Reviewerly/exHarmony huggingface dataset:
Robust isochron calculation

Abstract. The standard classical statistics approach to isochron calculation assumes that the distribution of uncertainties on the data arising from isotopic analysis is strictly Gaussian. This effectively excludes datasets that have more scatter from consideration, even though many appear to have age significance. A new approach to isochron calculations is developed in order to circumvent this problem, requiring only that the central part of the uncertainty distribution of the data defines a “spine” in the trend of the data. This central spine can be Gaussian but this is not a requirement. This approach significantly increases the range of datasets from which age information can be extracted but also provides seamless integration with well-behaved datasets and thus all legacy age determinations. The approach is built on the robust statistics of Huber (1981) but using the data uncertainties for the scale of data scatter around the spine rather than a scale derived from the scatter itself, ignoring the data uncertainties. This robust data fitting reliably determines the position of the spine when applied to data with outliers but converges on the classical statistics approach for datasets without outliers. The spine width is determined by a robust measure, the normalised median absolute deviation of the distances of the data points to the centre of the spine, divided by the uncertainties on the distances. A test is provided to ascertain that there is a spine in the data, requiring that the spine width is consistent with the uncertainties expected for Gaussian-distributed data. An iteratively reweighted least squares algorithm is presented to calculate the position of the robust line and its uncertainty, accompanied by an implementation in Python.
Optimizing ordered graph algorithms with GraphIt

Many graph problems can be solved using ordered parallel graph algorithms that achieve significant speedup over their unordered counterparts by reducing redundant work. This paper introduces a new priority-based extension to GraphIt, a domain-specific language for writing graph applications, to simplify writing high-performance parallel ordered graph algorithms. The extension enables vertices to be processed in a dynamic order while hiding low-level implementation details from the user. We extend the compiler with new program analyses, transformations, and code generation to produce fast implementations of ordered parallel graph algorithms. We also introduce bucket fusion, a new performance optimization that fuses together different rounds of ordered algorithms to reduce synchronization overhead, resulting in 1.2×–3× speedup over the fastest existing ordered algorithm implementations on road networks with large diameters. With the extension, GraphIt achieves up to 3× speedup on six ordered graph algorithms over state-of-the-art frameworks and hand-optimized implementations (Julienne, Galois, and GAPBS) that support ordered algorithms.
Using reservoir computer to predict and prevent extreme events

We show that a reservoir computer is an effective tool for model-free prediction of extreme events in deterministic chaotic systems. This prediction allows us to suppress unwanted extreme events, by applying weak control perturbations to the system at times preceding expected extreme events. The effectiveness of such a prediction and prevention strategy is demonstrated for a system of globally coupled FitzHugh-Nagumo neurons and for a system of two almost identical unidirectionally coupled chaotic oscillators.
ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding

The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art approaches typically tackle this problem by integrating an elaborate attention mechanism or (part-) localization method into a standard convolutional neural network (CNN). Also in this work the aim is to enhance the performance of a backbone CNN such as ResNet by including three efficient and lightweight components specifically designed for FGVC. This is achieved by using global k-max pooling, a discriminative embedding layer trained by optimizing class means and an efficient localization module that estimates bounding boxes using only class labels for training. The resulting model achieves state-of-the-art recognition accuracies on multiple FGVC benchmark datasets.
Dynamic Decentralized Functional Encryption

We introduce Dynamic Decentralized Functional Encryption ( $$\textsf {DDFE}$$ ), a generalization of Functional Encryption which allows multiple users to join the system dynamically, without relying on a trusted third party or on expensive and interactive Multi-Party Computation protocols. This notion subsumes existing multi-user extensions of Functional Encryption, such as Multi-Input, Multi-Client, and Ad Hoc Multi-Input Functional Encryption. We define and construct schemes for various functionalities which serve as building-blocks for latter primitives and may be useful in their own right, such as a scheme for dynamically computing sums in any Abelian group. These constructions build upon simple primitives in a modular way, and have instantiations from well-studied assumptions, such as DDH or LWE. Our constructions culminate in an Inner-Product scheme for computing weighted sums on aggregated encrypted data, from standard assumptions in prime-order groups in the Random Oracle Model.
Multi-Targeted Backdoor: Indentifying Backdoor Attack for Multiple Deep Neural Networks

