Reviewerly/exHarmony huggingface dataset:
Nucleus Composition in Transition-based Dependency Parsing

Abstract Dependency-based approaches to syntactic analysis assume that syntactic structure can be analyzed in terms of binary asymmetric dependency relations holding between elementary syntactic units. Computational models for dependency parsing almost universally assume that an elementary syntactic unit is a word, while the influential theory of Lucien Tesnière instead posits a more abstract notion of nucleus, which may be realized as one or more words. In this article, we investigate the effect of enriching computational parsing models with a concept of nucleus inspired by Tesnière. We begin by reviewing how the concept of nucleus can be defined in the framework of Universal Dependencies, which has become the de facto standard for training and evaluating supervised dependency parsers, and explaining how composition functions can be used to make neural transition-based dependency parsers aware of the nuclei thus defined. We then perform an extensive experimental study, using data from 20 languages to assess the impact of nucleus composition across languages with different typological characteristics, and utilizing a variety of analytical tools including ablation, linear mixed-effects models, diagnostic classifiers, and dimensionality reduction. The analysis reveals that nucleus composition gives small but consistent improvements in parsing accuracy for most languages, and that the improvement mainly concerns the analysis of main predicates, nominal dependents, clausal dependents, and coordination structures. Significant factors explaining the rate of improvement across languages include entropy in coordination structures and frequency of certain function words, in particular determiners. Analysis using dimensionality reduction and diagnostic classifiers suggests that nucleus composition increases the similarity of vectors representing nuclei of the same syntactic type.
Robust particle-Kalman filtering phase unwrapping algorithm for wrapped fringe patterns

This paper presents a robust phase unwrapping algorithm based on a particle-Kalman filter for wrapped fringe patterns by combining a particle filter and an extended Kalman filter, which formulates the phase unwrapping problem of wrapped fringe patterns as an optimal state estimation problem under the frame of the particle-Kalman filter. First, a state space equation for state variables is extended to the second order of Taylor series, and a local phase gradient estimator based on a modified matrix pencil model is used to obtain the first-order and second-order phase gradient information required by the extended state space equation, which is conducive to enhancing the phase unwrapping accuracy of the proposed procedure. Second, the initial estimate of unwrapped phase is obtained through applying an efficient phase unwrapping program based on a particle filter to unwrap noisy wrapped pixels. Finally, the initial estimate of unwrapped phase obtained by the particle filter is taken as the predicted estimate of state variables and further processed by the extended Kalman filter to obtain the final estimate of unwrapped phase. In addition, an efficient quality-guided strategy that has been demonstrated well is used to guarantee that the particle-Kalman filter efficiently and accurately unwraps wrapped pixels along a suitable path. Results obtained with synthetic data and experimental data demonstrate the effectiveness of the proposed method and show that this new approach can obtain more acceptable solutions from noisy wrapped fringe patterns, with respect to some of the most commonly used methods.
Image Based Visual Servoing for Floating Base Mobile Manipulator Systems with Prescribed Performance under Operational Constraints

This paper presents a novel Image-Based Visual Servoing (IBVS) control approach for Floating Base Mobile Manipulator Systems (FBMMSs) that imposes prescribed transient and steady-state response on the image feature coordinate errors while satisfying the visibility constraints that arise owing to the camera’s limited field of view. The proposed control strategy does not incorporate any knowledge on either the FBMMS dynamic model, the exogenous disturbances, or the inevitable camera calibration and depth measurement errors. More specifically, it guarantees: (i) predefined behavior in terms of overshoot, convergence rate, and maximum steady-state error value of the image features and system velocities tracking errors; (ii) satisfaction of camera field of view constraints; (iii) bounded closed-loop control signals, and (iv) reduced design and implementation complexity. Additionally, the performance of the developed scheme is solely determined by certain designer-specified performance functions/parameters, and it is fully decoupled by the control gains selection. The efficiency of the proposed scheme is demonstrated via a realistic simulation study, using an eye-in-hand Underwater Vehicle Manipulator System (UVMS) as a test-bed FBMMS platform.
A dual quantum image feature extraction method: PSQIFE

