Reviewerly/exHarmony huggingface dataset:
A framework for deep constrained clustering

The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our framework can not only handle standard together/apart constraints (without the well documented negative effects reported earlier) generated from labeled side information but more complex constraints generated from new types of side information such as continuous values and high-level domain knowledge. Furthermore, we propose an efficient training paradigm that is generally applicable to these four types of constraints. We validate the effectiveness of our approach by empirical results on both image and text datasets. We also study the robustness of our framework when learning with noisy constraints and show how different components of our framework contribute to the final performance. Our source code is available at: http://github.com/blueocean92 .
Graphs Cannot Be Indexed in Polynomial Time for Sub-quadratic Time String Matching, Unless SETH Fails

The string matching problem on a node-labeled graph $$G=(V,E)$$ asks whether a given pattern string P has an occurrence in G, in the form of a path whose concatenation of node labels equals P. This is a basic primitive in various problems in bioinformatics, graph databases, or networks, but only recently proven to have a O(|E||P|)-time lower bound, under the Orthogonal Vectors Hypothesis (OVH). We consider here its indexed version, in which we can index the graph in order to support time-efficient string queries. We show that, under OVH, no polynomial-time indexing scheme of the graph can support querying P in time $$O(|P|+|E|^\delta |P|^\beta )$$ , with either $$\delta < 1$$ or $$\beta < 1$$ . As a side-contribution, we introduce the notion of linear independent-components (lic) reduction , allowing for a simple proof of our result. As another illustration that hardness of indexing follows as a corollary of a lic reduction, we also translate the quadratic conditional lower bound of Backurs and Indyk (STOC 2015) for the problem of matching a query string inside a text, under edit distance. We obtain an analogous tight quadratic lower bound for its indexed version, improving the recent result of Cohen-Addad, Feuilloley and Starikovskaya (SODA 2019), but with a slightly different boundary condition.
Parallel Mixed Image Encryption and Extraction Algorithm Based on Compressed Sensing

In the actual image processing process, we often encounter mixed images that contain multiple valid messages. Such images not only need to be transmitted safely, but also need to be able to achieve effective separation at the receiving end. This paper designs a secure and efficient encryption and separation algorithm based on this kind of mixed image. Since chaotic system has the characteristics of initial sensitivity and pseudo-randomness, a chaos matrix is introduced into the compressed sensing framework. By using sequence signal to adjust the chaotic system, the key space can be greatly expanded. In the algorithm, we take the way of parallel transmission to block the data. This method can realize the efficient calculation of complex tasks in the image encryption system and improve the data processing speed. In the decryption part, the algorithm in this paper can not only realize the restoration of images, but also complete the effective separation of images through the improved restoration algorithm.
Research on ResNet101 Network Chemical Reagent Label Image Classification Based on Transfer Learning

Chemical reagent label image classification research plays an important role in dealing with massive reagent classification tasks in chemical laboratories. To solve the lack of performance of traditional image processing methods in image classification and misclassification caused by purely manual classification, as well as there are too few image training data and a lot of manual annotations. This paper proposes a ResNetl01 network classification method based on transfer learning. Under the PyTorch deep learning framework, using ResNetl01 as the basic network framework, and then introducing the transfer learning method, first use the ResNetl01 network parameters trained in the ImageNet dataset to initialize the convolution layer parameters of the chemical reagent label classification model, and then use the chemical reagent label image data train the model, fine-tune the model parameters, and finally get the classification model. The experimental results show that the classification method can obtain a good accuracy on the chemical reagent label image data set.
Expected warning times from the ShakeAlert earthquake early warning system for earthquakes in the Pacific Northwest

