Reviewerly/exHarmony huggingface dataset:
Demystification of Few-shot and One-shot Learning

Few-shot and one-shot learning have been the subject of active and intensive research in recent years, with mounting evidence pointing to successful implementation and exploitation of few-shot learning algorithms in practice. Classical statistical learning theories do not fully explain why few- or one-shot learning is at all possible since traditional generalisation bounds normally require large training and testing samples to be meaningful. This sharply contrasts with numerous examples of successful one- and few-shot learning systems and applications. In this work we present mathematical foundations for a theory of one-shot and few-shot learning and reveal conditions specifying when such learning schemes are likely to succeed. Our theory is based on intrinsic properties of high-dimensional spaces. We show that if the ambient or latent decision space of a learning machine is sufficiently high-dimensional than a large class of objects in this space can indeed be easily learned from few examples provided that certain data non-concentration conditions are met. In this work we present mathematical foundations for a theory of one-shot and few-shot learning and reveal conditions specifying when such learning schemes are likely to succeed. Our theory is based on intrinsic properties of high-dimensional spaces. We show that if the ambient or latent decision space of a learning machine is sufficiently high-dimensional than a large class of objects in this space can indeed be easily learned from few examples provided that certain data non-concentration conditions are met.
Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection

The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually.
MARSA: Multi-Domain Arabic Resources for Sentiment Analysis

The Arabic language has many spoken dialects. However, until recently, it was primarily written in Modern Standard Arabic (MSA), which is the formal variant of Arabic. Social media platforms have changed the face of written Arabic where users converse freely in various dialects, thus offering a massive number of resources for the study of dialectal text. The Arabic dialects differ from MSA in morphology, syntax, and phonetics. Consequently, since the effectiveness of NLP tasks—like sentiment analysis—is dependent on the availability of representative resources, there is currently a great need for such resources in these dialects. In this paper, we present MARSA—the largest sentiment annotated corpus for Dialectal Arabic (DA) in the Gulf region, which consists of 61,353 manually labeled tweets that contain a total of 840 K tokens. The tweets were collected from trending hashtags in four domains: political, social, sports, and technology to create a multi-domain corpus. The importance of such a corpus is to facilitate the study of domain-dependent sentiment analysis in Arabic. In addition to this corpus, the annotators extracted indicator words to form affect lexicons for each domain. We draw insights from these lexicons regarding contextual polarity of certain words. Furthermore, we present benchmark experiments on the MARSA corpus in order to establish a baseline for further studies.
Crafting Adversarial Example to Bypass Flow-&amp;ML- based Botnet Detector via RL

Machine learning(ML)-based botnet detection methods have become mainstream in corporate practice. However, researchers have found that ML models are vulnerable to adversarial attacks, which can mislead the models by adding subtle perturbations to the sample. Due to the complexity of traffic samples and the special constraints that to keep malicious functions, no substantial research of adversarial ML has been conducted in the botnet detection field, where the evasion attacks caused by carefully crafted adversarial examples may directly make ML-based detectors unavailable and cause significant property damage. In this paper, we propose a reinforcement learning(RL) method for bypassing ML-based botnet detectors. Specifically, we train an RL agent as a functionality-preserving botnet flow modifier through a series of interactions with the detector in a black-box scenario. This enables the attacker to evade detection without modifying the botnet source code or affecting the botnet utility. Experiments on 14 botnet families prove that our method has considerable evasion performance and time performance.
Impact of soft TQM practices on employee work role performance: role of innovative work behaviour and initiative climate

