Reviewerly/exHarmony huggingface dataset:
Chart-based Zero-shot Constituency Parsing on Multiple Languages

Zero-shot constituency parsing is a recent methodology in unsupervised parsing that aims to extract parse trees from pre-trained language models (PLMs) with no extra training. This paper improves upon the existing paradigm by introducing the combination of a novel chart-based method and an effective ensemble technique, attaining performance competitive to other unsupervised parsers on English PTB. Furthermore, we broaden the range of zero-shot parsing application by examining languages other than English. Specifically, we first demonstrate that the approach is applicable to the languages that are equipped with their respective monolingual PLMs. Finally, we propose to introduce multilingual PLMs into the zero-shot parsing framework, confirming that it is possible to generate reasonable parses for sentences in nine languages in an integrated and language-agnostic manner.
Smart face identification via improved <scp>LBP</scp> and <scp>HOG</scp> features

Smart face identification is widely used in smart city and smart healthcare. However, smart face identification technology is susceptible to envirnmental factor, such as illumination, mask, and expression. In order to fully extract facial feature information, we fuse an improved local binary pattern (LBP) and the histogram of oriented gradients (HOG) to extract the texture and detailed features on the face. The 2DPCA + PCA is used to reduce the dimensionality of the extracted features. The 2DPCA sloves the issue that the model is too complex when the feature dimension is very high. The feature reduction reduces the calculation scale and increases the calculation speed. Finally, experimental results on ORL and Yale face databases show that the feature extraction based on the fusion of improved LBP and HOG complement with each other. Compared with other recognition algorithms, the improved algorithm has higher recognition and identification rate.
Develop Academic Question Recommender Based on Bayesian Network for Personalizing Student’s Practice

Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning. In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components. This question recommender is discussed with theoretical analysis, and designed and implemented in software engineering way. It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course. This question recommender is planned to deploy in real learning context for the future validation of how well such question recommendation improves performance and saves practice time for student.
A Transfer Learning Approach Utilizing Combined Artificial Samples for Improved Robustness of Model to Estimate Heavy Metal Contamination in Soil

Benefiting from the nanoscale sampling intervals and subtle spectral information in the visible and near-infrared band, hyperspectral technology is considered as an efficient means for monitoring soil heavy metal contamination whereby the good robustness of prediction model is driven by the increase to spectral dimension in model analysis. Considering the positive correlation between samples size and spectral dimension, we focuses on a novel derivation of enlarging samples size in this study to improve model performance by i) preparing artificial samples taking into account of flexibility and control over the laboratory environment compared with collecting wild samples, and ii) using transfer learning method called transfer component analysis (TCA) for reducing spectral feature differences caused by soil heterogeneity to train model in the same data distribution. The proposed approach was tested on three heavy metals, namely copper (Cu), cadmium (Cd) and lead (Pb), collected in the mining area located in the Xiangjiang Basin, Hunan Province, China. The experiments showed that the initial model constructed by a small number of wild samples performed strong prediction sensitivity as the training samples change. In contrast, a modified model with TCA could showed good robustness with excellent predicted ability, the average prediction accuracy of the determinable coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and the ratio of prediction to deviation (RPD) improved to 0.73 and 1.90, 0.74 and 1.92, 0.72 and 1.73, respectively. The results illustrated there was a more reliable modeling method in potential to predict soil heavy metals based on hyperspectral analysis at low cost.
Collaborative Generative Hashing for Marketing and Fast Cold-Start Recommendation

Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce. Most existing studies proposed to alleviate the cold-start problem are also known as hybrid recommender systems that learn representations of users and items by combining user-item interactive and user/item content information. However, previous hybrid methods regularly suffered poor efficiency bottlenecking in online recommendations with large-scale items, because they were designed to project users and items into continuous latent space where the online recommendation is expensive. To this end, we propose a collaborative generated hashing (CGH) framework to improve the efficiency by denoting users and items as binary codes, then fast hashing search techniques can be used to speed up the online recommendation. In addition, the proposed CGH can generate potential users or items for marketing application where the generative network is designed with the principle of minimum description length, which is used to learn compact and informative binary codes. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze its feasibility in marketing application.
Investigative Analysis of Suboptimal Filter for State Estimation in Object Tracking Wireless Sensor Network