First posted March 10, 2021 Revised March 24, 2021 For additional information, contact: Earthquake Science Center—Menlo Park, Calif. OfficeU.S. Geological Survey345 Middlefield Road, MS 977Menlo Park, CA 94025 The ShakeAlert® earthquake early warning system has been live since October 2019 for the testing of public alerting to mobile devices in California and will soon begin testing this modality in Oregon and Washington. The Pacific Northwest presents new challenges and opportunities for ShakeAlert owing to the different types of earthquakes that occur in the Cascadia subduction zone. Many locations in the Pacific Northwest are expected to experience shaking from shallow crustal earthquakes (similar to those in California), earthquakes that occur deep within the subducted slab, and large megathrust earthquakes that occur primarily offshore. The different geometries and maximum magnitudes associated with these types of earthquakes lead to a range of warning times that are possible between when the initial ShakeAlert Message is issued and when a user experiences strong shaking. After an earthquake begins, the strategy of the ShakeAlert system for public alerting is to warn people who are located close enough to the fault that the system estimates they will experience at least weak to moderate shaking. By alerting the public at these low levels of expected shaking, it is possible to provide sufficient warning times for some users to take protective actions before strong shaking begins. In this study, we present an analysis of past ShakeAlert Messages as well as simulations of historical earthquakes and potential future Cascadia earthquakes to quantify the range of warning times that users who experience strong or worse shaking are likely to receive. Additional applications for ShakeAlert involve initiation of automatic protective actions prior to the onset of shaking, such as slowing trains, shutting water supplies, and opening firehouse doors, which are beyond the scope of this paper. Users in the Pacific Northwest should expect that the majority of alerts they receive will be from shallow crustal and intraslab earthquakes. In these cases, users will only have a few seconds of warning before strong shaking begins. This remains true even during infrequent, offshore great (magnitude ≥8) megathrust earthquakes, where warning times will generally range from seconds to tens of seconds, depending on the user’s location and the intensity of predicted shaking that a user chooses to be alerted for, with the longest warning times of 50–80 seconds possible only for users located at considerable distance from the epicenter. ShakeAlert thus requires short, readily understood alerts stating that earthquake shaking is imminent and suggesting protective actions users should take. Extensive education and outreach efforts that emphasize the need to take actions quickly will be required for ShakeAlert to successfully reduce injuries and losses.
Quantum Algorithm for Time-Dependent Hamiltonian Simulation by Permutation Expansion

We present a quantum algorithm for the dynamical simulation of time-dependent Hamiltonians. Our method involves expanding the interaction-picture Hamiltonian as a sum of generalized permutations, which leads to an integral-free Dyson series of the time-evolution operator. Under this representation, we perform a quantum simulation for the time-evolution operator by means of the linear combination of unitaries technique. We optimize the time steps of the evolution based on the Hamiltonian’s dynamical characteristics, leading to a gate count that scales with an L1-norm-like scaling with respect only to the norm of the interaction Hamiltonian, rather than that of the total Hamiltonian. We demonstrate that the cost of the algorithm is independent of the Hamiltonian’s frequencies, implying its advantage for systems with highly oscillating components, and for time-decaying systems the cost does not scale with the total evolution time asymptotically. In addition, our algorithm retains the near optimal log﻿(1/ϵ)/loglog﻿(1/ϵ) scaling with simulation error ϵ.Received 21 April 2021Accepted 25 August 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030342Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum simulationQuantum Information
COVID-19 Implications on the Relationship between Organizational Learning and Performance

This research examines the implications of COVID-19 on the relationship between organisational learning and operational performance in service organisations. By using social learning theory and transactive memory system theory, we developed competing hypotheses on how organisational learning and COVID-19 outbreak interact with each other to explain 10 their impact on operational performance. To test our hypotheses, we surveyed 106 employees of service organisations who have been remotely working during COVID-19 outbreak. The collected data were analysed through multivariate data techniques. We specifically measured operational performance to assess the organisation’s performance as they were the ones immediately affected by the outbreak of COVID-19, which then is expected to have a lagged 15 impact on financial performance. Through exploratory factor analysis, we identified two bundles of COVID-19 work implications, namely home office work environment and social distancing. Results confirmed that home office work environment positively mediates the relationship between organisational learning and operational performance of service organisations. Our research adds to theory, since we evidenced that individuals’ behaviours and work environment reciprocally influence each other, even when individuals are actually apart from the usual work environment, asobserved during the pandemic.
On Biases in Displacement Estimation for Image Registration, with a Focus on Photomechanics

