Reviewerly/exHarmony huggingface dataset:
Pruned Simulation-Based Optimal Sailboat Path Search Using Micro HPC Systems

Simulation-based optimal path search algorithms are often solved using dynamic programming, which is typically computationally expensive. This can be an issue in a number of cases including near-real-time autonomous robot or sailboat path planners. We show the solution to this problem which is both effective and (energy) efficient. Its three key elements – an accurate and efficient estimator of the performance measure, two-level pruning (which augments the estimator-based search space reduction with smart simulation and estimation techniques), and an OpenCL-based spmd-parallelisation of the algorithm – are presented in detail. The included numerical results show the high accuracy of the estimator (the medians of relative estimation errors smaller than 0.003), the high efficacy of the two-level pruning (search space and computing time reduction from seventeen to twenty times), and the high parallel speedup (its maximum observed value was almost 40). Combining these effects gives (up to) 782 times faster execution. The proposed approach can be applied to various domains. It can be considered as an optimal path planing framework parametrised by a problem specific performance measure heuristic/estimator.
iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic

This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021.This task is accomplished using a variety of techniques.We used state-of-theart contextualized text representation models that were fine-tuned for the down-stream task in hand.ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used.According to the results, BERT-basearabic had the highest 0.748 F1 score on the test set.
Functions of Code-Switching in a Private Chat on Facebook

Code switching is a significant language feature of the multilingual countries like Pakistan. The term code switching refers to the shift from one language to another. The main objective of this research is to explore the communicative functions of code switching by multilingual Sindhi speaking wife and husband in an inbox chat on Facebook. Applying the qualitative methodology, the data of one year chat was collected and varieties of functions using code switching theory of Blom and Gumperz (1972) as the theoretical framework. The findings of the study elucidate that Sindhi educated multilingual heavily utilize code switching for various communicative purposes like indexing identity, quotation, rephrasing, selfcorrection, metalinguistic, reported speech, idiomatic expressions, translation, expressing anger, humour, and euphemistic expressions in order to achieve their communicative goals.
UETrice at MEDIQA 2021: A Prosper-thy-neighbour Extractive Multi-document Summarization Model

This paper describes a system developed to summarize multiple answers challenge in the MEDIQA 2021 shared task collocated with the BioNLP 2021 Workshop.We propose an extractive summarization architecture based on several scores and state-of-the-art techniques.We also present our novel prosperthy-neighbour (PtN) strategies to improve performance.Our model has been proven to be effective with the best ROUGE-1/ROUGE-L scores, being the shared task runner-up by ROUGE-2 F 1 score (over 13 participated teams).
Financial Accounting in the Era of Blockchain - A Paradigm Shift from Double Entry to Triple Entry System

This paper aims to conceptualize the impact of blockchain technology on the financial accounting from technical and non-technical perspectives. It further investigates the way blockchain can improve the quality of accounting data. By reviewing the published research papers. this study observes that blockchain technology can create a platform for the organizations to disclose their information voluntarily in the short-run while it decreases errors in financial disclosure, enhances the quality of accounting information and reduce information redundancy in the long-run. A harmonized movement of accountants, auditors, regulatory authority and other relevant parties can extract the optimum benefits of blockchain technology in accounting ecosystem. This study indicates necessary way outs to improve the quality of accounting information by applying blockchain technology. It further identifies potential threats in implementing blockchain technology and recommends appropriate remedies. It is expected that, the findings of this study will encourage top management and policy makers to introduce blockchain technology in their business for log-term sustainability as well as to increase the quality of data and acceptability of their financial reports.
Ethical and technical challenges of AI in tackling hate speech

In this paper, we discuss some of the ethical and technical challenges of using Artificial Intelligence for online content moderation. As a case study, we used an AI model developed to detect hate speech on social networks, a concept for which varying definitions are given in the scientific literature and consensus is lacking. We argue that while AI can play a central role in dealing with information overload on social media, it could cause risks of violating freedom of expression (if the project is not well conducted). We present some ethical and technical challenges involved in the entire pipeline of an AI project - from data collection to model evaluation - that hinder the large-scale use of hate speech detection algorithms. Finally, we argue that AI can assist with the detection of hate speech in social media, provided that the final judgment about the content has to be made through a process with human involvement.
A Distributed Sailfish Optimizer Based on Multi-Agent Systems for Solving Non-Convex and Scalable Optimization Problems Implemented on GPU