The research presented here is the investigation of particle filter (PF) performance in a state estimation of a point mass type noncooperative moving target in an object tracking wireless sensor network (OTWSN). Motion dynamics of the target is modeled as a stochastic state-space model by simulating different time-varying parameters of a jump Markov linear system in a finite way considering these parameters are sufficiently large to meet the practical needs. Performance is evaluated on the basis of the lower bound of the posterior Cramér-Rao bound as root-mean-square error (RMSE). It has been found that the performance is subject to dynamics of the target motion and sensor observation process under consideration. The present research focuses on multiple motion dynamics, keeping in mind the real-time noncooperative moving target tracking in OTWSN. Results indicate a positive correlation between RMSE and motion models. Correlation increases as the motion dynamics change, but still, PF demonstrates suboptimal response to motion dynamics.
Proof of Activity Consensus Algorithm Based on Credit Reward Mechanism

Proof of Activity (PoA) is a key algorithm to reach consensus among nodes. In current PoA, N online representative nodes are only used to create one transaction block, and the probability of creating a block by malicious nodes cannot be controlled, which leads to a serious waste of computing power. An improved algorithm proposed in this paper introduces credit reward mechanism to replenish the missing trust in current PoA. It also can control the probability that the node obtains the right of creating block head and trading block according to the credit value and set up the reward and punishment scheme according to the proportion of credit value, which decrease the cost of good node generating block with increasing credit value and increase the cost of malicious node creating block significantly. The algorithm uses Byzantine fault tolerant idea and follow-the-Satoshi mechanism to select representative nodes through multi-level selection and set different workload for the nodes at different levels, by which the probability of malicious nodes creating transaction blocks is reduced effectively. The experimental results show that the number of transaction blocks in a block header in CPoA is 1.75 times increase than PoA. The reward and punishment scheme can achieve the purpose of dealing with malicious nodes quickly. When the proportion of malicious nodes increased from 30% to 70%, the average decline rate of their overall credit value increased about 1.7 times, which reduces the probability of malicious nodes creating blocks, increases the cost of malicious nodes creating blocks, and enhances the stability of the system.
WIP Vehicle Control Method Based on Improved Artificial Potential Field Subject to Multi-obstacle Environment

This paper presents a control method for a WIP vehicle in multi-obstacle environment based on improved artificial potential field. Firstly, an improved artificial potential field (IAPF) is developed, where a safe distance is introduced to the existing repulsive potential field to solve the security issue, while the local minima can also be eliminated in the meantime. Next, an obstacle avoidance controller is designed based on the IAPF, where the nonholonomic constraint and underactuated characteristic of the WIP vehicle are fully considered, and the stability condition of the system is analyzed by means of the related control theory. Moreover, to further improve the control performance, a key parameter that play an important role in the controller is adjusted by taking advantage of fuzzy logic, and detailed analyses are given to demonstrate its necessity and effectiveness. Finally, considering a motion environment that contains dense obstacles, narrow corridor and an obstacle near the target, numerical simulations are conducted to validate the proposed method, whose results indicate that the method has a good performance to control the WIP vehicle in multi-obstacle environment.
Fast Unsupervised Automobile Insurance Fraud Detection Based on Spectral Ranking of Anomalies

Collecting insurance fraud samples is costly and if performed manually is very time consuming. This issue suggests the usage of unsupervised models for fraud data collection. One of the accurate methods in this regards is Spectral Ranking of Anomalies (SRA) that is shown to work better than other methods for auto-insurance fraud detection, specifically. However, this approach is not scalable to large samples and is not appropriate for online fraud detection. This is while, real-time fraud management systems are necessary to prevent huge losses. In this study, we propose an implementation methodology which makes it possible to apply the SRA to big data senarios. We exploit the power of spectral ranking of anomalies to create an estimated target variable from the unlabeled dataset. We then use two robust models, namely, random forest and deep neural networks to ﬁt a model based on the estimated labeled training set. Next, the incoming live data are fed to the mentioned trained models for predicting the target variable. Simulation results conﬁrm that the proposed approach has higher speed and acceptable false alarm rate compared to existing related methods.
Building a Competitive Associative Classifier