Image registration under small displacements is the keystone of several image analysis tasks such as optical flow estimation, stereoscopic imaging, or full-field displacement estimation in photomechanics. A popular approach consists in locally modeling the displacement field between two images by a parametric transformation and performing least-squares estimation afterward. This procedure is known as “digital image correlation” in several domains as in photomechanics. The present article is part of this approach. First, the estimated displacement is shown to be impaired by biases related to the interpolation scheme needed to reach subpixel accuracy, the image gradient distribution, as well as the difference between the hypothesized parametric transformation and the true displacement. A quantitative estimation of the difference between the estimated value and the actual one is of importance in application domains such as stereoscopy or photomechanics, which have metrological concerns. Second, we question the extent to which these biases could be eliminated or reduced. We also present numerical assessments of our predictive formula in the context of photomechanics. Software codes are freely available to reproduce our results. Although this paper is focused on a particular application field, namely photomechanics, it is relevant to various scientific areas concerned by image registration.
OCC

In the recent trends, cloud computing service users agreed on the concept of pay and use model by accessing all the best services provided by the providers. The major problem here is that there is no standardization so that not everyone coming into the same platform. Now cloud computing efficiently uses time, cost, and effort. In this internet, speed is playing an important role. With the development of each and every field with its proper limitations, the researchers come into the picture that the ontology concept is playing a vital role in the field of computing. The key role of ontology is supporting the knowledge sharing activities. It is giving the set of criteria to prove the use of ontology in the computing world. In the design and development of ontology for computing and mathematics, the information from data centers is very important. The selected design and decisions give efficient and effective results to prove how ontology is playing a vital role in the computing system.
Data Augmentation with Signal Companding for Detection of Logical Access Attacks

The recent advances in voice conversion (VC) and text-to-speech (TTS) make it possible to produce natural sounding speech that poses threat to automatic speaker verification (ASV) systems. To this end, research on spoofing countermeasures has gained attention to protect ASV systems from such attacks. While the advanced spoofing countermeasures are able to detect known nature of spoofing attacks, they are not that effective under unknown attacks. In this work, we propose a novel data augmentation technique using a-law and mu-law based signal companding. We believe that the proposed method has an edge over traditional data augmentation by adding small perturbation or quantization noise. The studies are conducted on ASVspoof 2019 logical access corpus using light convolutional neural network based system. We find that the proposed data augmentation technique based on signal companding outperforms the state-of-the-art spoofing countermeasures showing ability to handle unknown nature of attacks.
Security Hardening of Botnet Detectors Using Generative Adversarial Networks

Machine learning (ML) based botnet detectors are no exception to traditional ML models when it comes to adversarial evasion attacks. The datasets used to train these models have also scarcity and imbalance issues. We propose a new technique named Botshot, based on generative adversarial networks (GANs) for addressing these issues and proactively making botnet detectors aware of adversarial evasions. Botshot is cost-effective as compared to the network emulation for botnet traffic data generation rendering the dedicated hardware resources unnecessary. First, we use the extended set of network flow and time-based features for three publicly available botnet datasets. Second, we utilize two GANs (vanilla, conditional) for generating realistic botnet traffic. We evaluate the generator performance using classifier two-sample test (C2ST) with 10-fold 70-30 train-test split and propose the use of 'recall' in contrast to 'accuracy' for proactively learning adversarial evasions. We then augment the train set with the generated data and test using the unchanged test set. Last, we compare our results with benchmark oversampling methods with augmentation of additional botnet traffic data in terms of average accuracy, precision, recall and F1 score over six different ML classifiers. The empirical results demonstrate the effectiveness of the GAN-based oversampling for learning in advance the adversarial evasion attacks on botnet detectors.
Style Normalization In Histology With Federated Learning

The global cancer burden is on the rise, and Artificial Intelligence (AI) has become increasingly crucial to achieve more objective and efficient diagnosis in digital pathology. Current AI-assisted histopathology analysis methods need to address the following two issues. First, the color variations due to use of different stains need to be tackled such as with stain style transfer technique. Second, in parallel with heterogeneity, datasets from individual clinical institutions are characterized by privacy regulations, and thus need to be addressed such as with robust data-private collaborative training. In this paper, to address the color heterogeneity problem, we propose a novel generative adversarial network with one orchestrating generator and multiple distributed discriminators for stain style transfer. We also incorporate Federated Learning (FL) to further preserve data privacy and security from multiple data centers. We use a large cohort of histopathology datasets as a case study.
Fully Remote Software Development Due to COVID Factor

