Reviewerly/exHarmony huggingface dataset:
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information

Pre-trained language model word representation, such as BERT, have been extremely successful in several Natural Language Processing tasks significantly improving on the state-of-the-art. This can largely be attributed to their ability to better capture semantic information contained within a sentence. Several tasks, however, can benefit from information available at a corpus level, such as Term Frequency-Inverse Document Frequency (TF-IDF). In this work we test the effectiveness of integrating this information with BERT on the task of identifying abuse on social media and show that integrating this information with BERT does indeed significantly improve performance. We participate in Sub-Task A (abuse detection) wherein we achieve a score within two points of the top performing team and in Sub-Task B (target detection) wherein we are ranked 4 of the 44 participating teams.
Scrum Essentials Cards

This article presents a series of examples and case studies on how people have used the Scrum Essentials cards to benefit their teams and improve how they work. Scrum is one of the most popular agile frameworks used successfully all over the world. It has been taught and used for 15-plus years. It is by far the most-used practice when developing software, and it has been generalized to be applicable for not just software but all kinds of products. It has been taught to millions of developers, all based on the Scrum Guide. Today all our Scrum training is accompanied by a set of cards designed using the Essence standard, which has given us a better Scrum.
Single-Shot Non-Gaussian Measurements for Optical Phase Estimation

Estimation of the properties of a physical system with minimal uncertainty is a central task in quantum metrology. Optical phase estimation is at the center of many metrological tasks where the value of a physical parameter is mapped to the phase of an electromagnetic field and single-shot measurements of this phase are necessary. While there are measurements able to estimate the phase of light in a single shot with small uncertainties, demonstrations of near-optimal single-shot measurements for an unknown phase of a coherent state remain elusive. Here, we propose and demonstrate strategies for single-shot measurements for ab initio phase estimation of coherent states that surpass the sensitivity limit of heterodyne measurement and approach the Cramer-Rao lower bound for coherent states. These single-shot estimation strategies are based on real-time optimization of coherent displacement operations, single photon counting with photon number resolution, and fast feedback. We show that our demonstration of these optimized estimation strategies surpasses the heterodyne limit for a wide range of optical powers without correcting for detection efficiency with a moderate number of adaptive measurement steps. This is, to our knowledge, the most sensitive single-shot measurement of an unknown phase encoded in optical coherent states.
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries

Pretrained language models have been suggested as a possible alternative or complement to structured knowledge bases. However, this emerging LM-as-KB paradigm has so far only been considered in a very limited setting, which only allows handling 21k entities whose single-token name is found in common LM vocabularies. Furthermore, the main benefit of this paradigm, namely querying the KB using a variety of natural language paraphrases, is underexplored so far. Here, we formulate two basic requirements for treating LMs as KBs: (i) the ability to store a large number facts involving a large number of entities and (ii) the ability to query stored facts. We explore three entity representations that allow LMs to represent millions of entities and present a detailed case study on paraphrased querying of world knowledge in LMs, thereby providing a proof-of-concept that language models can indeed serve as knowledge bases.
Content Analysis of Library Websites of Select Colleges of Delhi University

The paper examines the accessibility, accuracy, currency and user-friendliness in ten selected library websites&#x0D; of the colleges affiliated to the University of Delhi. It further, discusses services and facilities being offered by the websites. The data were collected through a well-structured checklist from the selected colleges under study. The study reveals that maximum number of college libraries have mentioned information related to introduction 9(90 %), library staff 8(80 %), library hours 6(60 %) and membership 6(60 %) on their websites. However, study also report that none of the library websites/web-pages have features of social networking tools, feedbacks, regular updates and they also lack in providing question papers, news-clippings, user manual and single window search. The study findings reveals that Deshbandhu College library scored thirty eight (38) out of43 which is highest (ranked 1st) whereas Ramjas College scored only five (05) out of 43 and stands last. The study suggests, for carrying out such evaluative studies which is the need of hour to enable the institutions to update their websites periodically and come up with flying colours on user’s expectations
Wigner negativity in spin- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>j</mml:mi></mml:math> systems