With the huge success of deep learning, other machine learning paradigms have had to take back seat. Yet other models, particularly rule-based, are more readable and explainable and can even be competitive when labelled data is not abundant. However, most of the existing rule-based classifiers suffer from the production of a large number of classification rules, affecting the model readability. This hampers the classification accuracy as noisy rules might not add any useful information for classification and also lead to longer classification time. In this study, we propose SigD2 which uses a novel, two-stage pruning strategy which prunes most of the noisy, redundant and uninteresting rules and makes the classification model more accurate and readable. To make SigDirect more competitive with the most prevalent but uninterpretable machine learning-based classifiers like neural networks and support vector machines, we propose bagging and boosting on the ensemble of the SigDirect classifier. The results of the proposed algorithms are quite promising and we are able to obtain a minimal set of statistically significant rules for classification without jeopardizing the classification accuracy. We use 15 UCI datasets and compare our approach with eight existing systems. The SigD2 and boosted SigDirect (ACboost) ensemble model outperform various state-of-the-art classifiers not only in terms of classification accuracy but also in terms of the number of rules.
Manifold Learning Algorithms Applied to Structural Damage Classification

A comparative study of four manifold learning algorithms was carried out to perform the dimensionality reduction process within a proposed methodology for damage classification in structural health monitoring (SHM). Isomap, locally linear embedding (LLE), stochastic proximity embedding (SPE), and laplacian eigenmaps were used as manifold learning algorithms. The methodology included several stages that comprised: data normalization, dimensionality reduction, classification through K-Nearest Neighbors (KNN) machine learning model and finally holdout cross-validation with 25% of data for training and the remaining 75% of data for testing. Results evaluated in an experimental setup showed that the best classification accuracy was 100% when the methodology uses isomap algorithm with a hyperparameter k of 170 and 8 dimensions as a feature vector at the input to the KNN classification machine.
Improved Attacks on sLiSCP Permutation and Tight Bound of Limited Birthday Distinguishers

Limited birthday distinguishers (LBDs) are widely used tools for the cryptanalysis of cryptographic permutations. In this paper we propose LBDs on several variants of the sLiSCP permutation family that are building blocks of two round 2 candidates of the NIST lightweight standardization process: Spix and SpoC. We improve the number of steps with respect to the previously known best results, that used rebound attack. We improve the techniques used for solving the middle part, called inbound, and we relax the external conditions in order to extend the previous attacks. The lower bound of the complexity of LBDs has been proved only against functions. In this paper, we prove for the first time the bound against permutations, which shows that the known upper bounds are tight.
Using a Genetic Algorithm to Optimize Configurations in a Data-Driven Application

Users of highly-configurable software systems often want to optimize a particular objective such as improving a functional outcome or increasing system performance. One approach is to use an evolutionary algorithm. However, many applications today are data-driven, meaning they depend on inputs or data which can be complex and varied. Hence, a search needs to be run (and re-run) for all inputs, making optimization a heavy-weight and potentially impractical process. In this paper, we explore this issue on a data-driven highly-configurable scientific application. We build an exhaustive database containing 3,000 configurations and 10,000 inputs, leading to almost 100 million records as our oracle, and then run a genetic algorithm individually on each of the 10,000 inputs. We ask if (1) a genetic algorithm can find configurations to improve functional objectives; (2) whether patterns of best configurations over all input data emerge; and (3) if we can we use sampling to approximate the results. We find that the original (default) configuration is best only 34% of the time, while clear patterns emerge of other best configurations. Out of 3,000 possible configurations, only 112 distinct configurations achieve the optimal result at least once across all 10,000 inputs, suggesting the potential for lighter weight optimization approaches. We show that sampling of the input data finds similar patterns at a lower cost.
Spin Connection Topology and Multi-Bit Spin Interaction Coefficients of Ising Spin Model with Digital Logic Gates

