Reviewerly/exHarmony huggingface dataset:
Rule-based and Machine Learning Hybrid System for Patient Cohort Selection

Clinical trials play a critical role in medical studies. However, identifying and selecting cohorts for suchtrials can be a troublesome task since patients must match a set of complex pre-determined criteria. Patientselection requires a manual analysis of clinical narratives in patients’ records, which is a time-consuming taskfor medical researchers. In this work, natural language processing (NLP) techniques were used to performautomatic patient cohort selection. The approach herein presented was developed and tested on the 2018 n2c2Track 1 Shared-Task dataset where each patient record is annotated with 13 selection criteria. The resultinghybrid approach is based on heuristics and machine learning and attained a micro-average and macro-averageF1-score of 0.8844 and 0.7271, respectively, in the n2c2 test set. Part of the source code resultant from thiswork is available at https://github.com/ruiantunes/2018-n2c2-track-1/.
Aligning Vector-spaces with Noisy Supervised Lexicon

Noa Yehezkel Lubin, Jacob Goldberger, Yoav Goldberg. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Quantum cryptography with realistic devices

In principle, quantum key distribution (QKD) offers information-theoretic security based on the laws of physics. In practice, however, the imperfections of realistic devices might introduce deviations from the idealized models used in security analyses. Can quantum code-breakers successfully hack real systems by exploiting the side channels? Can quantum code-makers design innovative counter-measures to foil quantum code-breakers? This article reviews theoretical and experimental progress in the practical security aspects of quantum code-making and quantum code-breaking. After numerous attempts, researchers now thoroughly understand and are able to manage the practical imperfections. Recent advances, such as the measurement-device-independent protocol, have closed the critical side channels in the physical implementations, paving the way for secure QKD with realistic devices.
Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

A noticeable topic to be pursued in the field of on-board real-time data processing is the influence of the modulation transfer function (MTF) of the image acquisition system on the lossless compressibility of raw (that is, uncalibrated) hyperspectral data. Actually, notwithstanding the system device is constrained by several design and manufacturing requirements, the impact of the on-board MTF on the performance of data compressors is becoming remarkable. In particular, the aim of reducing both transmission bandwidth/power and mass storage can be efficiently pursued. Such an analysis is expected to be useful especially for systems employed in mini-satellites, whose payload must be compact and light. From this perspective, this paper investigates the performance of a typical imaging system that acquires low/medium-spatial-resolution images, by considering high-resolution reference data, which simulate the real scene to be imaged. To this end, standard Consultative Committee for Space Data Systems (CCSDS) Aviris 2006 data have been chosen, due to their spatial resolution of 17 m, which is adequate to be a reference for simulated data whose spatial resolution is foreseen between 50 and 150 m. MTF requirements are usually provided based on the cut-off value of the amplitude at the Nyquist frequency, which is defined as a half of the sampling frequency. Typically, a cut-off value between 0 . 2 and 0 . 3 ensures that a sufficient amount of information is delivered from the scene to the acquired image, by avoiding at the same time the degradation due to an excessive aliasing distortion. All the scores are achieved by running the standard lossless compression scheme CCSDS 1.2.3.0-B-1 for multispectral/hyperspectral data, as a function of the cut-off value and different noise acquisition levels. The final results, and related plots, show that this analysis can suggest a suitable choice for the cut-off value, to ensure both a sufficient quality and low bit rates for the transmitted data to the ground station.
Pomelo, a Collaborative Education Technology Interaction Robot

We have designed “Pomelo”, an interactive robot that teaches children basic algorithmic skills and enhances classroom collaboration through games. Pomelo looks like a friendly dog with a screen displaying its eyes and cooperates with the students through vision and speech. Pomelo becomes a part of the class instead of merely being a second teacher by interacting with children in the same way children interact with each other, like “Robovie” [1]. Ultimately, Pomelo will encourage kids to use technology as an interactive learning tool instead of a form of addictive entertainment while creating a more cooperative and social classroom environment.
HYPE: Human eYe Perceptual Evaluation of Generative Models

Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc, neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time. We introduce two variants: one that measures visual perception under adaptive time constraints to determine the threshold at which a model's outputs appear real (e.g. 250ms), and the other a less expensive variant that measures human error rate on fake and real images sans time constraints. We test HYPE across six state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using four datasets: CelebA, FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements across training epochs, and we confirm via bootstrap sampling that HYPE rankings are consistent and replicable.
Demosaicking Using a Spatial Reference Image for an Anti-Aliasing Multispectral Filter Array

Multispectral imaging with a multispectral filter array (MSFA) facilitates snapshot imaging; however, a demosaicking process is required to estimate a fully defined multispectral image based on undersampled sensor data. Undersampling induces aliasing and adverse artifacts in the reconstructed image. To solve this problem, Jia et al. proposed the Fourier spectral filter array (FSFA), which can reduce aliasing. In this paper, we analyze the FSFA and a more generalized anti-aliasing MSFA, and we identify the property that makes MSFAs anti-aliasing. Furthermore, we propose a novel demosaicking method that is a hybrid of frequency-decomposition-based and compressive-sensing-based demosaicking. Anti-aliasing MSFAs enable demosaicking to comprehend the precise spatial structures of an image. The image assists our proposed method in precisely reconstructing images using compressive sensing. Our experimental results demonstrated that the proposed method performs better than the existing demosaicking methods, especially in terms of spatial reconstruction.
Prune Your Neurons Blindly: Neural Network Compression through Structured Class-blind Pruning

High performance of deep learning models typically comes at cost of considerable model size and computation time. These factors limit applicability for deployment on memory and battery constrained devices such as mobile phones or embedded systems. In this work, we propose a novel pruning technique that eliminates entire filters and neurons according to their relative L1-norm as compared to the rest of the network, yielding more compression and decreased parameters' redundancy. The resulting network is non-sparse, however, much more compact and requires no special infrastructure for deployment. We prove the viability of our method by achieving 97.4%, 86.1%, 47.8% and 53% compression of LeNet-5, VGG-16, ResNet-56 and ResNet-110 respectively, exceeding state-of-the-art compression results reported on VGG-16 and ResNet without losing any performance compared to the baseline. Our approach does not only exhibit good performance but is also easy to implement.
Depth Separations in Neural Networks: What is Actually Being Separated?

Existing depth separation results for constant-depth networks essentially show that certain radial functions in $\mathbb{R}^d$, which can be easily approximated with depth $3$ networks, cannot be approximated by depth $2$ networks, even up to constant accuracy, unless their size is exponential in $d$. However, the functions used to demonstrate this are rapidly oscillating, with a Lipschitz parameter scaling polynomially with the dimension $d$ (or equivalently, by scaling the function, the hardness result applies to $\mathcal{O}(1)$-Lipschitz functions only when the target accuracy $\epsilon$ is at most $\text{poly}(1/d)$). In this paper, we study whether such depth separations might still hold in the natural setting of $\mathcal{O}(1)$-Lipschitz radial functions, when $\epsilon$ does not scale with $d$. Perhaps surprisingly, we show that the answer is negative: In contrast to the intuition suggested by previous work, it \emph{is} possible to approximate $\mathcal{O}(1)$-Lipschitz radial functions with depth $2$, size $\text{poly}(d)$ networks, for every constant $\epsilon$. We complement it by showing that approximating such functions is also possible with depth $2$, size $\text{poly}(1/\epsilon)$ networks, for every constant $d$. Finally, we show that it is not possible to have polynomial dependence in both $d,1/\epsilon$ simultaneously. Overall, our results indicate that in order to show depth separations for expressing $\mathcal{O}(1)$-Lipschitz functions with constant accuracy -- if at all possible -- one would need fundamentally different techniques than existing ones in the literature.
Do software engineering practitioners cite software testing research in their online articles?