The nonclassicality of simple spin systems as measured by Wigner negativity is studied on a spherical phase space. Several SU(2)-covariant states with common qubit representations are addressed: spin coherent, spin cat (GHZ/N00N), and Dicke ($\textsf{W}$). We derive a bound on the Wigner negativity of spin cat states that rapidly approaches the true value as spin increases beyond $j \gtrsim 5$. We find that spin cat states are not significantly Wigner-negative relative to their Dicke state counterparts of equal dimension. We also find, in contrast to several entanglement measures, that the most Wigner-negative Dicke basis element is spin-dependent, and not the equatorial state $| j,0 \rangle$ (or $|j,\pm 1/2 \rangle$ for half-integer spins). These results underscore the influence that dynamical symmetry has on nonclassicality, and suggest a guiding perspective for finding novel quantum computational applications.
Multivariate statistical analyses of rare earth element compositions of spring waters from the Arima and Kii areas, Southwest Japan

Multivariate statistical analyses have been applied to a compositional data set for spring waters from the Arima and Kii areas in southwest Japan, for understanding sources and processes that produced the deep-seated Arima-type brine and the related spring waters in the areas. The data set consists of 14 rare earth elements (REEs), six major solute species (HCO3, Cl, Na, Ca, K, Mg) and isotopic ratios of oxygen, hydrogen and helium (δD, δ18O, 3He/4He) from 19 sites. Independent Component Analysis (ICA) and k-means cluster analysis on the whitened data (KCA) are useful to extract independent features hidden in such a high-dimensional geochemical data set. As a result of ICA and KCA, four ICs and eight clusters were found to sufficiently and effectively describe the observed variability. Comparison of the results with δD, δ18O and 3He/4He provides geochemical interpretations on the extracted four ICs as follows: IC1 represents the overall REE concentration level; IC2 measures the amplitude of Eu anomaly inherited from local basement rocks/minerals; IC3 reflects the amplitude of (reversed) W-shaped pattern, which developed on mineral precipitation event(s) when the deep brine mixes with meteoric water in a relatively shallow aquifer under oxidative conditions, and IC4 measures the amplitude of Ce anomaly possibly by precipitation or mechanical filtering of CeO2. Combination of the four ICs may create a complex and various REE pattern to explain the actual data. Although these geochemical features with Eu, Ce and W-shaped patterns have been broadly suggested to be important for understanding origin of the spring waters in the areas, this is the first time to demonstrate that the above features represent the independent processes and to compare them systematically with the regional compilation of the basement rock compositions. In addition, several clusters were found only in either the Arima area or the Kii area, which reflects broad geographical provenance of IC3 and IC4. These spatial variations can be attributed to compositional variations of the original deep brine, which possibly varies with slab depth and temperature to control the solute concentrations and subsequent geochemical evolution to produce different types of spring waters.
Property-Based Testing for Parameter Learning of Probabilistic Graphical Models

Code quality is a requirement for successful and sustainable software development. The emergence of Artificial Intelligence and data driven Machine Learning in current applications makes customized solutions for both data as well as code quality a requirement. The diversity and the stochastic nature of Machine Learning algorithms require different test methods, each of which is suitable for a particular method. Conventional unit tests in test-automation environments provide the common, well-studied approach to tackle code quality issues, but Machine Learning applications pose new challenges and have different requirements, mostly as far the numerical computations are concerned. In this research work, a concrete use of property-based testing for quality assurance in the parameter learning algorithm of a probabilistic graphical model is described. The necessity and effectiveness of this method in comparison to unit tests is analyzed with concrete code examples for enhanced retraceability and interpretability, thus highly relevant for what is called explainable AI.
Adaptive and multiple time-scale eligibility traces for online deep reinforcement learning

Deep reinforcement learning (DRL) is one promising approach to teaching robots to perform complex tasks. Because methods that directly reuse the stored experience data cannot follow the change of the environment in robotic problems with a time-varying environment, online DRL is required. The eligibility traces method is well known as an online learning technique for improving sample efficiency in traditional reinforcement learning with linear regressors rather than DRL. The dependency between parameters of deep neural networks would destroy the eligibility traces, which is why they are not integrated with DRL. Although replacing the gradient with the most influential one rather than accumulating the gradients as the eligibility traces can alleviate this problem, the replacing operation reduces the number of reuses of previous experiences. To address these issues, this study proposes a new eligibility traces method that can be used even in DRL while maintaining high sample efficiency. When the accumulated gradients differ from those computed using the latest parameters, the proposed method takes into account the divergence between the past and latest parameters to adaptively decay the eligibility traces. Bregman divergences between outputs computed by the past and latest parameters are exploited due to the infeasible computational cost of the divergence between the past and latest parameters. In addition, a generalized method with multiple time-scale traces is designed for the first time. This design allows for the replacement of the most influential adaptively accumulated (decayed) eligibility traces. The proposed method outperformed conventional methods in terms of learning speed and task quality by the learned policy on benchmark tasks on a dynamic robotic simulator. A real-robot demonstration confirmed the significance of online DRL as well as the adaptability of the proposed method to a changing environment.
Modelling of Photovoltaic Modules Optical Losses Due to Saharan Dust Deposition in Dakar, Senegal, West Africa