Purpose The purpose of this paper is to investigate the effect of soft total quality management (TQM) practices on employee work role performance in the manufacturing sector of United Arab Emirates. It also examined the indirect effect of innovative work behaviour and initiative climate. Design/methodology/approach The study is based on a conclusive research design and survey data was collected from 290 employees working in non-managerial positions across various manufacturing companies in UAE. The hypotheses of the empirical study are tested using structural equation modelling. The indirect effects created through moderated mediation are analysed using Hayes Process Macro. Findings The results show that soft TQM practices have a positive influence on work role performance. Employee innovative work behaviour acts as a partial mediator and initiative climate moderates the relationship. Research limitations/implications The study adopted a cross-sectional analysis and single informants are used, so it is advised that the interpretation of the results is made with enough caution. Practical implications Practitioners must bear in mind that when the employees are adequately trained, empowered, involved and organized as team, it would result in employee innovative behaviour and improved work role performance. The top management should be persuaded to be more committed to the above-mentioned activities for more positive employee-level outcomes. Originality/value Very few studies have conducted an empirical validation at the intersection of human resource management (HRM) and TQM. This is one of the first studies to examine the relationship between soft TQM and employee work role performance.
Continuous Chain Fibonacci: Knowledge Management System with Chatbot

Knowledge Management is a big deal when it comes to Organizations. There will be some general information that needs to be known by all the employees. The normal process of gaining information happens usually through communication. There will be lots of redundancy and misleading information. The main objective of this project is to reduce or eliminate the time of discussion to the person with knowledge within the same organization. The development of the Frequently Asked Questions (FAQ) chatbot helps to maintain the record of questions and answers, whenever a user approaches this bot with queries it provides the user with existing similar questions using Continuous Chain Fibonacci Algorithm. This algorithm is specifically designed for predicting the user’s expected question from the database with minimal complexity and requirements. If the user couldn’t find the query, he/she can post that query directly to the people with higher echelon or to a certain team within the organization to get the expected response. Everything will be stored to save time for someone who is looking for the same information.
Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention

This work deals with Natural Language Processing applied to Electronic Health Records (EHRs). EHRs are coded following the International Classification of Diseases (ICD) leading to a multi-label classification problem. Previously proposed approaches act as black-boxes without giving further insights. Explainable Artificial Intelligence (XAI) helps to clarify what brought the model to make the predictions.This work aims to obtain explainable predictions of the diseases and procedures contained in EHRs. As an application, we show visualizations of the attention stored and propose a prototype of a Decision Support System (DSS) that highlights the text that motivated the choice of each of the proposed ICD codes.Convolutional Neural Networks (CNNs) with attention mechanisms were used. Attention mechanisms allow to detect which part of the input (EHRs) motivate the output (medical codes), producing explainable predictions.We successfully applied methods in a Spanish corpus getting challenging results. Finally, we presented the idea of extracting the chronological order of the ICDs in a given EHR by anchoring the codes to different stages of the clinical admission.We found that explainable deep learning models applied to predict medical codes store helpful information that could be used to assist medical experts while reaching a solid performance. In particular, we show that the information stored in the attention mechanisms enables DSS and a shallow chronology of diagnoses.
Nonlocal Model-Free Denoising Algorithm for Single- and Multichannel SAR Data

Among the large number of Synthetic Aperture Radar (SAR) image despeckling approaches existing in literature, Non-Local (NL) filters have received a desirable boost.However, often NL approaches define the similarity critirion based on model assumptions, such as a fully developed speckle model.This assumption may not be verified in high-resolution images of urban environments.To address this issue, a stand-alone model-free despeckling framework is proposed in this paper.The presented approach provides a generic framework for denoising a variety of SAR products, from a single intensity/amplitude image to polarimetric and interferometric SAR data.In particular, the method is based on the empirical distributional similarity between the patch containing the pixel to be recovered and the patch containing a similar candidate pixel.To decide whether the patches follow a similar distribution, the Kolmogorov-Smirnov test is adapted.Finally, the restoration process aggregates the selected similar pixels based on their relative importance derived from their distribution similarities.To mitigate the blurring effect and preserve the resolution, the inhomogeneity of the ratio image is used to perform the bias reduction step.The designed generic despeckling filter was tested on different products of SAR data.The results show that the method proves to be an unbiased restoration approach and is able to preserve structures and textures.It works fully automatically and efficiently with single and multi-look (and multi-channel) images.
A comprehensive analysis of the 21st century's research trends in English Mobile Learning: a bibliographic review of the literature

