Reviewerly/exHarmony huggingface dataset:
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification

Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries.Performance of models in prior art is evaluated with standard precision, recall, and F 1 measures without regard for the rich hierarchical structure.In this work we argue for hierarchical evaluation of the predictions of neural LMTC models.With the example of the ICD-9 ontology we describe a structural issue in the representation of the structured label space in prior art, and propose an alternative representation based on the depth of the ontology.We propose a set of metrics for hierarchical evaluation using the depthbased representation.We compare the evaluation scores from the proposed metrics with previously used metrics on prior art LMTC models for ICD-9 coding in MIMIC-III.We also propose further avenues of research involving the proposed ontological representation.
Learning Spherical Convolution for 360 Recognition

While 360 cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make visual recognition non-trivial. Ideally, 360 imagery could inherit the convolutional neural networks (CNNs) trained with great success on perspective projection images. However, existing methods to transfer CNNs from perspective to spherical images introduce significant computational costs and/or degradations in accuracy. We propose to learn a Spherical Convolution Network (SphConv) that translates a planar CNN to the equirectangular projection of 360 images. Given a source CNN for perspective images, SphConv learns to reproduce the flat filter outputs on 360 data. The key benefits are 1) efficient and accurate recognition for 360 images, and 2) the ability to leverage pre-trained networks for perspective images. We propose two instantiations of SphConv---Spherical Kernel, which learns location dependent kernels on the sphere, and Kernel Transformer Network, which learns a functional transformation that generates SphConv from the source CNN. Validating our approach with multiple source CNNs and datasets, we show that it successfully preserves the source CNN's accuracy, while offering efficiency, transferability, and scalability to typical image resolutions. We further introduce a spherical Faster R-CNN based on SphConv and show that we can learn a spherical object detector without any object annotations in 360 images.
Visual cluster separation using high-dimensional sharpened dimensionality reduction

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our “High-Dimensional Sharpened DR” (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog.
Magic Dust for Cross-Lingual Adaptation of Monolingual Wav2vec-2.0

We propose a simple and effective cross-lingual transfer learning method to adapt monolingual wav2vec-2.0 models for Automatic Speech Recognition (ASR) in resource-scarce languages. We show that a monolingual wav2vec-2.0 is a good few-shot ASR learner in several languages. We improve its performance further via several iterations of Dropout Uncertainty-Driven Self-Training (DUST) by using a moderate-sized unlabeled speech dataset in the target language. A key finding of this work is that the adapted monolingual wav2vec-2.0 achieves similar performance as the topline multilingual XLSR model, which is trained on fifty-three languages, on the target language ASR task.
Line-Point Zero Knowledge and Its Applications

We introduce and study a simple kind of proof system called line-point zero knowledge (LPZK). In an LPZK proof, the prover encodes the witness as an affine line 𝐯(t) : = at + 𝐛 in a vector space 𝔽ⁿ, and the verifier queries the line at a single random point t = α. LPZK is motivated by recent practical protocols for vector oblivious linear evaluation (VOLE), which can be used to compile LPZK proof systems into lightweight designated-verifier NIZK protocols.We construct LPZK systems for proving satisfiability of arithmetic circuits with attractive efficiency features. These give rise to designated-verifier NIZK protocols that require only 2-5 times the computation of evaluating the circuit in the clear (following an input-independent preprocessing phase), and where the prover communicates roughly 2 field elements per multiplication gate, or roughly 1 element in the random oracle model with a modestly higher computation cost. On the theoretical side, our LPZK systems give rise to the first linear interactive proofs (Bitansky et al., TCC 2013) that are zero knowledge against a malicious verifier. We then apply LPZK towards simplifying and improving recent constructions of reusable non-interactive secure computation (NISC) from VOLE (Chase et al., Crypto 2019). As an application, we give concretely efficient and reusable NISC protocols over VOLE for bounded inner product, where the sender’s input vector should have a bounded L₂-norm.
Optimization of intensity-modulation/direct-detection optical key distribution under passive eavesdropping