This study aims to evaluate the optical losses of photovoltaic modules due to Saharan dust deposition in Dakar, Senegal, West Africa.For this purpose, an air-dust-glass system is modeled to simulate optical losses in transmittance and reflectance.To do this, we have collected dust samples from Photo-Voltaic (PV) surface in Dakar area (14˚42'N latitude, 17˚28'W longitude), Senegal.X-ray fluorescence reveals that silicon (Si), iron (Fe), calcium (Ca) and potassium (K) mainly composed these dust samples.Then, dust refractive indices obtained from an ellipsometer were used as an input to be used in the model.Simulations show that for radiation (at normal incidence) arriving on a dust layer of 30 μm-thick (corresponding to a dust deposit of 1.63 g/m 2 ), 79% of the visible spectrum is transmitted; 19% is reflected and 2% is absorbed.Overall, the transmittance decreases by more than 50% as of dust layer of 70 μm-thick corresponding to a dust deposit of 3.3 g/m 2 .
Modeling and Analysis of Residential Electricity Consumption Statistics: A Tracy-Widom Mixture Density Approximation

Residential electricity loads highly fluctuate which makes it challenging to predict and estimate changes and anomalies. This article studies the statistical distribution of the maximum load of residential settlements and proposes a new model that is more reliable than conventional methods to predict extreme events in residential electricity consumption. Specially, a multimodal Tracy-Widom distribution is proposed to characterize the maximum residential electricity consumption data. We also propose a numerical method to approximate the density function of the multimodal Tracy-Widom distribution as opposed to the conventional approach in the literature, where a multimodal normal distribution is utilized. A simulated electricity consumption data set is used to test and validate the proposed methods. The results demonstrate that the multimodal Tracy-Widom distribution is more accurate than conventional methods in modeling residential electricity consumption data. In addition, the numerical results show that residential electricity consumption behavior is determined by certain socioeconomic factors for which correlations with household electricity consumption exist.
Reconstruction of a Riemannian Manifold from Noisy Intrinsic Distances

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 13 June 2019Accepted: 12 May 2020Published online: 02 September 2020Keywordsinverse problems, manifold learning, geometric Whitney problemAMS Subject Headings60D05, 53C21, 35R30Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
Penerapan Data Mining Menggunakan Algoritma Apriori Terhadap Data Transaksi Sebagai Pendukung Informasi Strategi Penjualan

Retail business in West Java province ranks fourth most.This indicates that there is an increase in retail business competition followed by an increase in information technology implementation.The problem of stacking stocks that could harm retail store entrepreneurs is quite common; to overcome such a precise sales strategy is indispensable.In determining the right sales strategy requires the availability of useful data and information.In order to be more efficient data of sales transaction can be processed by applying data mining technique.The method applied by researchers to design the program is the approach of knowledge discovery in the database, including data analysis to the determination of the a-priori algorithm.The purpose of this research is to obtain information about the relation between products that support the sales strategy of the sales transaction data.The linkage information between the products can later be used to support the decision of the retail business sales strategy by adjusting the product to be packaged, product offerings to the buyer and the placement of the product.The results showed that the priori algorithm can be used to process the data of sales transactions into new information in relation between products based on testing with Orange tools.The associative rules established are tested using the lift ratio, so it is important to know the association rules among the most powerful products.
Saliency Guided Visual Tracking via Correlation Filter With Log-Gabor Filter

