Reviewerly/exHarmony huggingface dataset:
Superposition for Full Higher-order Logic

Abstract We recently designed two calculi as stepping stones towards superposition for full higher-order logic: Boolean-free $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math> -superposition and superposition for first-order logic with interpreted Booleans. Stepping on these stones, we finally reach a sound and refutationally complete calculus for higher-order logic with polymorphism, extensionality, Hilbert choice, and Henkin semantics. In addition to the complexity of combining the calculus’s two predecessors, new challenges arise from the interplay between $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math> -terms and Booleans. Our implementation in Zipperposition outperforms all other higher-order theorem provers and is on a par with an earlier, pragmatic prototype of Booleans in Zipperposition.
Blockchain-Based Solution for Detecting and Preventing Fake Check Scams

Fake check scam is one of the most common attacks used to commit fraud against consumers. This fraud is particularly costly for victims because they generally lose thousands of dollars as well as being exposed to judicial proceedings. Currently, there is no existing solution to authenticate checks and detect fake ones instantly. Instead, banks must wait for a period of more than 48 h to detect the scam. In this context, we propose a blockchain-based scheme to authenticate checks and detect fake check scams. Moreover, our approach allows the revocation of used checks. More precisely, our approach helps the banks to share information about provided checks and used ones, without exposing the banks’ customers’ personal data. We demonstrate a proof of concept of our proposed approach using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Namecoin</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hyperledger</i> blockchain technologies.
Reconstructing porous media using generative flow networks

One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize porous media volumes using a so-called generative flow model trained on x-ray microscope images of rock texture and pore structure. These models are optimized on a log-likelihood objective and they synthesize large and realistic three-dimensional images. We demonstrate the rapid generation of sandstone image volumes that display realism as gauged by quantitative comparison of topological features using Minkowski functionals of porosity, specific surface area, and the Euler–Poincaré characteristic (i.e., pore connectivity). We also evaluate the single-phase permeability using Navier–Stokes and lattice Boltzmann methods and show that transport properties of the generated samples match measured trends.
The Application of NLTK Library for Python Natural Language Processing in Corpus Research

Corpora play an important role in linguistics research and foreign language teaching. At present, the relevant research on the corpus in China mainly uses WordSmith, Antconc and other retrieval tools. NLTK library, which is based on Python language, can provide more flexible and rich research methods, and it can use unified data standards to avoid the trouble of various data type conversion. At the same time, with the help of Python’s numerous third-party libraries, it can make up for the shortcomings of other tools in syntax analysis, graphic rendering, regular expression retrieval and other aspects. In terms of the main links in corpus research, such as text cleaning, word form restoration, part of speech tagging and text retrieval statistics, this paper takes the US presidential inaugural speech in the corpus as an example to show how to use this tool to process the language data, and introduces the application of Python NLTK library in corpus research.
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking

Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and testing different optimization algorithms.Among other issues, this practice promotes overfitting and poor-performing user guidelines.To address this shortcoming, we propose in this work a benchmark suite, OptimSuite, which covers a broad range of black-box optimization problems, ranging from academic benchmarks to real-world applications, from discrete over numerical to mixed-integer problems, from small to very large-scale problems, from noisy over dynamic to static problems, etc.We demonstrate the advantages of such a broad collection by deriving from it Automated Black Box Optimizer (ABBO), a general-purpose algorithm selection wizard.Using three different types of algorithm selection techniques, ABBO achieves competitive performance on all benchmark suites.It significantly outperforms previous state of the art on some of them, including YABBOB and LSGO.ABBO relies on many high-quality base components.Its excellent performance is obtained without any task-specific parametrization.The OptimSuite benchmark collection, the ABBO wizard and its base solvers have all been merged into the open-source Nevergrad platform, where they are available for reproducible research.
A knowledge graph based question answering method for medical domain

Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc . In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes too much human labor. To address the problems, in this paper, we propose an approach of knowledge graph based question answering (KGQA) method for medical domain, which firstly constructs a medical knowledge graph by extracting named entities and relations between the entities from medical documents. Then, in order to understand a question, it extracts the key information in the question according to the named entities, and meanwhile, it recognizes the questions’ intentions by adopting information gain. The next an inference method based on weighted path ranking on the knowledge graph is proposed to score the related entities according to the key information and intention of a given question. Finally, it extracts the inferred candidate entities to construct answers. Our approach can understand questions, connect the questions to the knowledge graph and inference the answers on the knowledge graph. Theoretical analysis and real-life experimental results show the efficiency of our approach.
Analysis on Health Information Acquisition of Social Network Users by Opinion Mining: Case Analysis Based on the Discussion on COVID-19 Vaccinations