We analyze the theoretically optimal operation of an optical key distribution (OKD) link based on fine intensity modulation of an optical signal transmitted over an attenuating channel to a direct detection receiver. With suitable digital postprocessing, the users may generate a secret key that will be unknown to an unauthorized party collecting passively a fraction of the signal that escapes detection by the legitimate recipient. The security is ensured by the presence of the shot noise that inevitably accompanies an eavesdropper’s attempt to detect the collected signal. It is shown that the key amount depends on a ratio that compares the legitimate recipient’s and eavesdropper’s capabilities to detect the signal, including noise contributed by their respective detectors. A simple proportionality relation is derived in the strong eavesdropping regime, and closed expressions for the optimal depth of binary intensity modulation as well as the discrimination thresholds for hard-decoded direct detection are given. The presented results substantially simplify the design of practical OKD systems operating under changing external conditions, e.g. variable atmospheric absorption in the case of free-space optical links.
Is the Leader Robot an Adequate Sensor for Posture Estimation and Ergonomic Assessment of A Human Teleoperator?

Ergonomic assessment of human posture plays a vital role in understanding work-related safety and health. Current posture estimation approaches face occlusion challenges in teleoperation and physical human-robot interaction. We investigate if the leader robot is an adequate sensor for posture estimation in teleoperation and we introduce a new probabilistic approach that relies solely on the trajectory of the leader robot for generating observations. We model the human using a redundant, partially-observable dynamical system and we infer the posture using a standard particle filter. We compare our approach with postures from a commercial motion capture system and also two least-squares optimization approaches for human inverse kinematics. The results reveal that the proposed approach successfully estimates human postures and ergonomic risk scores comparable to those estimates from gold-standard motion capture.
A blueprint for effective pandemic mitigation

Traditional methods for mitigating pandemics employ a dual strategy of contact tracing plus testing combined with quarantining and isolation. The contact tracing aspect is usually done via manual (human) contact tracers, which are labor-intensive and expensive. In many large-scale pandemics (e.g., COVID-19), testing capacity is resource limited, and current myopic testing strategies are resource wasteful. To address these challenges, in this work, we provide a blueprint on how to contain the spread of a pandemic by leveraging wireless technologies and advances in sequential learning for efficiently using testing resources in order to mitigate the spread of a large-scale pandemic.&#x0D; We study how different wireless technologies could be leveraged to improve contact tracing and reduce the probabilities of detection and false alarms. The idea is to integrate different streams of data in order to create a susceptibility graph whose nodes correspond to an individual and whose links correspond to spreading probabilities. We then show how to develop efficient sequential learning based algorithms in order to minimize the spread of the virus infection. In particular, we show that current contact tracing plus testing strategies that are aimed at identifying (and testing) individuals with the highest probability of infection are inefficient. Rather, we argue that in a resource constrained testing environment, it is instead better to test those individuals whose expected impact on virus spread is the highest. We rigorously formulate the resource constrained testing problem as a sequential learning problem and provide efficient algorithms to solve it. We also provide numerical results that show the efficacy of our testing strategy.
GenNI: Human-AI Collaboration for Data-Backed Text Generation

Table2Text systems generate textual output based on structured data utilizing machine learning. These systems are essential for fluent natural language interfaces in tools such as virtual assistants; however, left to generate freely these ML systems often produce misleading or unexpected outputs. GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text. The tool utilizes a deep learning model designed with explicit control states. These controls allow users to globally constrain model generations, without sacrificing the representation power of the deep learning models. The visual interface makes it possible for users to interact with AI systems following a Refine-Forecast paradigm to ensure that the generation system acts in a manner human users find suitable. We report multiple use cases on two experiments that improve over uncontrolled generation approaches, while at the same time providing fine-grained control. A demo and source code are available at https://genni.vizhub.ai.
Tribological properties of copper (II) oxide nanoparticle-enriched sandbox bio-lubricant

