Reviewerly/exHarmony huggingface dataset:
ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning

Prior work infers the causation between events mainly based on the knowledge induced from the annotated causal event pairs.However, additional evidence information intermediate to the cause and effect remains unexploited.By incorporating such information, the logical law behind the causality can be unveiled, and the interpretability and stability of the causal reasoning system can be improved.To facilitate this, we present an Event graph knowledge enhanced explainable CAusal Reasoning framework (ExCAR).ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.To learn the conditional probabilistic of logical rules, we propose the Conditional Markov Neural Logic Network (CMNLN) that combines the representation learning and structure learning of logical rules in an end-to-end differentiable manner.Experimental results demonstrate that ExCAR outperforms previous state-of-the-art methods.Adversarial evaluation shows the improved stability of Ex-CAR over baseline systems.Human evaluation shows that ExCAR can achieve a promising explainable performance.
Laser Scanning and Parametrization of Weld Grooves with Reflective Surfaces

This paper presents a novel weld groove parametrization algorithm, which is developed specifically for weld grooves in typical stub and butt joints between large tubular elements. The procedure is based on random sample consensus (RANSAC) with additionally proposed correction steps, including a corner correction step for grooves with narrow root weld, and an iterative error elimination step for improving the initially obtained data fit. The problem of curved groove sides (due to the pipe geometry) is attributed and solved. In addition, the procedure detects and eliminates several types of data noise due to laser line reflections. The performance of the procedure is studied experimentally using small-scale test objects, which have been ground using typical industrial power tools to achieve a realistic level of reflections. The execution times and data fit errors of the proposed procedure are compared to a procedure based on a more conventional RANSAC approach for line segment detection.
A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction

Visible and near-infrared (VNIR) spectroscopy technology for soil heavy metal (HM) concentration prediction has been widely studied. However, its spectral response characteristics are still uncertain. In this study, a near standard soil Cd samples (NSSCd) spectra enhanced modeling strategy was developed in order to to reveal the soil cadmium (Cd) spectral response characteristics and predict its concentration. NSSCd were produced by adding the quantitative Cd solution into background soil. Then, prior spectral bands (i.e., the bands with higher variable importance in projection (VIP) score in NSSCd spectra) were used for predicting Cd concentration in soil samples collected from the Hengyang mining area and Baoding agriculture area. The partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS) were used for validation. Compared to using entire VNIR spectral ranges, the new modeling strategy performed very well, with the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) showing an improvement from 0.63 and 1.72 to 0.71 and 1.95 in Hengyang and from 0.54 and 1.57 to 0.76 and 2.19 in Baoding. These results suggest that NSS prior spectral bands are critical for soil HM prediction. Our results represent an exciting finding for the future design of remote sensing sensors for soil HM detection.
Optimal Estimation of Brownian Penalized Regression Coefficients

In this paper we introduce a new methodology to determine an optimal coefficient of penalized functional regression. We assume the dependent, independent variables and the regression coefficients are functions of time and error dynamics follow a stochastic differential equation. First we construct our objective function as a time dependent residual sum of square and then minimize it with respect to regression coefficients subject to different error dynamics such as LASSO, group LASSO, fused LASSO and cubic smoothing spline. Then we use Feynman-type path integral approach to determine a Schr¨odinger-type equation which have the entire information of the system. Using first order conditions with respect to these coefficients give us a closed form solution of them.
An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence

In order to improve the accuracy of sports combination training action recognition, a sports combination training action recognition model based on SMO algorithm optimization model and artificial intelligence is proposed. In this paper, by expanding the standard action data, the standard database of score comparison is established, and the system architecture and the key acquisition module design based on 3D data are given. In this paper, the background subtraction method is used to process the sports video image to obtain the sports action contour and realize the sports action segmentation and feature extraction, and the artificial intelligence neural network is used to train the feature vector to establish the sports action recognition classifier. This paper mainly uses a three-stream CNN artificial intelligence deep learning framework based on convolutional neural network and uses a soft Vlad representation algorithm based on data decoding to learn the action features. Through the data enhancement of the existing action database, it uses support vector machine to achieve high-precision action classification. The test results show that the model improves the recognition rate of sports action and reduces the error recognition rate, which can meet the online recognition requirements of sports action.
Software Project Estimation with Machine Learning