This study aims to explore phenomena and laws that occur when different users on social network platforms obtain health information by constructing an opinion mining model, analyzing the user's position on selected cases, and exploring the reflection of the phenomenon of truth decay on platforms. It selects group posts regarding the COVID-19 vaccination dispute on the Douban platform, analyzes the positions of different users, and explores phenomena related to users obtaining health information on domestic social platforms according to different topics and information behaviors. The results reveal a linear relationship between the negative and neutral attitudes of netizens on social networking platforms. Moreover, netizens tend to hold subjective language when expressing their views and attitudes, and their views on social platforms will not change easily. The study explores the health information acquisition behavior of netizens on social platforms based on the constructed user opinion mining model. The study is helpful for relevant units and platforms to make scientific decisions and provide guidance according to different positions of Internet users.
Frequency Effects on Syntactic Rule Learning in Transformers

Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract symbols and rules.We investigate this question using the case study of BERT's performance on English subject-verb agreement.Unlike prior work, we train multiple instances of BERT from scratch, allowing us to perform a series of controlled interventions at pre-training time.We show that BERT often generalizes well to subject-verb pairs that never occurred in training, suggesting a degree of rule-governed behavior.We also find, however, that performance is heavily influenced by word frequency, with experiments showing that both the absolute frequency of a verb form, as well as the frequency relative to the alternate inflection, are causally implicated in the predictions BERT makes at inference time.Closer analysis of these frequency effects reveals that BERT's behavior is consistent with a system that correctly applies the SVA rule in general but struggles to overcome strong training priors and to estimate agreement features (singular vs. plural) on infrequent lexical items.
An Analysis of ResNet50 Model and RMSprop Optimizer for Education Platform Using an Intelligent Chatbot System

A chatbot is a software (or machine) that has the ability to talks with a user: it is a virtual assistant that can answer a number of user questions, and providing the correct responses. In the last few years, the use of chatbots is very popular in various fields, such as health care, marketing, educational, supporting systems, cultural heritage, entertainment, and many others. This paper proposes an intelligent chatbot system that can give a response in the form of natural language or audio to a natural language question or image input in different domains of education and will support multiple languages (English, French, and Arabic). To realize this System, we used different deep learning architectures (CNN, LSTM, Transformers), computer vision, transfer learning to extract image features vector, and natural language processing techniques. In the end, after the implementation of the proposed model, a comparative study was conducted in order to prove the performance of this system using image-response model and question-response model using accuracy and BLEU score metrics.
A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval

Conventional approaches to image-text retrieval mainly focus on indexing visual objects appearing in pictures but ignore the interactions between these objects. Such objects occurrences and interactions are equivalently useful and important in this field as they are usually mentioned in the text. Scene graph presentation is a suitable method for the image-text matching challenge and obtained good results due to its ability to capture the inter-relationship information. Both images and text are represented in scene graph levels and formulate the retrieval challenge as a scene graph matching challenge. In this paper, we introduce the Local and Global Scene Graph Matching (LGSGM) model that enhances the state-of-the-art method by integrating an extra graph convolution network to capture the general information of a graph. Specifically, for a pair of scene graphs of an image and its caption, two separate models are used to learn the features of each graph’s nodes and edges. Then a Siamese-structure graph convolution model is employed to embed graphs into vector forms. We finally combine the graph-level and the vector-level to calculate the similarity of this image-text pair. The empirical experiments show that our enhancement with the combination of levels can improve the performance of the baseline method by increasing the recall by more than 10% on the Flickr30k dataset. Our implementation code can be found at https://github.com/m2man/LGSGM.
A Recognition Method for Multi-Radial-Distance Event of Φ-OTDR System Based on CNN