The internal transformation and using the fully remote software development under the influence of the pandemic has not only changed the industry, but heralded the construction of a new reality. This article presents the results of a study that covered the experience of transformation of 26 project teams from the world's leading IT corporations, software vendors, and high-tech companies with strong internal development practices: Alphabet, Amazon, BSC Group, Custis, Deutsche Bank, Evernote, Exness, Positive Technologies, PromSvyazBank, Sberbank, VTB, Yandex. Experts determined the results of rapid adaptation to changes, considered the medium-term impact of the pandemic factor on work processes, and made forecasts for 2021. The results of the study are accompanied by brief comments and recommendations of the author, the main idea of which is the need to quickly understand a new trend in software development, hiring specialists, and organizing teams associated with the refusal of high-tech IT companies to return to teamwork in shared offices.
A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space

The representation degeneration problem in Contextual Word Representations (CWRs) hurts the expressiveness of the embedding space by forming an anisotropic cone where even unrelated words have excessively positive correlations.Existing techniques for tackling this issue require a learning process to re-train models with additional objectives and mostly employ a global assessment to study isotropy.Our quantitative analysis over isotropy shows that a local assessment could be more accurate due to the clustered structure of CWRs.Based on this observation, we propose a local cluster-based method to address the degeneration issue in contextual embedding spaces.We show that in clusters including punctuations and stop words, local dominant directions encode structural information, removing which can improve CWRs performance on semantic tasks.Moreover, we find that tense information in verb representations dominates sense semantics.We show that removing dominant directions of verb representations can transform the space to better suit semantic applications.Our experiments demonstrate that the proposed cluster-based method can mitigate the degeneration problem on multiple tasks. 1
Pseudolinear kalman filters for target tracking using hybrid measurements

This paper presents new variants of the pseudolinear Kalman filter (PLKF) for target tracking in 2D-plane using angle-of-arrival, time-difference-of-arrival and frequency-difference-of-arrival measurements collected by stationary sensors. Using hybrid measurements can yield performance advantage over the traditional bearings-only estimators, but may involve complex noise vector and correlation between the measurement matrix and the noise vector. A closed-form PLKF is developed by rearranging measurement equations to compensate the non-zero mean of the noise vector. To tackle the bias issue of PLKF, the bias is derived and compensated instantaneously, leading to the proposed BCPLKF estimator. Then a new variant of instrumental variable-based Kalman filter (IVKF) was presented, which alleviates the bias by utilizing BCPLKF estimates instead of noisy measurements to compute the measurement matrix. In addition, the posterior Cramér-Rao lower bound (PCRLB) is derived for the nonlinear filtering problem. Simulation results demonstrate that the proposed estimators outperform the bearings-only estimator significantly and have the mean squared error fairly close to the PCRLB.
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking

Injecting external domain-specific knowledge (e.g., UMLS) into pretrained language models (LMs) advances their capability to handle specialised in-domain tasks such as biomedical entity linking (BEL).However, such abundant expert knowledge is available only for a handful of languages (e.g., English).In this work, by proposing a novel cross-lingual biomedical entity linking task (XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically diverse languages, we first investigate the ability of standard knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual LMs beyond the standard monolingual English BEL task.The scores indicate large gaps to English performance.We then address the challenge of transferring domainspecific knowledge from resource-rich languages to resource-poor ones.To this end, we propose and evaluate a series of crosslingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data.Remarkably, we show that our proposed domain-specific transfer methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language, and without any in-domain parallel data.
SAT-based Decision Tree Learning for Large Data Sets

Decision trees of low depth are beneficial for understanding and interpreting the data they represent. Unfortunately, finding a decision tree of lowest depth that correctly represents given data is NP-hard. Hence known algorithms either (i) utilize heuristics that do not optimize the depth or (ii) are exact but scale only to small or medium-sized instances. We propose a new hybrid approach to decision tree learning, combining heuristic and exact methods in a novel way. More specifically, we employ SAT encodings repeatedly to local parts of a decision tree provided by a standard heuristic, leading to a global depth improvement. This allows us to scale the power of exact SAT-based methods to almost arbitrarily large data sets. We evaluate our new approach experimentally on a range of real-world instances that contain up to several thousand samples. In almost all cases, our method successfully decreases the depth of the initial decision tree; often, the decrease is significant.