Background: Software engineering (SE) research continues to study the degree to which practitioners perceive research as relevant to practice. Such studies typically comprise surveys of practitioner opinions. In a preliminary, and relatively small scale, study of online articles we previously found few explicit citations to software testing research. Our previous study provided an in situ complement to the typical survey study, however the findings of the previous study were limited by the size of our sample.
Optimization of the proposed hybrid denoising technique to overcome over-filtering issue

Abstract Image denoising has become a crucial task in medical ultrasound (US) imaging due to the presence of speckle or multiplicative noise and additive Gaussian noise. Recently, several denoising techniques such as adaptive wavelet thresholding &amp; joint bilateral (AWT + JB) filter, adaptive fuzzy switching weighted mean (AFSWM) filter and median patch-based locally optimal Wiener (MPBLOW) filter have been proposed to remove the speckle noise. However, these denoising techniques were found to remove noise along with the essential parts of the actual image data which is known as over-filtering. Thereby, it reduces the accuracy of the recognition process. In this paper, a new hybrid filter technique is proposed by combining anisotropic diffusion (AD) with Butterworth band pass filter to overcome over-filtering of the image. In addition, the performance of the proposed hybrid filter and its design parameters are enhanced using the particle swarm optimization (PSO) algorithm. The simulation results show that the proposed filtering technique achieves a better denoising performance when compared with other filtering techniques in terms of peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), structural similarity index (SSIM) and edge preservation index (EPI). Moreover, the results validated that the proposed filtering technique using PSO achieves effective performance than using the harmony search algorithm (HSA) and other filtering techniques.
The Transparency of Big Data, Data Harvesting and Digital Twins

Computer storage and cloud computing has become more powerful with multiple algorithms running complex data analysis looking at intelligence trends, user behaviour, profiling and ways to make use of these outputs. Added with the artificial intelligence (AI) interaction has meant a new and dynamic method to create models forging analysis to be more clinical, proficient and continually seeking more improvement with the self-learning and intelligent programming of machine learning (ML). In the healthcare sector there is deep interest in collecting, curating the data and making the best use of silo’d data through methods such as blockchain. This can then lead to a multitude of innovations such as precision based medicine, targeting individual variability in genes, their environment, etc. It also means that big data analytics in healthcare is evolving into providing these insights from very large data sets and improving outcomes while reducing costs and inefficiencies. However, there also are some ethical impacts in the process of Digital Twins which can lead to segmentation and discrimination. Or perhaps the data that is automatically collected from healthcare sensors in IoMT and what type of governance are they scrutinized to. It is clear that data is the most important asset of not just an organisation but also to the individual and why the General Data Protection Regulation (GDPR) has taken an important stance in data protection by design and default, that all organisations needs to follow. This chapter aims to highlight some of the concerns.
Comparing the Robustness of Stepwise Mixture Modeling With Continuous Nonnormal Distal Outcomes

The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means. This study has implications for researchers looking to apply recommended latent class analysis mixture modeling approaches in that nonnormality, which has been not fully considered in previous studies, was taken into account to address the distributional form of distal outcomes.
A Smart Cloud-Based Energy Data Mining Agent Using Big Data Analysis Technology

The aim of this paper was the development of a smart cloud-based energy data mining agent, OntoDMA, within the cloud environment Web service-based Information Agent System. Big Data analysis technology was used, embedded in a cloud-based active multi-agent system, to proactively provide fast and appropriate, real-time, domain information prediction. A case study was done to outline and explore the feasibility of the system architecture and the energy saving information system. A preliminary system presentation and experimental verification were prepared. The cache performance of the Solutions Pool was increased by 19.82% and the query workload of the Prediction Rules was reduced by 66.51%, a considerable decrease of the back-end servo system workload.
Few-Shot Learning with Localization in Realistic Settings

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast to both extremes, real world recognition problems exhibit heavy-tailed class distributions, with cluttered scenes and a mix of coarse and fine-grained class distinctions. We show that prior methods designed for few-shot learning do not work out of the box in these challenging conditions, based on a new benchmark. We introduce three parameter-free improvements: (a) better training procedures based on adapting cross-validation to meta-learning, (b) novel architectures that localize objects using limited bounding box annotations before classification, and (c) simple parameter-free expansions of the feature space based on bilinear pooling. Together, these improvements double the accuracy of state-of-the-art models on meta-iNat while generalizing to prior benchmarks, complex neural architectures, and settings with substantial domain shift.
A Study on Satisfaction Level Among Amateur Web Application Developers Towards Pigeon-Table as Nano Web Development Framework