The SailFish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing high convergence speed. Nowadays, multi agent systems and metaheuristic algorithms can provide high performance solutions for solving combinatorial optimization problems. These methods provide a prominent approach to reduce the execution time and improve of the solution quality. In this paper, we elaborate a multi agent based and distributed method for sailfish optimizer (DSFO), which improves the execution time and speedup of the algorithm while maintaining the results of optimization in high quality. The Graphics Processing Units (GPUs) using Compute Uniﬁed Device Architecture (CUDA) are used for the massive computation requirements in this approach. In depth of the study, we present the implementation details and performance observations of DSFO algorithm. Also, a comparative study of distributed and sequential SFO is performed on a set of standard benchmark optimization functions. Moreover, the execution time of distributed SFO is compared with other parallel algorithms to show the speed of the proposed algorithm for solving unconstrained optimization problems. The final results indicate that the proposed method is executed about maximum 14 times faster than other parallel algorithms and shows the ability of DSFO for solving non-separable, non-convex and scalable optimization problems.
ENTRUST: Argument Reframing with Language Models and Entailment

Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman, 1983).Differences in lexical framing, the focus of our work, can have large effects on peoples' opinions and beliefs.To make progress towards reframing arguments for positive effects, we create a dataset and method for this task.We use a lexical resource for connotations to create a parallel corpus and propose a method for argument reframing that combines controllable text generation (positive connotation) with a postdecoding entailment component (same denotation).Our results show that our method is effective compared to strong baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear.
From Behavioral Design to Reverence for Life: Care Policies for the Digital Environment

The article raises the problem of the ecology of a human being in a digital reality, namely, the issue of caring for a resource accessible to any person living in new conditions. The aim of the study is to substantiate the need for a transition from the behavioral design as well as the corresponding ethological paradigm to the existential design, involving a more responsible and careful approach to ecology and ethics in human capital management in the context of cognitive capitalism and digitalization of life. In the modern world it is often the question of productivity growth and technological advancement. Meanwhile, the question of human capacities and incapacities (mental, physical, behavioral) does not arise. Taking into account the absence of a caring attitude towards human capacities, this resource will never go turn into human capital. The research demonstrates that the design of digital media allows the use of the available human resources more properly if technical requirements of speed, quantity, simplicity are changed in compliance with existential requirements (changes in the subject of herself/himself, not in the objects of her/his activities) increasing his/her role, ethical meaning, feeling of presence in the event, tracking the results of actions and a deeper awareness of the results. The article is aimed at media philosophers, anthropologists and theorists of digital culture.
Cross Entropy Optimization of Constrained Problem Hamiltonians for Quantum Annealing

This paper proposes a Cross Entropy approach to shape constrained Hamiltonians by optimizing their energy penalty values. The results show a significantly improved solution quality when run on D-Wave’s quantum annealing hardware and the numerical computation of the eigenspectrum reveals that the solution quality is correlated with a larger minimum spectral gap. The experiments were conducted based on the Knapsack-, Minimum Exact Cover- and Set Packing Problem. For all three constrained optimization problems we could show a remarkably better solution quality compared to the conventional approach, where the energy penalty values have to be guessed.
Video Frame Interpolation Based on Deformable Kernel Region

Video frame interpolation task has recently become more and more prevalent in the computer vision field. At present, a number of researches based on deep learning have achieved great success. Most of them are either based on optical flow information, or interpolation kernel, or a combination of these two methods. However, these methods have ignored that there are grid restrictions on the position of kernel region during synthesizing each target pixel. These limitations result in that they cannot well adapt to the irregularity of object shape and uncertainty of motion, which may lead to irrelevant reference pixels used for interpolation. In order to solve this problem, we revisit the deformable convolution for video interpolation, which can break the fixed grid restrictions on the kernel region, making the distribution of reference points more suitable for the shape of the object, and thus warp a more accurate interpolation frame. Experiments are conducted on four datasets to demonstrate the superior performance of the proposed model in comparison to the state-of-the-art alternatives.
Hybrid model for evaluation of quality aware DevOps