Purpose This study examines the importance of English Mobile Learning research as a foundation for lifelong and sustainable education from different points of view, including those of technology innovation experts, psychologists and educators. It aims to explore the current status and relevant research trends through the application of bibliometric mapping and bibliometric analysis. Design/methodology/approach For this study, all Web of Science records (in total 5,343) from 2000 to 2020 in the field of English Mobile Learning were analyzed using the VOSviewer and CiteSpace software tools. The WoS built-in functions, including “Refine” and “Analyze,” were employed to perform the bibliometric analysis. The study further analyzed a sample of the five most-cited articles to identify the previous studies with the highest quality or impact. Findings The results showed that research in English Mobile Learning is growing quickly and steadily with a noticeable emphasis on various device-based technologies and applications. The study also discusses the key implications for research institutions, education policymakers and academicians, and identifies the most prominent avenues for future research on English Mobile Learning. Moreover, the results shared in this review highlight the most important and emerging areas of research in the field. Originality/value This article is the most recent bibliographic review of literature that particularly addresses the English Mobile Learning research during the past two decades.
A Comparison of Two Mixed-Integer Linear Programs for Piecewise Linear Function Fitting

The problem of fitting continuous piecewise linear (PWL) functions to discrete data has applications in pattern recognition and engineering, amongst many other fields. To find an optimal PWL function, the positioning of the breakpoints connecting adjacent linear segments must not be constrained and should be allowed to be placed freely. Although the univariate PWL fitting problem has often been approached from a global optimisation perspective, recently, two mixed-integer linear programming approaches have been presented that solve for optimal PWL functions. In this paper, we compare the two approaches: the first was presented by Rebennack and Krasko [Rebennack S, Krasko V (2020) Piecewise linear function fitting via mixed-integer linear programming. INFORMS J. Comput. 32(2):507–530] and the second by Kong and Maravelias [Kong L, Maravelias CT (2020) On the derivation of continuous piecewise linear approximating functions. INFORMS J. Comput. 32(3):531–546]. Both formulations are similar in that they use binary variables and logical implications modelled by big-[Formula: see text] constructs to ensure the continuity of the PWL function, yet the former model uses fewer binary variables. We present experimental results comparing the time taken to find optimal PWL functions with differing numbers of breakpoints across 10 data sets for three different objective functions. Although neither of the two formulations is superior on all data sets, the presented computational results suggest that the formulation presented by Rebennack and Krasko is faster. This might be explained by the fact that it contains fewer complicating binary variables and sparser constraints. Summary of Contribution: This paper presents a comparison of the mixed-integer linear programming models presented in two recent studies published in the INFORMS Journal on Computing. Because of the similarity of the formulations of the two models, it is not clear which one is preferable. We present a detailed comparison of the two formulations, including a series of comparative experimental results across 10 data sets that appeared across both papers. We hope that our results will allow readers to take an objective view as to which implementation they should use.
Giant nonreciprocal unconventional photon blockade with a single atom in an asymmetric cavity

Inspired by the advantage of unconventional photon blockade (UCPB), we propose a simple cavity-driven quantum electrodynamics model composed of a single two-level atom embedded in an asymmetrical Fabry-Perot cavity. We derive the necessary matching conditions of frequency and intensity required for unconventional photon blockade. In the asymmetric cavity, the optimal atom-cavity coupling strength to get UCPB is related to the driving direction, which causes the equal-time second-order correlation function ${g}^{(2)}(0)$ of the cavity field depending on the driving direction, too. Due to the direction dependent effect, the output photons can be bunched in one direction incident while they are antibunched in the opposite direction incident, which is the so-called nonreciprocal UCPB (NUCPB). A giant NUCPB can be obtained under the appropriate parameter regime, where the output photons are fully antibunching in the opposite driving case. Our findings show a prospective path to producing giant quantum nonreciprocity.
Analysis of Human Interactive Accounting Management Information Systems Based on Artificial Intelligence