This project involves research about software effort estimation using machine learning algorithms.Software cost and effort estimation are crucial parts of software project development.It determines the budget, time and resources needed to develop a software project.One of the well-established software project estimation models is Constructive Cost Model (COCOMO) which was developed in the 1980s.Even though such a model is being used, COCOMO has some weaknesses and software developers still facing the problem of lack of accuracy of the effort and cost estimation.Inaccuracy in the estimated effort will affect the schedule and cost of the whole project as well.The objective of this research is to use several algorithms of machine learning to estimate the effort of software project development.The best machine learning model is chosen to compare with the COCOMO.
Presentation of a new method based on modern multivariate approaches for big data replication in distributed environments

As the amounts of data and use of distributed systems for data storage and processing have increased, reducing the number of replications has turned into a crucial requirement in these systems, which has been addressed by plenty of research. In this paper, an algorithm has been proposed to reduce the number of replications in big data transfer and, eventually to lower the traffic load over the grid by classifying data efficiently and optimally based on the sent data types and using VIKOR as a method of multivariate decision-making for ranking replication sites. Considering different variables, the VIKOR method makes it possible to take all the parameters effective in the assessment of site ranks into account. According to the results and evaluations, the proposed method has exhibited an improvement by about thirty percent in average over the LRU, LFU, BHR, and Without Rep. algorithms. Furthermore, it has improved the existing multivariate methods through different approaches to replication by thirty percent, as it considers effective parameters such as time, the number of replications, and replication site, causing replication to occur when it can make an improvement in terms of access.
The relationship between Internet of things and search engine optimization in Jordanian Tele-communication Companies: The mediating role of user behavior

This research paper aims to investigate the effect of Internet of Things (IoT) on Search Engine Optimization (SEO) by considering the mediating role of user behavior on this effect. A conceptual research model was developed in accordance with the UTAUT and based on a thorough analysis of extant research. A self-handled questionnaire survey was administered to a purposely selected sample of employees working in telecommunications companies in Jordan. A dataset of 131 usable questionnaires were subjected to data analysis using SmartPLS 3.0. The main findings showed that IoT affected SEO and user behavior. User behavior was also found to be significantly predicted by IoT and to mediate its effect on SEO. The results revealed the significance of SEO as an emerging effective electronic marketing tool in encouraging consumers' adoption of new technologies. The paper ends with a discussion of findings, conclusions, and areas for future research.
A Deep Learning Approach Combining CNN and Bi-LSTM with SVM Classifier for Arabic Sentiment Analysis

Deep learning models have recently been proven to be successful in various natural language processing tasks, including sentiment analysis.Conventionally, a deep learning model's architecture includes a feature extraction layer followed by a fully connected layer used to train the model parameters and classification task.In this paper, we employ a deep learning model with modified architecture that combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, with Support Vector Machine (SVM) for Arabic sentiment classification.In particular, we use a linear SVM classifier that utilizes the embedded vectors obtained from CNN and Bi-LSTM for polarity classification of Arabic reviews.The proposed method was tested on three publicly available datasets.The results show that the method achieved superior performance than the two baseline algorithms of CNN and SVM in all datasets.
Alethe: Towards a Generic SMT Proof Format (extended abstract)

The first iteration of the proof format used by the SMT solver veriT was presented ten years ago at the first PxTP workshop.Since then the format has matured.veriT proofs are used within multiple applications, and other solvers generate proofs in the same format.We would now like to gather feedback from the community to guide future developments.Towards this, we review the history of the format, present our pragmatic approach to develop the format, and also discuss problems that might arise when other solvers use the format.
Towards plug-and-play visual graph query interfaces

Canned patterns (i.e. small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIs for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel generic and extensible framework called TATTOO that takes a data-driven approach to automatically selecting canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity, and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.
A Medical Image Registration Method Based on Progressive Images

Background. Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods. As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results. For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions. The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.
Event-Centric Natural Language Processing

This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events.This tutorial will provide audience with a systematic introduction of (i) knowledge representations and acquisition of events, (ii) various methods for automated extraction, conceptualization, coreference resolution and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLP and commonsense understanding tasks that benefit from aforementioned techniques.We will conclude the tutorial by outlining emerging research problems in this area.
Conceptual Modeling of Explainable Recommender Systems: An Ontological Formalization to Guide Their Design and Development

With the increasing importance of e-commerce and the immense variety of products, users need help to decide which ones are the most interesting to them. This is one of the main goals of recommender systems. However, users’ trust may be compromised if they do not understand how or why the recommendation was achieved. Here, explanations are essential to improve user confidence in recommender systems and to make the recommendation useful.&#x0D; Providing explanation capabilities into recommender systems is not an easy task as their success depends on several aspects such as the explanation’s goal, the user’s expectation, the knowledge available, or the presentation method. Therefore, this work proposes a conceptual model to alleviate this problem by defining the requirements of explanations for recommender systems. Our goal is to provide a model that guides the development of effective explanations for recommender systems as they are correctly designed and suited to the user’s needs. Although earlier explanation taxonomies sustain this work, our model includes new concepts not considered in previous works. Moreover, we make a novel contribution regarding the formalization of this model as an ontology that can be integrated into the development of proper explanations for recommender systems.
How Might We Create Better Benchmarks for Speech Recognition?