Web applications are an important platform in today's society, which humans rely on to complete daily tasks. Most of the web applications were developed with sophisticated, well-known, and powerful web development frameworks. While these frameworks evolve and grow at exponential rate, it become very complex, challenging to learn and no longer intuitive for web application developers, especially those who are inexperience and amateur. There is a need for a simpler web development framework which is sufficient for contemporary web application development. Pigeon-table is a simple module developed under ngPigeon project with the aim to generate web content, i.e. table using data from MySQL database with a single html tag. Pigeon-table is intuitive and easy to learn as it was developed with the principle “web developer-centred design” in mind. A pilot test was conducted in this study to evaluate the satisfactory level among amateur web application developers towards pigeon-table as nano-framework.
Role of use of Social Media on Effective Buying Decision Process: A Study of Consumer buying Behavior in the Context of Bangladesh Market

Buyer behavior has always been an important and interesting area for research. This study focuses on measuring the influence of use of social media during different stages of buying decision process with effective buying decision process in the context of Bangladesh market. This study is based on empirical findings of a quantitative research in which primary data is collected by using survey method with the help of a self-completion questionnaire and by applying personal interview technique. The study reveals that out of five stages of consumer buying decision processes, uses of social media during problem recognition, purchase and post-purchase decision stages have small but positive and statistically significant associations with effective buying decision process. Overall, use of social media has weak but statistically significant association with effective buying decision process. Use of social media during problem recognition stage followed by use of social media during post-purchase decision made the strongest unique contribution to explain effective consumer buying decision process and are statistically significant. The study extends a direction for new researchers and concludes with suggestions for further research and suggests marketers to use social media to generate demand and to focus on offering superior value products to provide customer satisfaction so that buyers spread positive word-ofmouth which will in turn develop company/brand image and will bring more customers and will play significant role on the buying behavior of consumers.
Cross-species Data Classification by Domain Adaptation via Discriminative Heterogeneous Maximum Mean Discrepancy

Cross-species or Cross-platform data classification is a challenging problem in the field of bioinformatics, which aims to classify data samples in one species/platform by using labeled data samples in another species/platform. Traditional classification methods can not be used in this case, since the samples from two species/platforms may have different feature spaces, or follow different statistical distributions. Domain adaptation is a new strategy which could be used to deal with this problem. A big challenge in domain adaptation is how to reduce the difference and correct the drift between the source and the target domains in the heterogeneous case, when the feature spaces of the two domains are different. It has been shown theoretically that probability divergences between the two domains such as maximum mean discrepancy (MMD) play an important role in the generalization bound for domain adaptation. However, they are rarely used for heterogeneous domain adaptation due to the different feature spaces of the domains. In this work, we propose a heterogeneous domain adaptation approach by making use of MMD, which measures the probability divergence in an embedded low-dimensional common subspace. Our proposed discriminative heterogeneous MMD approach (DMMD) aims to find new representations of the samples in a common subspace by minimizing the domain probability divergence with preserving the known discriminative information. A conjugate gradient algorithm on a Grassmann manifold is applied to solve the nonlinear DMMD model. Our experiments on both simulation and benchmark machine learning datasets show that our approaches outperform other state-of-the-art approaches for heterogeneous domain adaptation. We finally apply our approach to a cross-platform dataset and a cross-species dataset, and the results show the effectiveness of our approach.
The relationship between the technology acceptance model and preference for ebooks at a large research university

Purpose This study aims to uncover factors related to students’ preference for ebooks with hopes that understanding what drives these preferences will help librarians to figure out how to increase students’ use of ebooks. Design/methodology/approach Based on the unified theory of acceptance and use of technology, researchers developed a model of ebook preference and a survey including constructs related to perceived usefulness and perceived ease of use. Respondents were undergraduate students from a core political science course at a large research university in the USA. Findings Ebook performance expectancy and ebook self-efficacy have indirect effects on ebook preference by way of ebook attitude. Ebook attitude and social influence both have direct effects. Research limitations/implications One of the limitation include respondents being of a similar age and having a similar experience of technology and ebooks. Prior use of ebooks may partially explain the results. Practical implications Librarians should help students develop ebook self-efficacy. Vendors should consider how interfaces may impact ebook self-efficacy. Ebook attitude may be positively influenced by tapping students’ desire to utilize technology. Originality/value This research adds to the understanding about ebook preference while expanding research in libraries by applying a theory and model from another research discipline.