BACKGROUND: With the gradual improvement of market economy, people' s consumption level is constantly improving, and the quality requirements are getting higher and higher. OBJECTIVES: In order to study the management accounting information analysis platform based on Artificial Intelligence (AI) and realize the goal of accounting computerization, the application of AI in expert system is applied to the field of accounting information analysis. METHODS: The combination of subsystems is applied to the construction of AI accounting information Web system, and the feasibility analysis of its theory and technology is carried out. RESULTS: The results show that its effect is obvious: accelerating the flow of all information and promoting the change of enterprise management mode. Moreover, compared with the traditional system algorithm, the accuracy of the system model is improved by 6% and the time delay is reduced by 9ms, which makes the overall management level of the enterprise further improved, the scope of enterprise competition further expanded, the cost of enterprise saved
Adaptive mutation quantum-inspired squirrel search algorithm for global optimization problems

This paper presents a novel Adaptive Mutation Quantum-inspired Squirrel Search Algorithm (AM-QSSA). Firstly, based on the population mutation, a location-update of quantum state correlative and attractor method is proposed. By introducing a random process to modify the sliding factor of the local attractor, the inevitable lack of diversity in the population renewal method is solved. The premature convergence problem of adaptive mutation rate improvement algorithm based on squirrel position update mode is introduced. Meanwhile, the paper decomposes the location update process of the SSA, and improves it with quantum-behavior. Furthermore, it proposes a novel quantum-inspired squirrels search algorithm. This method finds the complementary effect between quantum behavior and squirrel search algorithm, and solves the problem of premature convergence probability of SSA. In addition, it improves population diversity, and achieves the balance between global and local search. The efficiency of the proposed AM-QSSA is evaluated using exploitation analysis, exploration analysis, success rate analysis, convergence rate analysis on classical benchmark functions as well as Congress on Evolutionary Computation (CEC) 2017 test functions. For further study, AM-QSSA optimizes an image registration problem for an extensive study to check its applicability. The results reveal that AM-QSSA is more efficient and stable than SSA. And it is comparable to the most advanced optimization algorithms.
A new Gaussian approximate filter with colored non-stationary heavy-tailed measurement noise

The filtering issue of a nonlinear system with colored non-stationary heavy-tailed measurement noise (CNSHMN) is addressed in this study via designing a new Gaussian approximate filter. By utilizing the state expansion method and the measurement difference method, the nonlinear filtering problem with the one-step delayed state and the white non-stationary heavy-tailed measurement noise (NSHMN) after the difference is turned into the traditional nonlinear filtering problem with NSHMN. A Gaussian student t-mixed distribution (GSTM) with Bernoulli random variable is utilized to describe the differenced measurement noise. The state vector, intermediate random variables (IRV), mixed probability and Bernoulli random variable (BRV) are simultaneously inferred by introducing variational Bayesian (VB) technique. Target tracking simulation examples reveal that the proposed filter is superior to the existing methods in the nonlinear filtering issue of CNSHMN.
A Recommendation System Based on AI for Storing Block Data in the Electronic Health Repository

The proliferation of wearable sensors that record physiological signals has resulted in an exponential growth of data on digital health. To select the appropriate repository for the increasing amount of collected data, intelligent procedures are becoming increasingly necessary. However, allocating storage space is a nuanced process. Generally, patients have some input in choosing which repository to use, although they are not always responsible for this decision. Patients are likely to have idiosyncratic storage preferences based on their unique circumstances. The purpose of the current study is to develop a new predictive model of health data storage to meet the needs of patients while ensuring rapid storage decisions, even when data is streaming from wearable devices. To create the machine learning classifier, we used a training set synthesized from small samples of experts who exhibited correlations between health data and storage features. The results confirm the validity of the machine learning methodology.
Video augmentation technique for human action recognition using genetic algorithm

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.