Combinatorial optimization problems often arise naturally in various fields of research. However, finding the global optimum solution of these problems remains computationally difficult. Ising spin model is considered to be an effective method to solve combinatorial optimization problems because these problems can be mapped and embedded into the Ising spin model. In general, Ising spin model requires to adjust and tune the spin connection parameters, for example, topology of spin connection and resolution of exchange interaction coefficient values between spins, according to the problems. In this paper, we focus on the number of spin connection, which is one of the spin connection parameters, and perform the calculation experiments through solving max-cut problems with Ising spin computing. As a result, all-to-all connectivity makes Ising spin computing well suited for searching the ground states of complex combinatorial optimization problems.
Knowledge, semantics and AI for risk and crisis management

Journal of Contingencies and Crisis ManagementVolume 28, Issue 3 p. 174-177 EDITORIAL Knowledge, semantics and AI for risk and crisis management Antonio De Nicola, Corresponding Author Antonio De Nicola antonio.denicola@enea.it ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy Correspondence Antonio De Nicola, ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Italy. Email: antonio.denicola@enea.itSearch for more papers by this authorHedi Karray, Hedi Karray INP-ENIT, University of Toulouse, Tarbes, FranceSearch for more papers by this authorMayank Kejriwal, Mayank Kejriwal University of Southern California, Marina Del Rey, USASearch for more papers by this authorNada Matta, Nada Matta University of Technology of Troyes, Troyes, FranceSearch for more papers by this author Antonio De Nicola, Corresponding Author Antonio De Nicola antonio.denicola@enea.it ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy Correspondence Antonio De Nicola, ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Italy. Email: antonio.denicola@enea.itSearch for more papers by this authorHedi Karray, Hedi Karray INP-ENIT, University of Toulouse, Tarbes, FranceSearch for more papers by this authorMayank Kejriwal, Mayank Kejriwal University of Southern California, Marina Del Rey, USASearch for more papers by this authorNada Matta, Nada Matta University of Technology of Troyes, Troyes, FranceSearch for more papers by this author First published: 29 September 2020 https://doi.org/10.1111/1468-5973.12322Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume28, Issue3Special Issue: Knowledge, Semantics and AI for Risk and Crisis ManagementSeptember 2020Pages 174-177 RelatedInformation
A CMM-Based Method of Control Point Position Calibration for Light Pen Coordinate Measuring System

A light pen coordinate measuring system (LPCMS) is a kind of vision-based portable coordinate measuring technique. It implements coordinate measurement by analyzing the image of a light pen, which has several control points and a probe. The relative positions of control points need to be determined before measurement and serve as the measuring basis in LPCMS. How to accurately calibrate the relative positions of control points is the most important issue in system calibration. In this paper, a new method of control point position calibration based on a traditional coordinate measuring machine (CMM) is proposed. A light pen is fastened to the measuring arm of a CMM and performs accurate translational movement driven by the CMM. A camera is used to capture the images of control points at different positions, and the corresponding readings of the CMM are recorded at the same time. By establishing a separate coordinate system for each control point, the relative positions of the control points can be transformed to the differences of a series of translation vectors. Experiments show that the calibration repeatability of control point positions can reach 10 μm and the standard deviation of measurement of the whole LPCMS can reach 30 μm. A CMM is used to generate accurate translation, which provides a high accuracy basis of calibration. Through certain mathematical treatment, tremendous data acquired by moving the light pen to tens of thousands of different positions can be processed in a simple way, which can reduce the influence of random error. Therefore, the proposed method provides a high-accuracy solution of control point position calibration for LPCMS.
Neural Network based Explicit Mixture Models and Expectation-maximization based Learning

We propose two neural network based mixture models in this work. The proposed mixture models are explicit. The explicit models have analytical forms with the advantages of computing likelihood and efficiency of generating samples. Expectation-maximization based algorithms are developed for learning parameters of the proposed models. We provide sufficient conditions to realize the expectation-maximization based learning. The main requirements are invertibility of neural networks that are used as generators and Jacobian computation of functional form of the neural networks. The requirements are practically realized using a flow-based neural network. In our first mixture model, we use multiple flow-based neural networks as generators. Naturally the model is complex. A single latent variable is used as the common input to all the neural networks. The second mixture model uses a single flow-based neural network as a generator to reduce complexity. The single generator has a latent variable input that follows a Gaussian mixture distribution. The proposed models are verified via training with expectation-maximization based algorithms on practical datasets. We demonstrate efficiency of proposed mixture models through extensive experiments for generating samples and maximum likelihood based classification.