We propose a multi-targeted backdoor that misleads different models to different classes. The method trains multiple models with data that include specific triggers that will be misclassified by different models into different classes. For example, an attacker can use a single multi-targeted backdoor sample to make model A recognize it as a stop sign, model B as a left-turn sign, model C as a right-turn sign, and model D as a U-turn sign. We used MNIST and Fashion-MNIST as experimental datasets and Tensorflow as a machine learning library. Experimental results show that the proposed method with a trigger can cause misclassification as different classes by different models with a 100% attack success rate on MNIST and Fashion-MNIST while maintaining the 97.18% and 91.1% accuracy, respectively, on data without a trigger.
Modified transform‐based gamma correction for <scp>MRI</scp> tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Abstract Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low‐ and high‐pass tetrolet coefficients. In the second module, multiple low‐pass tetrolet coefficients are applied through a modified transform‐based gamma correction method. Generalized cross‐validation is used on multiple high‐pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon‐based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal‐to‐noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.
Smart Contract Based Industrial Data Preservation on Block Chain

The ground work for establishing the industrial 4.0 age involves the tiered architectures that are made up of multiple planes encompassed with the physical things that are embedded with internet capabilities. This infrastructure enables an incorporation of the various functions that are necessary in a manufacturing industry to enhance the efficiency of the industry, and contrivance a highly supple and a self-organizing industry that is smart. On the other hand it becomes essential to preserve the information’s safely as many internet enabled devices are utilized and more networks are formed for the proper incorporation of the various activities taking place. Since the conventional methods are inadequate the block chain method was most preferred due to its transparency and highly reliable and expedient services. To further structure a tamper proof network the blockchain is built with the smart contract laid on the system providing the impermeable authenticity on the industrial data transferred by securing the information’s against the authentication that is anonymous. The performance of the system is validated to on the basis of security provided on the transactions made, and the cost spent on the proposed design.
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

We present HERO, a novel framework for large-scale video+language omni-representation learning. HERO encodes multimodal inputs in a hierarchical structure, where local context of a video frame is captured by a Cross-modal Transformer via multimodal fusion, and global video context is captured by a Temporal Transformer. In addition to standard Masked Language Modeling (MLM) and Masked Frame Modeling (MFM) objectives, we design two new pre-training tasks: (i) Video-Subtitle Matching (VSM), where the model predicts both global and local temporal alignment; and (ii) Frame Order Modeling (FOM), where the model predicts the right order of shuffled video frames. HERO is jointly trained on HowTo100M and large-scale TV datasets to gain deep understanding of complex social dynamics with multi-character interactions. Comprehensive experiments demonstrate that HERO achieves new state of the art on multiple benchmarks over Text-based Video/Video-moment Retrieval, Video Question Answering (QA), Video-and-language Inference and Video Captioning tasks across different domains. We also introduce two new challenging benchmarks How2QA and How2R for Video QA and Retrieval, collected from diverse video content over multimodalities.
Attitude-Constrained Time-Optimal Trajectory Planning for Rotorcrafts: Theory and Application to Visual Servoing

In this article, a novel real-time time-optimal trajectory planning (TOTP) approach is proposed for rotorcrafts under velocity constraints and complex nonconvex thrust, attitude input constraints. Different from existing works, the proposed method is the first real-time TOTP solution for rotorcrafts subject to velocity constraints and these nonconvex input constraints. The key insight is to transform the thrust and attitude constraints into the inequality constraints on the quadratic nonlinear function of the path parameter acceleration and the squared velocity, by exploiting the differential flatness characteristic of the rotorcraft. As a result, the constraints can be formulated as the problem of jointly solving analytical quadratic inequalities. On this basis, a path acceleration calculation algorithm is proposed to obtain the backward and forward acceleration profiles. In addition, by formulating the well-known field of view (FOV) constraints into the attitude constraints, the proposed framework can further guarantee the FOV constraint for visual servoing of rotorcrafts. Comparative experimental results are presented to show the superior performance of the proposed approach when compared with the second-order cone programming approach. In addition, some visual servoing experiments are conducted to demonstrate the capability of the proposed method to guarantee the FOV constraint.
A Multi-Layered Data Encryption and Decryption Scheme Based on Genetic Algorithm and Residual Numbers