In digital image processing, feature extraction occupies a very important position, which is related to the effect of image classification or recognition. At present, effective quantum feature extraction methods are relatively lacking. And the current feature extraction methods are mainly devoted to the extraction of basic features of images, failing to consider the global features of classical images and the global features of quantum images comprehensively. In this paper, we propose a dual quantum image feature extraction method named PSQIFE, which focuses on the global energy representation of images by constructing dual quantum image global features. The representation of the global features of the dual quantum image is obtained by quantum superposition of two parts of quantum state features. In this paper, quantum image reconstruction and quantum image fidelity tests are performed on the extracted global features by 9 classes of classical images, and the overall fidelity is above 95%. In addition, the effectiveness of PSQIFE dual quantum image feature extraction method is verified by comparing the image classification test with convolutional feature extraction method on Mnist dataset. The method has some reference significance for the research of quantum image feature extraction and classification.
Metric learning via perturbing hard-to-classify instances

Constraint selection is an effective means to alleviate the problem of a massive amount of constraints in metric learning. However, it is difficult to find and deal with all association constraints with the same hard-to-classify instance (i.e., an instance surrounded by dissimilar instances), negatively affecting metric learning algorithms. To address this problem, we propose a new metric learning algorithm from the perspective of selecting instances, Metric Learning via Perturbing of Hard-to-classify Instances (ML-PHI), which directly perturbs the hard-to-classify instances to reduce over-fitting for the hard-to-classify instances. ML-PHI perturbs hard-to-classify instances to be closer to similar instances while keeping the positions of the remaining instances as constant as possible. As a result, the negative impacts of hard-to-classify instances are effectively reduced. We have conducted extensive experiments on real data sets, and the results show that ML-PHI is effective and outperforms state-of-the-art methods.
The Application of Successful Physical Education Teaching Mode Integrating Deep Learning in Basketball Teaching

The successful physical education teaching mode is a new teaching mode used in physical under the traditional physical education teaching mode, which is formed and developed from the teaching reform of physical education in recent years. Under the background of The Times, the new educational mode conforms to the trend of The Times and is conducive to the benign development of education. Compared with the traditional physical education teaching mode, the students of the successful physical education teaching mode have a stronger sense of experience. This strong sense of participation and experience can not only improve students’ love for sports but also have a positive impact on the development of students’ mental health and also free the cultivation of students’ personality. Deep learning (DL) is a type of pattern analysis method in machine learning algorithms, which solves many complex pattern recognition problems, and has achieved many results in the fields of search technology. In this paper, by constructing a deep learning model, the model is applied to the application research of the successful physical education teaching mode in basketball teaching, and the effect of the successful physical education teaching mode in actual teaching is obtained through comparative experiments. According to the experimental results, the successful physical education teaching mode has a more significant effect on the teaching of students’ basketball skills, and according to the experiment, when the number of fine-tuning is 180, the correct rate is the highest at this time, which is 85%. When the number of hidden layer nodes is 180, the highest accuracy rate is 85.6%, and according to the experiments on the classification results, it is concluded that the model has a high degree of matching with the application research of the successful physical education model in basketball teaching.
An Empirical Study Towards an Automatic Phishing Attack Detection Using Ensemble Stacking Model

Phishing attacks are one of the most attacks facing internet users, especially after the COVID-19 pandemic, as most organizations have transferred part or most of their work and communication to become online using well-known tools, like email, Zoom, WebEx, etc. Therefore, cyber phishing attacks have become progressively recent, directly and frankly reflecting the designated website, allowing the attacker to observe everything while the victim is exploring Webpages. Hence, utilizing Artificial Intelligence (AI) techniques has become a necessary approach that could be used to detect such attacks automatically. In this paper, we introduce an empirical analysis for automatic phishing detection using several machine learning classification algorithms compared with an ensemble learning model for detecting phishing sites based on the uniform resource locator (URL) using two preprocessed datasets. In this empirical study, we concluded that the ensemble model grants accuracy 97.49% for dataset 1 and 98.69% for dataset 2, which gives higher accuracy than using a single machine learning classification algorithm such as Naive Bayes (NB), Random Forest (RF), Decision Trees (DTs), K-Nearest Neighbors (KNN), and linear and Quadratic Discriminant Analysis. We also compared the proposed ensemble model with one of the most recent similar models.
Detection of Appearance and Behavior Anomalies in Stationary Camera Videos Using Convolutional Neural Networks