The applications of automatic speech recognition (ASR) systems are proliferating, in part due to recent significant quality improvements.However, as recent work indicates, even state-of-the-art speech recognition systems -some which deliver impressive benchmark results, struggle to generalize across use cases.We review relevant work, and, hoping to inform future benchmark development, outline a taxonomy of speech recognition use cases, proposed for the next generation of ASR benchmarks.We also survey work on metrics, in addition to the de facto standard Word Error Rate (WER) metric, and we introduce a versatile framework designed to describe interactions between linguistic variation and ASR performance metrics.
Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis

Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method is split into two stages, parameters optimization and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN is applied to classify the patient with optimized parameters. Our proposed IEC-MLP method which can not only help to reduce the complexity of MLP-NN and effectively selection the optimal feature subset, but it can also obtain the minimum misclassification cost. The classification results were evaluated using the IEC-MLP for different breast cancer datasets and the prediction results obtained were very promising (98.74% accuracy on the WBCD dataset). Meanwhile, the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP could also be applied to other cancer diagnosis.
Benchmarking near-term quantum devices with the variational quantum eigensolver and the Lipkin-Meshkov-Glick model

The Variational Quantum Eigensolver (VQE) is a promising algorithm for Noisy Intermediate Scale Quantum (NISQ) computation. Verification and validation of NISQ algorithms' performance on NISQ devices is an important task. We consider the exactly-diagonalizable Lipkin-Meshkov-Glick (LMG) model as a candidate for benchmarking NISQ computers. We use the Bethe ansatz to construct eigenstates of the trigonometric LMG model using quantum circuits inspired by the LMG's underlying algebraic structure. We construct circuits with depth $\mathcal{O}(N)$ and $\mathcal{O}(\log_2N)$ that can prepare any trigonometric LMG eigenstate of $N$ particles. The number of gates required for both circuits is $\mathcal{O}(N)$. The energies of the eigenstates can then be measured and compared to the exactly-known answers.
Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction

This paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques.Our best submission to the Lexical Complexity Prediction (LCP) shared task was ranked 3rd out of 48 systems for sub-task 1 and achieved Pearson correlation coefficients of 0.779 and 0.809 for single words and multi-word expressions respectively.The conclusion is that a combination of lexical, contextual and semantic features can still produce strong baselines when compared against human judgement.
Semantic Similarity Based Evaluation for Abstractive News Summarization

ROUGE is a widely used evaluation metric in text summarization.However, it is not suitable for the evaluation of abstractive summarization systems as it relies on lexical overlap between the gold standard and the generated summaries.This limitation becomes more apparent for agglutinative languages with very large vocabularies and high type/token ratios.In this paper, we present semantic similarity models for Turkish and apply them as evaluation metrics for an abstractive summarization task.To achieve this, we translated the English STSb dataset into Turkish and presented the first semantic textual similarity dataset for Turkish.We showed that our best similarity models have better alignment with average human judgments compared to ROUGE in both Pearson and Spearman correlations.
A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.
SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning

This paper introduces the SemEval-2021 shared task 4: Reading Comprehension of Abstract Meaning (ReCAM).This shared task is designed to help evaluate the ability of machines in representing and understanding abstract concepts.Given a passage and the corresponding question, a participating system is expected to choose the correct answer from five candidates of abstract concepts in a cloze-style machine reading comprehension setup.Based on two typical definitions of abstractness, i.e., the imperceptibility and nonspecificity, our task provides three subtasks to evaluate the participating models.Specifically, Subtask 1 aims to evaluate how well a system can model concepts that cannot be directly perceived in the physical world.Subtask 2 focuses on models' ability in comprehending nonspecific concepts located high in a hypernym hierarchy given the context of a passage.Subtask 3 aims to provide some insights into models' generalizability over the two types of abstractness.During the SemEval-2021 official evaluation period, we received 23 submissions to Subtask 1 and 28 to Subtask 2. The participating teams additionally made 29 submissions to Subtask 3. The leaderboard and competition website can be found at https://competitions .codalab.org/competitions/26153.