The demand for bio-lubricant is constantly on the increase due to rapid depletion of world fossil fuel reserves, technological advancement, concern for environmental pollution and its potential to address the energy and environmental problems. This research evaluated the tribological properties of copper (II) oxide nanoparticle-enriched bio-lubricant developed from sandbox (Hura crepitans) seed oil. Copper (II) oxide nanoparticle was added to improve the tribological properties of the bio-lubricant. The experimental parameters were set up according to central composite design of response surface methodology to minimize the numbers of experiments. The bio-lubricants containing varying proportions of copper (II) oxide nanoparticle additive were prepared and their tribological properties were evaluated using ball-on-disc tribometer. The values of parameters used to assess the tribological behaviors were: load (2 N, 5 N, 8 N), speed (150 rpm, 200 rpm, 250 rpm) and nanoparticle concentration (0 wt%, 0.75 wt%, 1.50 wt%). The effects of these parameters on wear rate, friction coefficient and flash temperature parameter were evaluated. The lowest value of coefficient of friction (0.048) was obtained at a speed of 250 rpm and concentration of 0.75 % with load of 5 N. The lowest value of wear rate (0.012) was obtained with load of 5 N, speed of 200 rpm and concentration of 0.75 %. The highest value of flash temperature parameter (0.035) was obtained with load of 5 N, speed of 250 rpm and concentration of 0.75 %. The obtained values complied with the standard specified by American Standards and Testing Materials (ASTM) and compare favourably with conventional lubricant SAE 40 and other bio-lubricants. The results of the analysis were used for optimizing the parameters to obtain the best lubricant effects. The optimal combination of parameters for minimum coefficient of friction and wear rate as well as maximum flash temperature were found to be: 5.0909 N load, 217.6768 rpm speed and 1.1061 wt% concentration. The overall results revealed that enrichment of sandbox oil with copper (II) oxide nanoparticle improved the tribological behavior of the oil.
Effects of cosine tapering window on quantum phase estimation

We provide a modification to the quantum phase estimation algorithm (QPEA) [Abrams and Lloyd, Phys. Rev. Lett. 83, 5162 (1999); Cleve et al., Proc. R. Soc. A 454, 339 (1998); Nielsen and Chuang, Quantum computation and quantum information, 2002.] inspired by classical windowing methods for spectral density estimation. From this modification we obtain an upper bound in the cost that implies a cubic improvement with respect to the algorithm's error rate. Numerical evaluation of the costs also demonstrates an improvement. Moreover, with similar techniques, we detail an iterative projective measurement method for ground state preparation that gives an exponential improvement over previous bounds using QPEA. Numerical tests that confirm the expected scaling behavior are also obtained. For these numerical tests we have used a lattice Thirring model as testing ground. Using well-known perturbation theory results, we also show how to more appropriately estimate the cost scaling with respect to state error instead of evolution operator error.
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training

With the growth of the academic engines, the mining and analysis acquisition of massive researcher data, such as collaborator recommendation and researcher retrieval, has become indispensable. It can improve the quality of services and intelligence of academic engines. Most of the existing studies for researcher data mining focus on a single task for a particular application scenario and learning a task-specific model, which is usually unable to transfer to out-of-scope tasks. The pre-training technology provides a generalized and sharing model to capture valuable information from enormous unlabeled data. The model can accomplish multiple downstream tasks via a few fine-tuning steps. In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT. Specifically, we divide the researchers' data into semantic document sets and community graph. We design the hierarchical Transformer and the local community encoder to capture information from the two categories of data, respectively. Then, we propose three self-supervised learning objectives to train the whole model. Finally, we also propose two transfer modes of RPT for fine-tuning in different scenarios. We conduct extensive experiments to evaluate RPT, results on three downstream tasks verify the effectiveness of pre-training for researcher data mining.
BioVAE: a pre-trained latent variable language model for biomedical text mining

Abstract Summary Large-scale pre-trained language models (PLMs) have advanced state-of-the-art (SOTA) performance on various biomedical text mining tasks. The power of such PLMs can be combined with the advantages of deep generative models. These are examples of these combinations. However, they are trained only on general domain text, and biomedical models are still missing. In this work, we describe BioVAE, the first large-scale pre-trained latent variable language model for the biomedical domain, which uses the OPTIMUS framework to train on large volumes of biomedical text. The model shows SOTA performance on several biomedical text mining tasks when compared to existing publicly available biomedical PLMs. In addition, our model can generate more accurate biomedical sentences than the original OPTIMUS output. Availability and implementation Our source code and pre-trained models are freely available: https://github.com/aistairc/BioVAE. Supplementary information Supplementary data are available at Bioinformatics online.
Inscriptis - A Python-based HTML to text conversion library optimized for knowledge extraction from the Web