Today most of the organizations are switching to DevOps for faster and reliable delivery. It promotes the collaboration between developers and IT teams. In ancient development practices, development and operation teams were working in silos and when DevOps introduced, it combines both teams to integrate and automate the processes. A single team with cross functioning members provides not only technical advantages but also cultural benefits. Faster delivery of software, less complex designs, stable operating environments, customer satisfaction is some of the outcomes of DevOps. Although it proved to be a responsive environment for software delivery yet lacks in quantifiable perspective. There is no metric defined to measure performance and that can be estimated by using key attributes of software. In this research hybrid framework is proposed to improve the software reliability and productivity. Proposed framework for DevOps is named TDMBD (Test Driven Measurement Based DevOps) which provides solutions to challenges in DevOps like performance issues, poorly defined methodologies, and unstandardized processes. Paper focuses on defining and measuring metrics that are derived from measurement-based system of software and TDMBD is evaluated based on metrics analysis. To validate results of proposed approach a comparison is shown in between existing approach and proposed approach. Finally, through proposed method better quality of product is retrieved.
Study of Atmospheric Turbidity in a Northern Tropical Region Using Models and Measurements of Global Solar Radiation

Radiative transfer in the Earth’s atmosphere under clear-sky conditions strongly depends on turbidity due to aerosols and hydrometeors. It is therefore important to know its temporal radiative properties for a given site when the objective is to optimize the solar energy that is collected there. Turbidity can be studied via measurements and models of the global solar radiation reaching the ground in cloudless conditions. These models generally depend on two parameters, namely the Angström turbidity coefficient and the Linke factor. This article aims to do a comparative study of five models of global solar radiation, all dependent on the Linke factor, based on real data. The measurements are provided by the Tamanrasset Meteorological Center (Algeria), which has a long series of global solar radiation data recorded between 2005 and 2011. Additional data from AERONET and MODIS onboard the TERRA satellite were also used to perform the comparison between the two estimated parameters and those obtained from AERONET. The study shows that the ESRA models are the most reliable among the five models for estimating the Linke factor with a correlation coefficient R of the data fits of 0.9995, a RMSE of 13.44 W/m2, a MBE of −0.64 W/m2 and a MAPE of 6.44%. The maximum and minimum statistical values were reached, respectively, in June and during the autumn months. The best correlation is also observed in the case of ESRA models between the Linke parameter and the joint optical thickness of aerosols and the total column-integrated water vapor. The Angström turbidity coefficient β, calculated from the Linke factor and MODIS data, has values less than 0.02 at 9% of the cases, and 76% present values ranging between 0.02 and 0.15 and 13% higher than 0.15. These β values are validated by AERONET measurements since a very good correlation (R≈0.87) is observed between the two datasets. The temporal variations of β also show a maximum in June. Satellite observations confirm more aerosols during the summer season, which are mostly related to the African monsoon.
Automatic Speech-Based Checklist for Medical Simulations

Medical simulators provide a controlled environment for training and assessing clinical skills.However, as an assessment platform, it requires the presence of an experienced examiner to provide performance feedback, commonly preformed using a task specific checklist.This makes the assessment process inefficient and expensive.Furthermore, this evaluation method does not provide medical practitioners the opportunity for independent training.Ideally, the process of filling the checklist should be done by a fully-aware objective system, capable of recognizing and monitoring the clinical performance.To this end, we have developed an autonomous and a fully automatic speech-based checklist system, capable of objectively identifying and validating anesthesia residents' actions in a simulation environment.Based on the analyzed results, our system is capable of recognizing most of the tasks in the checklist: F 1 score of 0.77 for all of the tasks, and F 1 score of 0.79 for the verbal tasks.Developing an audio-based system will improve the experience of a wide range of simulation platforms.Furthermore, in the future, this approach may be implemented in the operation room and emergency room.This could facilitate the development of automatic assistive technologies for these domains.
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models. Resampling is a key ingredient of PF, necessary to obtain low variance likelihood and states estimates. However, traditional resampling methods result in PF-based loss functions being non-differentiable with respect to model and PF parameters. In a variational inference context, resampling also yields high variance gradient estimates of the PF-based evidence lower bound. By leveraging optimal transport ideas, we introduce a principled differentiable particle filter and provide convergence results. We demonstrate this novel method on a variety of applications.
Enhanced Security Feature of ATM's Through Facial Recognition

