Reviewerly/exHarmony huggingface dataset:
Science–graphic art partnerships to increase research impact

Graphics are becoming increasingly important for scientists to effectively communicate their findings to broad audiences, but most researchers lack expertise in visual media. We suggest collaboration between scientists and graphic designers as a way forward and discuss the results of a pilot project to test this type of collaboration.
Deep learning with perspective modeling for early detection of malignancy in mammograms

Malignancy in human bodies are named behind the body part in which it invades exponentially like lungs cancer if malignancy is invading lungs. Growth of a cell is controlled by its centroid and it goes uncontrolled if it is behaving abnormally. Objective of this work is to deliver a classification system that can be used to classify breast images as a benign or malignant and if malignant then can further classify which type of malignancy that is non-invasive or invasive cancer. This model can also prescribe treatment for predicted malignant class with details like time taken, degree of seriousness, probability of curing by opted treatment because treatment of a breast cancer depends on type and stage of malignancy. To achieve higher or clinical usage accuracy by deploying advances of soft computing and image analysis like deep learning and deep neural networks to decrease breast cancer death as a concrete effort using mammograms by detecting breast cancer in an early stage.
Health TAPESTRY: co-designing interprofessional primary care programs for older adults using the persona-scenario method

Working with patients and health care providers to co-design health interventions is gaining global prominence. While co-design of interventions is important for all patients, it is particularly important for older adults who often experience multiple and complex chronic conditions. Persona-scenarios have been used by designers of technology applications. The purpose of this paper is to explore how a modified approach to the persona-scenario method was used to co-design a complex primary health care intervention (Health TAPESTRY) by and for older adults and providers and the value added of this approach.The persona-scenario method involved patient and clinician participants from two academically-linked primary care practices. Local prospective volunteers and community service providers (e.g., home care services, support services) were also recruited. Persona-scenario workshops were facilitated by researchers experienced in qualitative methods. Working mostly in homogenous pairs, participants created a fictitious but authentic persona that represented people like themselves. Core components of the Health TAPESTRY intervention were described. Then, participants created a story (scenario) involving their persona and an aspect of the proposed Health TAPESTRY program (e.g., volunteer roles). Two stages of analysis involved descriptive identification of themes, followed by an interpretive phase to extract possible actions and products related to ideas in each theme.Fourteen persona-scenario workshops were held involving patients (n = 15), healthcare providers/community care providers (n = 29), community service providers (n = 12), and volunteers (n = 14). Fifty themes emerged under four Health TAPESTRY components and a fifth category - patient. Eight cross cutting themes highlighted areas integral to the intervention. In total, 414 actions were identified and 406 products were extracted under the themes, of which 44.8% of the products (n = 182) were novel. The remaining 224 had been considered by the research team.The persona-scenario method drew out feasible novel ideas from stakeholders, which expanded on the research team's original ideas and highlighted interactions among components and stakeholder groups. Many ideas were integrated into the Health TAPESTRY program's design and implementation. Persona-scenario method added significant value worthy of the added time it required. This method presents a promising alternative to active engagement of multiple stakeholders in the co-design of complex interventions.
Coverage guided, property based testing

Property-based random testing, exemplified by frameworks such as Haskell's QuickCheck, works by testing an executable predicate (a property) on a stream of randomly generated inputs. Property testing works very well in many cases, but not always. Some properties are conditioned on the input satisfying demanding semantic invariants that are not consequences of its syntactic structure---e.g., that an input list must be sorted or have no duplicates. Most randomly generated inputs fail to satisfy properties with such sparse preconditions, and so are simply discarded. As a result, much of the target system may go untested. We address this issue with a novel technique called coverage guided, property based testing (CGPT). Our approach is inspired by the related area of coverage guided fuzzing, exemplified by tools like AFL. Rather than just generating a fresh random input at each iteration, CGPT can also produce new inputs by mutating previous ones using type-aware, generic mutator operators. The target program is instrumented to track which control flow branches are executed during a run and inputs whose runs expand control-flow coverage are retained for future mutations. This means that, when sparse conditions in the target are satisfied and new coverage is observed, the input that triggered them will be retained and used as a springboard to go further. We have implemented CGPT as an extension to the QuickChick property testing tool for Coq programs; we call our implementation FuzzChick. We evaluate FuzzChick on two Coq developments for abstract machines that aim to enforce flavors of noninterference, which has a (very) sparse precondition. We systematically inject bugs in the machines' checking rules and use FuzzChick to look for counterexamples to the claim that they satisfy a standard noninterference property. We find that vanilla QuickChick almost always fails to find any bugs after a long period of time, as does an earlier proposal for combining property testing and fuzzing. In contrast, FuzzChick often finds them within seconds to minutes. Moreover, FuzzChick is almost fully automatic; although highly tuned, hand-written generators can find the bugs faster, they require substantial amounts of insight and manual effort.
Modeling Individual and Team Behavior through Spatio-temporal Analysis

Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Wrapping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: BoomTown, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.
The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years

It has been increasingly recognized that preservation and public accessibility of primary experimental data are cornerstones necessary for the reproducibility of empirical sciences. In the field of molecular crystallography, many journals now recommend that authors of manuscripts presenting a new crystal structure should deposit their primary experimental data (X-ray diffraction images) to one of the dedicated resources created in recent years. Here, we describe our experiences developing the Integrated Resource for Reproducibility in Molecular Crystallography (IRRMC) and describe several examples of a crucial role that diffraction data can play in improving previously determined protein structures. In its first four years, several hundred crystallographers have deposited data from over 5200 diffraction experiments performed at over 60 different synchrotron beamlines or home sources all over the world. In addition to improving the resource and curating submitted data, we have been building a pipeline for extraction or, in some cases, reconstruction of the metadata necessary for seamless automated processing. Preliminary analysis indicates that about 95% of the archived data can be automatically reprocessed. A high rate of reprocessing success shows the feasibility of using the automated metadata extraction and automated processing as a validation step for the deposition of raw diffraction images. The IRRMC is guided by the Findable, Accessible, Interoperable, and Reusable data management principles.
Visualization by example

While visualizations play a crucial role in gaining insights from data, generating useful visualizations from a complex dataset is far from an easy task. In particular, besides understanding the functionality provided by existing visualization libraries, generating the desired visualization also requires reshaping and aggregating the underlying data as well as composing different visual elements to achieve the intended visual narrative. This paper aims to simplify visualization tasks by automatically synthesizing the required program from simple visual sketches provided by the user. Specifically, given an input data set and a visual sketch that demonstrates how to visualize a very small subset of this data, our technique automatically generates a program that can be used to visualize the entire data set. From a program synthesis perspective, automating visualization tasks poses several challenges that are not addressed by prior techniques. First, because many visualization tasks require data wrangling in addition to generating plots from a given table, we need to decompose the end-to-end synthesis task into two separate sub-problems. Second, because the intermediate specification that results from the decomposition is necessarily imprecise, this makes the data wrangling task particularly challenging in our context. In this paper, we address these problems by developing a new compositional visualization-by-example technique that (a) decomposes the end-to-end task into two different synthesis problems over different DSLs and (b) leverages bi-directional program analysis to deal with the complexity that arises from having an imprecise intermediate specification. We have implemented our visualization-by-example approach in a tool called Viser and evaluate it on over 80 visualization tasks collected from on-line forums and tutorials. Viser can solve 84 of these benchmarks within a 600 second time limit, and, for those tasks that can be solved, the desired visualization is among the top-5 generated by Viser in 70% of the cases.
SloMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers. Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates. Inspired by the BMUF method of Chen & Huo (2016), we propose a slow momentum (SloMo) framework, where workers periodically synchronize and perform a momentum update, after multiple iterations of a base optimization algorithm. Experiments on image classification and machine translation tasks demonstrate that SloMo consistently yields improvements in optimization and generalization performance relative to the base optimizer, even when the additional overhead is amortized over many updates so that the SloMo runtime is on par with that of the base optimizer. We provide theoretical convergence guarantees showing that SloMo converges to a stationary point of smooth non-convex losses. Since BMUF is a particular instance of the SloMo framework, our results also correspond to the first theoretical convergence guarantees for BMUF.
UKF Based on Maximum Correntropy Criterion in the Presence of Both Intermittent Observations and Non-Gaussian Noise

Motivated by tracking applications with sensor networks under non-Gaussian noise and intermittent observations, this paper considers a maximum correntropy unscented Kalman filter (MCUKF). MCUKF is based on maximum correntropy criterion (MCC) and unscented transformation (UT) which can deal with both non-Gaussian noise and intermittent observations. The intermittent observations are described by a binary sequence satisfying some properties. The MCC is used to deal with non-Gaussian noise and improves the robustness. Moreover, the arrival probabilities under non-Gaussian noise (shot noise and Gaussian mixture noise) and intermittent observations are given. The performance of the presented algorithm is verified by illustrating numerical examples.
Sentiment analysis of tweets through Altmetrics: A machine learning approach

The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.
NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.
Adaptive, Distribution-Free Prediction Intervals for Deep Networks