Correlation filter (CF) based tracking algorithms have tremendously contributed to the field of visual tracking due to the high computational efficiency and competitive performance. Nonetheless, most CF-based trackers are vulnerable to the influence of occlusion and boundary effect, which results in suboptimal performance. In this article, we propose saliency guided visual tracking via correlation filter with log-Gabor filter to robustify its performance under occlusion and boundary effect challenges. Firstly, we propose the CF with log-Gabor filter to get a robust appearance model. The log-Gabor filter is adopted to preprocess the sequence to gain the log-Gabor feature, which provides important cues for tracking since it encodes the texture information. Secondly, considering the prior information, we embed the novel saliency guided adaptive spatial feature selection to filter learning to preserve the spatial structure in the lower manifold and mitigate boundary distortion. Thirdly, the occlusion estimating strategy, performing on-line evaluation of tracking, triggers the motion estimation module to optimize the optimal location. Experiments on benchmark databases demonstrate the enhanced discrimination and interpretability of the proposed tracker and its superiority over other trackers.
Development of Cyber Resilient Capability Maturity Model for Cloud Computing Services

The research objectives were: 1) to develop cyber resilient model, 2) to develop the cyber resilient capability maturity model and 3) to develop self-assessment model for cyber resilient capability of cloud computing services which are qualitative and applicative research. Referring to the cybersecurity concept from National Institute of Standards and Technology (NIST) from the in-depth interview, focusgroup discussion was developed with cybersecurity experts and data collection from cloud services providers. It was found that trend of cyber-attacks was violent with smarter method. The authors had synthesized the concept of cyber resilient capability maturity model for cloud computing services including developed application for cloud services providers to evaluate their organization in order to improve the better cybersecurity level in cloud computing services and the cyber resilient capability maturity model in the future.
Visual exploratory behavior and its development

Visual exploratory behavior refers to actively gathering visual information through coordinated eye, head, and body movements—looking around with a purpose. In this article, I compare the study of visual exploration using stationary, screen-based tasks vs mobile, naturalistic tasks. In doing so, I discuss how ecological validity depends on variations in three factors: visual characteristics of stimuli, the opportunity for participants to interact with their surroundings, and participants’ ability to move their bodies. In particular, the long-standing reliance on stationary, screen tasks has precluded progress in understanding the last factor—how anatomical, biomechanical, and physiological aspects of the body influence where observers look. I argue that each factor is vital to revealing the flexibility, adaptiveness, and efficiency of everyday visual exploration.
Checking <scp>ID</scp>‐cards for the sale of restricted goods: Age decisions bias face decisions

Summary Matching unfamiliar faces is highly error‐prone, and most studies highlight the implications for real‐world ID‐checking. Here we study a particular instance of ID‐checking: proof of age for buying restricted goods such as alcohol. In this case, checkers must establish that an identity document is carried by its legitimate owner (i.e., that the ID photo matches the face of the bearer) and that the ID proves the bearer to be old enough to make the purchase. Across three experiments, using two common forms of photo‐ID (i.e., driving licenses, PASS+ cards) we show that observers produce very high error rates when age requirements are met, but faces mismatch. This bias away from detecting a face mismatch remained evident in experienced cashiers—though to a somewhat attenuated level. We discuss interactions between face matching and other tasks, and the practical consequences of a bias which favours those using photo‐ID with fraudulent intent.
On robustness of camera identification algorithms

Abstract In this paper we consider the problem of a privacy threat enabling tracing digital cameras by the analysis of pictures they produced. As thousands of images are processed at a mass scale, the threat may apply to most users of digital cameras. We consider a state-of-the-art algorithm for digital camera identification proposed in Lucas et al. (IEEE Trans Inf Forensics Secur 1(2):205–214, 2006) and discuss strategies that can be used to bypass it, in order to make information about the camera unavailable. It turns out that many natural strategies like Gaussian blur, adding artificial noise or removing pixels’ least significant bit from the image does not prevent the identification of a camera unless a huge loss of image details is suffered. On the other hand, we show a method to bypass the camera identification with a just marginally more complex, yet not intuitive, method namely cropping the image on the edges and resizing to the original size using Lanczos resampling.
Overcoming the Challenges – the Impact of COVID-19 on Agricultural Higher Education in Ukraine

Due to the COVID-19 outbreak, higher education institutions all over the world have been faced with the urgent need for going online. Still, higher education institutions in Ukraine have met this new challenge with significant gaps in online education expertise. In this paper, we provide a brief overview of the major challenges that have become relevant to Ukrainian agricultural universities in the pandemic period. In particular, we tried to evaluate the main areas where the major interventions have been carried out, such as providing access to relevant ICT infrastructure, methodological support and training of academic staff etc. The results of the case study support the conclusion that the rapid shift to remote learning has the potential to be the impetus for radical decisions in the context of implementation of pedagogical innovations and digital tools in agricultural higher education.