This paper proposes a multi-radial-distance event classification method based on deep learning. To the best of our knowledge, this is the first time that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR can tell how far the target event from the sensing fiber is through deep learning approach. The temporal-spatial data matrix collected by the system is filtered by three different band-pass filters to form RGB images as the input of the Inception_V3 network trained by ImageNet dataset. The passband of three band-pass filters is selected by searching the maximum Euclidean distance in the frequency domain. Three kinds of filters with different frequency bands enhance the effective features of data samples in advance. The simulated annealing (SA) algorithm is applied to search the maximum Euclidean distance. Field experiment includes five kinds of events with four different radial distances, where there are 17 subclasses in total, has been carried out. The classification results show that the classification accuracy reaches 86% and the method can tell both the event type and radial distance.
Revisiting the TESOL Technology Standards for Teachers

This article revisits the TESOL Technology Standards for Teachers (TTST) and discusses the potential for their continued relevance 13 years after they were first promulgated (TESOL, 2008). To provide a historical backdrop, I first cover the development of the standards from the inside perspective of one of the six members of the team that created them. I next review some key literature relating to the influence of the TESOL standards on teacher education and professional development. I then discuss a multi-year project, in which the TTST were first introduced into an existing CALL course and then integrated throughout the eight units of the course. I describe how the TTST as presented in the two TESOL publications—TESOL (2008) and Healey et al. (2011)—can provide a somewhat problematic set of expectations for the pre-service teacher candidates in that course. Input from participants led to a restructuring of the course and a reformulation of the Healey et al.’s (2011) “can do” statements to better serve those who have not yet begun their teaching career. I conclude with speculation about the value of the TTST in the present and near future, particularly in light of the recent expansion of online teaching brought about by the COVID-19 pandemic.
SituatedQA: Incorporating Extra-Linguistic Contexts into QA

Answers to the same question may change depending on the extra-linguistic contexts (when and where the question was asked).To study this challenge, we introduce SITUATEDQA, an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context.To construct SITUATEDQA, we first identify such questions in existing QA datasets.We find that a significant proportion of information seeking questions have context-dependent answers (e.g.roughly 16.5% of NQ-Open).For such context-dependent questions, we then crowdsource alternative contexts and their corresponding answers.Our study shows that existing models struggle with producing answers that are frequently updated or from uncommon locations.We further quantify how existing models, which are trained on data collected in the past, fail to generalize to answering questions asked in the present, even when provided with an updated evidence corpus (a roughly 15 point drop in accuracy).Our analysis suggests that open-retrieval QA benchmarks should incorporate extra-linguistic context to stay relevant globally and in the future.Our data, code, and datasheet are available at https: //situatedqa.github.io/.
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters

We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs.For this problem the standard procedure so far to leverage the monolingual data is back-translation, which is computationally costly and hard to tune.In this paper we propose instead to use denoising adapters, adapter layers with a denoising objective, on top of pre-trained mBART-50.In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.
CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.
Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

Today, the traffic amount is growing inexorably due to the increase in the number of devices on the network. Researchers analyze traffic by identifying sophisticated dependencies, anomalies, and novel traffic patterns to improve the system performance. One of the fast development niches in this domain is related to Classic and Deep Machine Learning techniques that are supposed to improve the network operation in the most complex heterogeneous environment. In this work, we first outline existing applications of Machine Learning in the communications domain and further list the most significant challenges and potential solutions while implementing those. Finally, we compare different classical methods predicting the traffic on the LTE network Edge by utilizing such techniques as Linear Regression, Gradient Boosting, Random Forest, Bootstrap Aggregation (Bagging), Huber Regression, Bayesian Regression, and Support Vector Machines (SVM). We develop the corresponding Machine Learning environment based on a public cellular traffic dataset and present a comparison table of the quality metrics and execution time for each model. After the analysis, the SVM method proved to allow for a much faster training compared to other algorithms. Gradient Boosting showed the best quality of predictions as it has the most efficient data determination. Random forest shows the worst result since it depends on the number of features that may be limited. The probabilistic approach-based Bayesian regression method showed slightly worse results than Gradient Boosting, but its training time was shorter. The performance evaluation demonstrated good results for linear models with the Huber loss function, which optimizes the model parameters better. As a standalone contribution, we offer the source code of the analyzed algorithms in Open Access.
Adaptive job and resource management for the growing quantum cloud

As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing.This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations.Overall, this proposal is evaluated on simulated IBM machines across a diverse set of quantum applications and system loading scenarios, and is able to reduce wait times by over 3x and improve fidelity by over 40% on specific usecases, when compared to traditional job schedulers.
Ensemble Classification Approach for Sarcasm Detection

Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K-means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.