The automatic detection and tracking of appearance and behavior anomalies in video surveillance systems is one of the promising areas for the development and implementation of artificial intelligence. In this paper, we present a formalization of these problems. Based on the proposed generalization, a detection and tracking algorithm that uses the tracking-by-detection paradigm and convolutional neural networks (CNNs) is developed. At the first stage, people are detected using the YOLOv5 CNN and are marked with bounding boxes. Then, their faces in the selected regions are detected and the presence or absence of face masks is determined. Our approach to face-mask detection also uses YOLOv5 as a detector and classifier. For this problem, we generate a training dataset by combining the Kaggle dataset and a modified Wider Face dataset, in which face masks were superimposed on half of the images. To ensure a high accuracy of tracking and trajectory construction, the CNN features of the images are included in a composite descriptor, which also contains geometric and color features, to describe each person detected in the current frame and compare this person with all people detected in the next frame. The results of the experiments are presented, including some examples of frames from processed video sequences with visualized trajectories for loitering and falls.
Efficiency of Local Binarization Methods in Segmentation of Selected Objects in Echocardiographic Images

Nowadays echocardiography devices have become more and more precise, therefore allowing for more detailed diagnostics. With the use of automatic or semi-automatic algorithms the needed time for diagnostics can be shortened thus it can allow for better medical treatment and results. An example of that can be found in examination of echocardiographic images of a PFO suspected patient. To accelerate that process the automatic algorithm can be used to segment the first generation contrast agent bubbles used in condition assessment of a patient. We propose a method that consists of local thresholding methods which, unlike neural networks, does not need a big dataset of images. Obtained results are at the Dice coefficient level of 0.5–0.8 depending on the image.
Popper’s Theory of Deductive Logic

We present Popper’s theory of deductive logic as exhibited in his articles published between 1947 and 1949. After an introduction to Popper’s inferentialist approach and his idea of “inferential definitions” of logical constants, we discuss in more formal detail Popper’s general theory of the deducibility relation (which, using Gentzen’s terminology, might be called his “structural” theory), as well as his special theory of logical constants. We put special emphasis on his inferential notion of duality, which includes his analysis of the “anti-conditional” (today called “co-implication”), his systematic study of various forms of negation including classical, intuitionistic and weaker negations, his system of bi-intuitionistic logic, and his theory of quantification and identity. We also touch on his treatment of modal operators, which is based on Carnap’s Meaning and Necessity.
Perspectives from 50+ Years’ Practical Zero Trust Experience and Learnings on Buyer Expectations and Industry Promises

Everybody's talking about Zero Trust (ZT). Even the White House issued an Executive Order to start implementing Zero Trust. Many technology manufacturers position their products as enabling or fulfilling ZT requirements. ZT focuses on eliminating trust in the digital network, verifying all traffic, and segmenting the environment. By enforcing on every service, user, or application, strict access and verification policy avoid bad actors having unauthorized access to systems. This paper continues the authors' previous research on examining Zero Trust approaches. It defines the core problems of vendor promises which causes Information Asymmetry that impede the understanding and successful implementation of Zero Trust. We first start with a description of Zero Trust and continue with practical lessons that we have gathered from six expert interviews with a collective experience of over 50 years implementing Zero Trust in diverse settings. The paper finishes by providing concrete guidance and examples that practitioners can consider when implementing Zero Trust.
Survey of Leukemia Cancer Cell Detection Using Image Processing

Cancer is the development of abnormal cells that divide at an abnormal pace, uncontrollably. Cancerous cells have the ability to destroy other normal tissues and can spread throughout the body. Cancer cells can develop in various parts of the body. The paper focuses on leukemia which is a type of blood cancer. Blood cancer usually start in the bone marrow where the blood is produced in the body. The types of blood cancer are: Leukemia, Non-Hodgkin lymphoma, Hodgkin lymphoma, and Multiple myeloma. Leukemia is a type of blood cancer that originates in the bone marrow. Leukemia is seen when the body produces an abnormal amount of white blood cells that hinder the bone marrow from creating red blood cells and platelets. Several detection methods to identify the cancerous cells have been proposed. Identification of the cancer cells through cell image processing is very complex. The use of computer aided image processing allows the images to be viewed in 2D and 3D making it easier to identify the cancerous cells. The cells have to undergo segmentation and classification in order to identify the cancerous tumours. Several papers propose segmentation methods, classification methods and some propose both. The purpose of this survey is to review various papers that use either conventional methods or machine learning methods to detect the cells as cancerous and non-cancerous.
Perfect state transfer on semi-Cayley graphs over abelian groups