The machine learning literature contains several constructions for prediction intervals thatare intuitively reasonable but ultimately ad-hoc in that they do not come with provableperformance guarantees. We present methods from the statistics literature that can beused efficiently with neural networks underminimal assumptions with guaranteed performance. We propose a neural network thatoutputs three values instead of a single pointestimate and optimizes a loss function motivated by the standard quantile regression loss. We provide two prediction interval methodswith finite sample coverage guarantees solelyunder the assumption that the observations are independent and identically distributed. The first method leverages the conformal in-ference framework and provides average coverage. The second method provides a new, stronger guarantee by conditioning on the observed data. Lastly, our loss function doesnot compromise the predictive accuracy of thenetwork like other prediction interval methods. We demonstrate the ease of use of our procedures as well as its improvements overother methods on both simulated and realdata. As most deep networks can easily be modified by our method to output predictions with valid prediction intervals, its use should become standard practice, much like reporting standard errors along with mean estimates.
Fast and Secure Updatable Encryption

Updatable encryption allows a client to outsource ciphertexts to some untrusted server and periodically rotate the encryption key. The server can update ciphertexts from an old key to a new key with the help of an update token, received from the client, which should not reveal anything about plaintexts to an adversary. We provide a new and highly efficient suite of updatable encryption schemes that we collectively call $$\mathsf {SHINE}$$ . In the variant designed for short messages, ciphertext generation consists of applying one permutation and one exponentiation (per message block), while updating ciphertexts requires just one exponentiation. Variants for longer messages provide much stronger security guarantees than prior work that has comparable efficiency. We present a new confidentiality notion for updatable encryption schemes that implies prior notions. We prove that $$\mathsf {SHINE}$$ is secure under our new confidentiality definition while also providing ciphertext integrity.
New semi-quantum key agreement protocol based on high-dimensional single-particle states*

A new efficient two-party semi-quantum key agreement protocol is proposed with high-dimensional single-particle states. Different from the previous semi-quantum key agreement protocols based on the two-level quantum system, the propounded protocol makes use of the advantage of the high-dimensional quantum system, which possesses higher efficiency and better robustness against eavesdropping. Besides, the protocol allows the classical participant to encode the secret key with qudit shifting operations without involving any quantum measurement abilities. The designed semi-quantum key agreement protocol could resist both participant attacks and outsider attacks. Meanwhile, the conjoint analysis of security and efficiency provides an appropriate choice for reference on the dimension of single-particle states and the number of decoy states.
Defending Bit-Flip Attack through DNN Weight Reconstruction

Recent studies show that adversarial attacks on neural network weights, aka, Bit-Flip Attack (BFA), can degrade Deep Neural Network’s (DNN) prediction accuracy severely. In this work, we propose a novel weight reconstruction method as a countermeasure to such BFAs. Specifically, during inference, the weights are reconstructed such that the weight perturbation due to BFA is minimized or diffused to the neighboring weights. We have successfully demonstrated that our method can significantly improve the DNN robustness against random and gradient-based BFA variants. Even under the most aggressive attacks (i.e., greedy progressive bit search), our method maintains a test accuracy of 60% on ImageNet after 5 iterations while the baseline accuracy drops to below 1%.
Text Segmentation by Cross Segment Attention

Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization. In this work, we propose three transformer-based architectures and provide comprehensive comparisons with previously proposed approaches on three standard datasets. We establish a new state-of-the-art, reducing in particular the error rates by a large margin in all cases. We further analyze model sizes and find that we can build models with many fewer parameters while keeping good performance, thus facilitating real-world applications.
A Novel Eye Center Localization Method for Head Poses With Large Rotations

Eye localization is undoubtedly crucial to acquiring large amounts of information. It not only helps people improve their understanding of others but is also a technology that enables machines to better understand humans. Although studies have reported satisfactory accuracy for frontal faces or head poses at limited angles, large head rotations generate numerous defects (e.g., disappearance of the eye), and existing methods are not effective enough to accurately localize eye centers. Therefore, this study makes three contributions to address these limitations. First, we propose a novel complete representation (CR) pipeline that can flexibly learn and generate two complete representations, namely the CR-center and CR-region, of the same identity. We also propose two novel eye center localization methods. This first method employs geometric transformation to estimate the rotational difference between two faces and an unknown-localization strategy for accurate transformation of the CR-center. The second method is based on image translation learning and uses the CR-region to train the generative adversarial network, which can then accurately generate and localize eye centers. Five image databases are employed to verify the proposed methods, and tests reveal that compared with existing methods, the proposed method can more accurately and robustly localize eye centers in challenging images, such as those showing considerable head rotation (both yaw rotation of -67.5° to +67.5° and roll rotation of +120° to -120°), complete occlusion of both eyes, poor illumination in addition to head rotation, head pose changes in the dark, and various gaze interaction.