as it cannot be easily converted into an implementable quantum circuit. Here, we present and experimentally realize an implementable gate-based quantum algorithm for efficiently solving the LDE problem: given an $N\ifmmode\times\else\texttimes\fi{}N$ matrix $\mathcal{M}$, an $N$-dimensional vector $\mathbf{b}$, and an initial vector $\mathbf{x}(0)$, we obtain a target vector $\mathbf{x}(t)$ as a function of time $t$ according to the constraint $d\mathbf{x}(t)/dt=\mathcal{M}\mathbf{x}(t)+\mathbf{b}$. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances, and a gate-based quantum circuit is produced which is friendly to the experimentalists and implementable in current quantum techniques. In addition, we experimentally solve a $4\ifmmode\times\else\texttimes\fi{}4$ linear differential equation using our quantum algorithm in a four-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.
Image encryption based on the pseudo-orbits from 1D chaotic map

Chaotic systems have been extensively applied in image encryption as a source of randomness. However, dynamical degradation has been pointed out as an important limitation of this procedure. To overcome this limitation, this paper presents a novel image encryption scheme based on the pseudo-orbits of 1D chaotic maps. We use the difference of two pseudo-orbits to generate a random sequence. The generated sequence has been successful in all NIST tests, which implies it has adequate randomness to be employed in encryption process. Confusion and diffusion requirements are also effectively implemented. The usual low key space of 1D maps has been improved by a novelty procedure based on multiple perturbations in the transient time. A factor using the plain image is one of the perturbation conditions, which ensures a new and distinct secret key for each image to be encrypted. The proposed encryption scheme has been efficaciously verified using the Lena, Baboon, and Barbara test images.
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization

Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder where the mean of the representations of the input reviews decodes to a reasonable summary-review while not relying on any review-specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline.
Approach to M-learning Acceptance Among University Students

A growing number of higher education institutions have adopted tools to promote mobile learning. However, studies into the driving factors of its adoption are insufficient. This article identifies the aspects that have an effect on the adoption of mobile learning (m-learning) among university students. The theory of planned behavior (TPB) and technology acceptance model (TAM) have been shown to be valid and powerful models in the research on the adoption of learning technologies. Based on TPB and TAM, we propose a model to explain how perceptions influence m-learning adoption among Colombian university students. To confirm the acceptability of the model, a self-administered questionnaire was applied to 878 undergraduate university students from the Instituto Tecnológico Metropolitano (ITM), a higher education institution in Colombia The results suggest that all of the constructs of TPB and TAM have a moderate impact on the intention to adopt m-learning. Specifically, perceived usefulness and attitude have a significant influence on students’ acceptance of m-learning. These results can stimulate future research and promote an effective diffusion of m-learning in developing countries.
Ensemble Models for Spoofing Detection in Automatic Speaker Verification

Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modelling approach. For robustness, we use both deep neural networks and t ...
Pseudo-Random Number Generator Based on Logistic Chaotic System

In recent years, a chaotic system is considered as an important pseudo-random source to pseudo-random number generators (PRNGs). This paper proposes a PRNG based on a modified logistic chaotic system. This chaotic system with fixed system parameters is convergent and its chaotic behavior is analyzed and proved. In order to improve the complexity and randomness of modified PRNGs, the chaotic system parameter denoted by floating point numbers generated by the chaotic system is confused and rearranged to increase its key space and reduce the possibility of an exhaustive attack. It is hard to speculate on the pseudo-random number by chaotic behavior because there is no statistical characteristics and infer the pseudo-random number generated by chaotic behavior. The system parameters of the next chaotic system are related to the chaotic values generated by the previous ones, which makes the PRNG generate enough results. By confusing and rearranging the output sequence, the system parameters of the previous time cannot be gotten from the next time which ensures the security. The analysis shows that the pseudo-random sequence generated by this method has perfect randomness, cryptographic properties and can pass the statistical tests.
A study of the impact of data sharing on article citations using journal policies as a natural experiment

This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.
Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&amp;E images

Abstract Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&amp;E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of “tissue fingerprints,” which leverages large, unannotated datasets in a label-free manner to learn H&amp;E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call “fingerprints,” to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&amp;E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.