Automated Teller Machines also known as ATM's are widely used nowadays by each and everyone. The ATM machine (Automated Teller Machine) is an electronic device that is used by the banks to perform banking tasks like withdrawal of money, transferring of money, and many to get many information about a user's bank account without the need to visit a bank. This System revolutionised the way of transactions. There were no long lines of queue in front of the bank for a simple withdrawal of money. The number of ATM's a bank has can be a factor in considering the strength of a bank. As there is increase in the number of ATM's, there is also increase in the fraudulent activities in the ATM. The main motivation of this project is to increase the security feature of the use of ATM. The current method uses static key (PIN) for security. The proposed method uses Face-id as a key incorporated with current method. The advantages can be found as that the face-id is unique for everybody; it cannot be used by anybody other than the user. For the implementation of the face-id scan, the machine learning and image processing algorithms (Eigenface algorithm) are used.
Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents

Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron.However, these cannot exploit the representation learning ability of neural networks, which would make supervised clustering even more powerful, i.e., general clustering patterns can be learned automatically.In this paper, we design neural networks based on latent structured prediction loss and Transformer models to approach supervised clustering.We tested our methods on the task of automatically recreating categories of intents from publicly available question intent corpora.The results show that our approach delivers 95.65% of F1, outperforming the state of the art by 17.24%.
Optimization of Reversible Circuits Using Toffoli Decompositions with Negative Controls

The synthesis and optimization of quantum circuits are essential for the construction of quantum computers. This paper proposes two methods to reduce the quantum cost of 3-bit reversible circuits. The first method utilizes basic building blocks of gate pairs using different Toffoli decompositions. These gate pairs are used to reconstruct the quantum circuits where further optimization rules will be applied to synthesize the optimized circuit. The second method suggests using a new universal library, which provides better quantum cost when compared with previous work in both cost015 and cost115 metrics; this proposed new universal library “Negative NCT” uses gates that operate on the target qubit only when the control qubit’s state is zero. A combination of the proposed basic building blocks of pairs of gates and the proposed Negative NCT library is used in this work for synthesis and optimization, where the Negative NCT library showed better quantum cost after optimization compared with the NCT library despite having the same circuit size. The reversible circuits over three bits form a permutation group of size 40,320 (23!), which is a subset of the symmetric group, where the NCT library is considered as the generators of the permutation group.
Adapting Coreference Resolution for Processing Violent Death Narratives

Coreference resolution is an important component in analyzing narrative text from administrative data (e.g., clinical or police sources).However, existing coreference models trained on general language corpora suffer from poor transferability due to domain gaps, especially when they are applied to gender-inclusive data with lesbian, gay, bisexual, and transgender (LGBT) individuals.In this paper, we analyzed the challenges of coreference resolution in an exemplary form of administrative text written in English: violent death narratives from the USA's Centers for Disease Control's (CDC) National Violent Death Reporting System.We developed a set of data augmentation rules to improve model performance using a probabilistic data programming framework.Experiments on narratives from an administrative database, as well as existing gender-inclusive coreference datasets, demonstrate the effectiveness of data augmentation in training coreference models that can better handle text data about LGBT individuals.
Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model

Evaluating the quality of responses generated by open-domain conversation systems is a challenging task.This is partly because there can be multiple appropriate responses to a given dialogue history.Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment.To address this problem, researchers have investigated the possibility of assessing response quality without using a set of known correct responses.Tao et al. (2018) demonstrated that an automatic response evaluation model could be made using unsupervised learning for the next-utterance prediction (NUP) task.For unsupervised learning of such a model, we propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response.We find, from our experiments on English datasets, that using the negative samples generated by our method alongside random negative samples can increase the model's correlation with human evaluations.The process of generating such negative samples is automated and does not rely on human annotation.
On Biasing Transformer Attention Towards Monotonicity

Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior via specialized attention functions or pretraining.In this work, we introduce a monotonicity loss function that is compatible with standard attention mechanisms and test it on several sequence-to-sequence tasks: grapheme-to-phoneme conversion, morphological inflection, transliteration, and dialect normalization.Experiments show that we can achieve largely monotonic behavior.Performance is mixed, with larger gains on top of RNN baselines.General monotonicity does not benefit transformer multihead attention, however, we see isolated improvements when only a subset of heads is biased towards monotonic behavior.