Over the years, Steganography and Cryptography have been complementary techniques for enforcing security of digital data. The need for the development of robust multi-layered schemes to counter the exponential grow in the power of computing devices that can compromise security is critical in the design and implementation of security systems. Therefore, we propose a new combined steganographic and cryptographic scheme using the operators of genetic algorithm (GA) such selection, crossover and mutation, and some properties of the residue number system (RNS) with an appropriate fusing technique in order to embed encrypted text within images. The proposed scheme was tested using MatLab <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> R2017b and a CORE™i7 processor. Simulation results show that the proposed scheme can be deployed at one level with only the stego image containing the encrypted hidden message and at another level where the stego message is further encrypted. An analysis based on standard key metrics such as visual perception and statistical methods on steganalysis and cryptanalysis show that the proposed scheme is robust, is not complex with reduced runtime and will consume less power due to the use of residue numbers when compared to similar existing schemes.
Architecture and noise analysis of continuous-variable quantum gates using two-dimensional cluster states

Due to its unique scalability potential, continuous-variable quantum optics is a promising platform for large-scale quantum computing. In particular, very large cluster states with a two-dimensional topology that are suitable for universal quantum computing and quantum simulation can be readily generated in a deterministic manner, and routes towards fault tolerance via bosonic quantum error correction are known. In this article we propose a complete measurement-based quantum computing architecture for the implementation of a universal set of gates on the recently generated two-dimensional cluster states [M. V. Larsen et al., Science 366, 369 (2019); W. Asavanant et al., Science 366, 373 (2019)]. We analyze the performance of the various quantum gates that are executed in these cluster states as well as in other two-dimensional cluster states (the bilayer-square lattice and quad-rail lattice cluster states [R. N. Alexander et al., Phys. Rev. A 94, 032327 (2016); N. C. Menicucci, Phys. Rev. A 83, 062314 (2011)]) by estimating and minimizing the associated stochastic noise addition as well as the resulting gate error probability. We compare the four different states and find that, although they all allow for universal computation, the quad-rail lattice cluster state performs better than the other three states, which all exhibit similar performance.
Assessment of University Willingness to the Transition to Digital Educational Environment

The digital transformation has affected all economically developed countries in the world including Russia. The issues of transition to the digital economy are considered at the highest level: the decrees of the President of the Russian Federation created national and federal projects, including in the higher education system. But the transition to the digital environment is rather slow. The article presents an assessment of the current state and trends in the development of the domestic system of higher education, identifies the problems of transition of Russian universities to the digital educational environment. The purpose of the article is to generalize and systematize the existing developments and create a methodology for assessing the degree of universities willingness for digitalization. The authors have systematized and expanded the list of factors for assessing the willingness of universities for digital transformation. The main research methods are: expert assessment method, morphological analysis, cluster analysis. The matrix of factors and their features formed the basis for ranking indicators and forming clusters. The method proposed by the authors is universal, it has been tested at one of the Russian universities – NSTU NETI and can be applied by any institution of higher education to assess the willingness of the University for digitalization.
Is the online learning good in the midst of Covid-19 Pandemic? The case of EFL learners

The purpose of this study is to investigate the learners’ perception on online learning in the midst of a COVID-19 pandemic. This research applied qualitative method. The subject of this research are the learners of English study program of UKI Toraja. The instrument used is semi-structured interview. Thus the pandemic of covid-19, the researcher interviewed the learners by calling them by using WhatsApp application.  A thematic analysis was employed in the current study. The study showed that the learners’ perception on online learning reveals that it is good in the midst of COVID-19 pandemic. They perceived online learning is very helpful in the middle of pandemic. This study not only report that online learning is good in the midst of COVID-19 pandemic but also spotted the light on the availability of internet access, financial issue, and online learning implementation. At the learners’ condition in terms of financial issue, they hope that lecturers make use of facilities such as free Messenger application in Online Learning System. In the light of the availability of internet access, they said that individual tasks are better to keep the distance physically due to pandemic, they need group tasks to help friends who do not have an internet pulse and access. About the implementation, they hope that material and assignment must be preceded by explanation. Thus, they recommended that Voice Note will be effectively used when giving instructions. It implied that the material and instruction implemented by the lecturer in the online learning were not easy to use.
Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning

Dynamic hand gesture recognition based on one-shot learning requires full assimilation of the motion features from a few annotated data. However, how to effectively extract the spatio-temporal features of the hand gestures remains a challenging issue. This paper proposes a skeleton-based dynamic hand gesture recognition using an enhanced network (GREN) based on one-shot learning by improving the memory-augmented neural network, which can rapidly assimilate the motion features of dynamic hand gestures. Besides, the network effectively combines and stores the shared features between dissimilar classes, which lowers the prediction error caused by the unnecessary hyper-parameters updating, and improves the recognition accuracy with the increase of categories. In this paper, the public dynamic hand gesture database (DHGD) is used for the experimental comparison of the state-of-the-art performance of the GREN network, and although only 30% of the dataset was used for training, the accuracy of skeleton-based dynamic hand gesture recognition reached 82.29% based on one-shot learning. Experiments with the Microsoft Research Asia (MSRA) hand gesture dataset verified the robustness of the GREN network. The experimental results demonstrate that the GREN network is feasible for skeleton-based dynamic hand gesture recognition based on one-shot learning.
A Joint Model for Document Segmentation and Segment Labeling

Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections.Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly.We introduce the Segment Pooling LSTM (S-LSTM) model, which is capable of jointly segmenting a document and labeling segments.In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments.We show that S-LSTM reduces segmentation error by 30% on average, while also improving segment labeling.
Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment

Entity alignment (EA) aims to identify entities located in different knowledge graphs (KGs) that refer to the same real-world object. To learn the entity representations, most EA approaches rely on either translation-based methods which capture the local relation semantics of entities or graph convolutional networks (GCNs), which exploit the global KG structure. Afterward, the aligned entities are identified based on their distances. In this paper, we propose to jointly leverage the global KG structure and entity-specific relational triples for better entity alignment. Specifically, a global structure and local semantics preserving network is proposed to learn entity representations in a coarse-to-fine manner. Experiments on several real-world datasets show that our method significantly outperforms other entity alignment approaches and achieves the new state-of-the-art performance.
On Deep Unsupervised Active Learning

Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating. Most existing works are based on shallow linear models by assuming that each sample can be well approximated by the span (i.e., the set of all linear combinations) of certain selected samples, and then take these selected samples as representative ones to label. However, in practice, the data do not necessarily conform to linear models, and how to model nonlinearity of data often becomes the key point to success. In this paper, we present a novel Deep neural network framework for Unsupervised Active Learning, called DUAL. DUAL can explicitly learn a nonlinear embedding to map each input into a latent space through an encoder-decoder architecture, and introduce a selection block to select representative samples in the the learnt latent space. In the selection block, DUAL considers to simultaneously preserve the whole input patterns as well as the cluster structure of data. Extensive experiments are performed on six publicly available datasets, and experimental results clearly demonstrate the efficacy of our method, compared with state-of-the-arts.
Reranking for Efficient Transformer-based Answer Selection

IR-based Question Answering (QA) systems typically use a sentence selector to extract the answer from retrieved documents. Recent studies have shown that powerful neural models based on the Transformer can provide an accurate solution to Answer Sentence Selection (AS2). Unfortunately, their computation cost prevents their use in real-world applications. In this paper, we show that standard and efficient neural rerankers can be used to reduce the amount of sentence candidates fed to Transformer models without hurting Accuracy, thus improving efficiency up to four times. This is an important finding as the internal representation of shallower neural models is dramatically different from the one used by a Transformer model, e.g., word vs. contextual embeddings.