In this paper, we consider the problem on the existence of perfect state transfer (PST for short) on semi-Cayley graphs over abelian groups (which are not necessarily regular), i.e. on the graphs having semiregular and abelian subgroups of automorphisms with two orbits of equal size. We stablish a characterization of semi-Cayley graphs over abelian groups having PST. As a result, we give a characterization of Cayley graphs over groups with an abelian subgroup of index 2 having PST, which improves the earlier results on Cayley graphs over abelian groups, dihedral groups and dicyclic group and determines Cayley graphs over generalized dihedral groups and generalized dicyclic groups having PST.
COVID‐19 and future pandemics: A blockchain‐based privacy‐aware secure borderless travel solution from electronic health records

COVID-19 pandemic undoubtedly lingers on and has brought unprecedented changes globally including travel arrangements. Blockchain-based solutions have been proposed to aid travel amid the pandemic hap. Presently, extant solutions are country or regional-based, downplay privacy, non-responsive, often impractical, and come with blockchain-related complexities presenting technological hurdle for travelers. We therefore propose a solution namely, Borderless to foster global travel allowing travelers and countries collaboratively engage in a secure adaptive proof protocol dubbed Proof-of-COVID-19 status a number of arbitrary statements to ascertain the fact that the traveler poses no danger irrespective of the country located. As far as we know, this is first of its kind. Borderless is implemented as a decentralized application leveraging blockchain as a trust anchor and decentralized storage technology. Security analysis and evaluation are performed proving security, privacy-preservation, and cost-effectiveness along with implementation envisioning it as a blueprint to facilitate cross-border travel during the present and future pandemics. Our experimental results show it takes less than 60 and 3 s to onboard users and perform proof verification respectively attesting to real usability scenarios along with the traits of arbitrary proofs to aid responsiveness to the dynamics of pandemics and blockchain abstraction from travelers.
Knowledge-oriented methodologies for causal inference relations using fuzzy cognitive maps: A systematic review

The latest research in the field of casual relations using fuzzy cognitive maps utilizes various adaptive supervised learning techniques. These supervised methods provide promising data understanding and processing the fuzzy outcomes. This article contributes in the modern research by providing an adaptive review of different knowledge-oriented methods for the establishment of Fuzzy Cognitive Maps (FCM) based Causal Inference Relations exploiting various domains. A comprehensive review is presented in this article that significantly introduces the artificial intelligence-based methodologies and their progress in the field of casual inference relations. An ensembled-FCM based approach is presented in this article which is analysed and compared to the conventional Hebbian based, error-driven and hybrid approaches. The performance parameters evaluated for the testing phase of E-FCM provides the precision, recall and accuracy rates of 98.05 %, 97.83 % and 94.54 % respectively, while prediction errors observed MSE value of 0.034 ± 0.005 with relevant reduction in RSME and MSE of 0.052 ± 0.007 and 0.085 ± 0.009 respectively. Further, a systematic categorization of existing techniques is presented based on the taxonomy of different techniques from varying perspectives like its extension and application domains. Finally, an outline of the potential research directions in this field is presented, establishing a clear understanding of development in this domain.
Hospital patients’ length of stay prediction: A federated learning approach

Predicting patient's length of stay (LOS) is a crucial determinant for hospitals to maintain resource efficiency and quality treatment, where machine learning-based predictive approaches can be extremely beneficial. Though the healthcare industry's increasing adoption of information technology has transformed it into a massive data hub, the bulk of this data is kept within the medical institution yet not shared with others for confidentiality concerns; which makes it difficult to construct efficient predictive analytics that require vast amount of training data. Hence, this study proposes a federated machine learning-based model for forecasting patients' LOS in days combining the results from locally trained models of various decentralized and heterogeneous hospital clients while maintaining their data privacy to train an aggregated predictive model in the server. Here, ten hospital client's administrative data have been trained using three types of machine learning regression models locally utilizing their own data. Then, the parameters (intercept and coefficient) of the locally trained models are sent to the central server in multiple rounds, where they have been aggregated to construct a combined model for LOS prediction. The regression analysis performances of the locally trained models and the server-side model aggregating different number of clients have been compared through various parameter metrics. The findings reveal that, the aggregated model's predictive performance with federated learning is less error-prone, and that the model's performance improves when more clients' parameters are integrated on the server side.