Inscriptis provides a library, command line client and Web service for converting HTML to plain text.Its development has been triggered by the need to obtain accurate text representations for knowledge extraction tasks that preserve the spatial alignment of text without drawing upon heavyweight, browser-based solutions such as Selenium (Huggins et al., 2021).In contrast to existing software packages such as HTML2text (Swartz, 2021), jusText (Belica, 2021) and Lynx (Dickey, 2021), Inscriptis 1. provides a layout-aware conversion of HTML that more closely resembles the rendering obtained from standard Web browsers and, therefore, better preserves the spatial arrangement of text elements.Inscriptis excels in terms of conversion quality, since it correctly converts complex HTML constructs such as nested tables and also interprets a subset of HTML (e.g., align, valign) and CSS (e.g., display, white-space, margin-top, vertical-align, etc.) attributes that determine the text alignment.2. supports annotation rules, i.e., user-provided mappings that allow for annotating the extracted text based on structural and semantic information encoded in HTML tags and attributes used for controlling structure and layout in the original HTML document.These unique features ensure that downstream knowledge extraction components can operate on accurate text representations, and may even use information on the semantics and structure of the original HTML document, if annotation support has been enabled. Statement of needResearch in a growing number of scientific disciplines relies upon Web content.Li et al. (2014), for instance, studied the impact of company-specific News coverage on stock prices, in medicine and pharmacovigilance social media listening plays an important role in gathering insights into patient needs and the monitoring of adverse drug effects (Convertino et al., 2018), and communication sciences analyze media coverage to obtain information on the perception and framing of issues as well as on the rise and fall of topics within News and social media (Scharl et al., 2017;Weichselbraun et al., 2021).Computer science focuses on analyzing content by applying knowledge extraction techniques such as entity recognition (Fu et al., 2021) to automatically identify entities (e.g., persons, organizations, locations, products, etc.) within text documents, entity linking (Ding et al., 2021) to link these entities to knowledge bases such as Wikidata and DBPedia, and sentiment
Sudden sensorineural hearing loss: what can we learn from examining Reddit posts?

Many patients with sudden sensorineural hearing loss may seek hearing health information and social support online, although little is known about the online information seeking behaviour.The present study aimed to examine the discussions around sudden sensorineural hearing loss in Reddit posts.A total of 526 Reddit posts about sudden sensorineural hearing loss were extracted and analysed using qualitative and quantitative methods.The content analysis identified eight main categories. Most of the posts were on topics of: sharing personal experiences (34 per cent), describing symptoms (31 per cent), discussing treatment options (36 per cent) and discussing possible causes (19 per cent) of sudden sensorineural hearing loss. The sudden sensorineural hearing loss Reddit posts varied significantly in terms of linguistic variables when compared to baseline Reddit posts. Reddit posts by individuals with sudden sensorineural hearing loss had significantly higher engagement, higher authenticity and made more references to their body when compared to other users.The study results provide insights that can be helpful for professionals during clinical interactions.
An exploratory study on the repeatedly shared external links on Stack Overflow

On Stack Overflow, users reuse 11,926,354 external links to share the resources hosted outside the Stack Overflow website. The external links connect to the existing programming-related knowledge and extend the crowdsourced knowledge on Stack Overflow. Some of the external links, so-called as repeated external links, can be shared for multiple times. We observe that 82.5% of the link sharing activities (i.e., sharing links in any question, answer, or comment) on Stack Overflow share external resources, and 57.0% of the occurrences of the external links are sharing the repeated external links. However, it is still unclear what types of external resources are repeatedly shared. To help users manage their knowledge, we wish to investigate the characteristics of the repeated external links in knowledge sharing on Stack Overflow. In this paper, we analyze the repeated external links on Stack Overflow. We observe that external links that point to the text resources (hosted in documentation websites, tutorial websites, etc.) are repeatedly shared the most. We observe that different users repeatedly share the same knowledge in the form of repeated external links, thus increasing the maintenance effort of knowledge (e.g., update invalid links in multiple posts). The repeated external links can bring risks to the software engineering process, as 1) the same users can repeatedly share the external links for the purpose of promotion, and 2) external links can point to webpages with an overload of information that makes it difficult for users to retrieve relevant information. Our findings provide insights to Stack Overflow moderators and researchers. For example, we encourage Stack Overflow to centrally manage the commonly occurring knowledge in the form of repeated external links in order to better maintain the crowdsourced knowledge on Stack Overflow.