Reviewerly/exHarmony huggingface dataset:
Deep traffic sign detection and recognition without target domain real images

Deep learning has become a standard approach to machine vision in recent years. Despite several advances, it requires large amounts of annotated data. Nonetheless, in many applications, large-scale data acquisition and annotation is expensive and data imbalance is an intrinsic problem. To address these challenges, we propose a novel synthetic database generation method that only requires (i) arbitrary natural images, i.e., does not demand real images from the target domain, and (ii) templates of the traffic signs. Our method does not aim at overcoming the training with real data but to be a compatible option when there is a lack of real data. Results with data of multiple countries show that the synthetic database generated without human effort is effective for training a deep traffic sign detector. On large datasets, training with a fully synthetic dataset almost matches the performance of training with a real one. When compared to training with a smaller dataset of real images, training with synthetic images increased the accuracy by 12.25%. The proposed method also improves the performance of the detector when target-domain data are available.
A Gaussian-Biased Heuristic for Stochastic Sampling-Based 2D Trajectory Planning Algorithms

This paper addresses the problem of slow convergence for stochastic sampling-based trajectory planners with applications to Unmanned Aerial Vehicles. Typically, stochastic sampling-based trajectory planners apply a uniform probability distribution to the vehicle's configuration space for random sampling. This results in execution times that make these planners not applicable to many real-time systems. The proposed method obtains first the obstacle-free trajectory according to the trajectory planner's steering function. A minimum area bounding ellipse is then defined for the obstacle-free trajectory and is expanded to satisfy a given maximum obstacle intersection area. The resulting elliptical surface is then converted to a Gaussian distribution for randomly generating a given percentage of samples in the interior or along the boundary of the elliptical surface. The proposed Gaussian-biased sampling strategy is applied to a minimum time trajectory planning problem and is compared with a uniformly distributed sampling strategy as well as two other sampling strategies taken from the literature. Simulations results show that the proposed sampling strategy yields a reduction of the computation time for producing an initial trajectory, of the initial trajectory cost, of the final trajectory cost, and of the algorithm's failure rate. Additionally, the proposed Gaussian-biased sampling strategy naturally inherits the completeness and optimality properties of the trajectory planning algorithm.
Construction of TCM Health Management Model for Patients with Convalescence of Coronavirus Disease Based on Artificial Intelligence

Coronavirus Disease(COVID-19) is prevalent worldwide.Discharged patients generally have decreased lung function, immune function, psychological and social adaptability.Providing dynamic, comprehensive and continuous health services for patients returning to the community and families has become a prominent problem in the global public health field.In recent years, artificial intelligence (AI) and big data management has been widely used in the field of medicine, but it is still relatively blank in the field of health management.Based on this, this study is guided by the traditional Chinese medicine(TCM) thinking of "pre-preventing disease, preventing pre-existing disease, and preventing post-relapse", combined with TCM health management plan, referring to the three-way linkage model of hospital-community-family, constructing TCM health management model for patients with convalescence of COVID-19 based on artificial intelligence.This article will tap the advantages of TCM resources, improve the quality of patient recovery "Rehabilitation", break the time and space limitations of health management and provide TCM health management methods for patients with convalescence with a view to human health promotion.
Calculus of new intuitionistic fuzzy generator: In generated intuitionistic fuzzy sets and its applications in medical diagnosis

In this research paper, we defined the generated intuitionistic fuzzy set.We generated an intuitionistic fuzzy generator to define the generated intuitionistic fuzzy set.The generated intuitionistic fuzzy set is a generalization of the intuitionistic fuzzy set.We proved some basic properties of generated intuitionistic fuzzy set in the context of intuitionistic fuzzy sets; some results are proved by using the notion of newly generated intuitionistic fuzzy set and constructed intuitionistic fuzzy generator.A mathematical approach for the application of the generated intuitionistic fuzzy set is also given in this paper.
Pedagogical Features Of Moodle Platform

This article discusses ways to improve the pedagogical capabilities of the Moodle platform.Based on the Five Principles of the Moodle platform, the main components of the educational process, elements and possibilities of the structure of the distance course are considered.
A multilayer multi-configurational approach to efficiently simulate large-scale circuit-based quantum computers on classical machines

A multilayer multi-configurational theory framework is adapted to simulate circuit-based quantum computers. Quantum addition of superpositions of an exponential number of summands is performed in polynomial time with high accuracy. We demonstrate numerically accurate calculations including up to one million qubits for entangling benchmarks. Simulation cost can be assessed by entropy-based entanglement measures. For the considered systems, we show that the entanglement only grows weakly with the system size. The present simulations demonstrate how quantum algorithms in low-entropy regimes can be used efficiently on classically simulated quantum computers.
A Concept for Virtual Reality Based Industrial Maintenance Training Preparation

This paper presents ongoing research about the use of Virtual Reality (VR) to facilitate training for industrial maintenance. The objective is the help industry training personnel for maintenance operations at the distance without being obliged to make trainers and training objects travel. Despite the availability of increasingly many and mature VR devices, it is still difficult to achieve the level of realism needed for the effective training of particular manipulation gestures that are vital for specific assembly and disassembly procedures. We propose a systematic concept for a process in a VR-based experimentation environment that facilitates the selection, calibration, and evaluation of different VR devices for the training of specific maintenance operations. We present a concrete implementation of this process for demonstrating the feasibility of our concept. Via virtual hands, users can interact directly with virtual objects via various devices (mouse, keyboard and Leap Motion). Maintenance gestures can be executed as naturally as possible and recorded for facilitating the preparation of work instructions and training sessions.
Design of Festival Sentiment Classifier Based on Social Network

With the development of society, more and more attention has been paid to cultural festivals. In addition to the government’s emphasis, the increasing consumption in festivals also proves that cultural festivals are playing increasingly important role in public life. Therefore, it is very vital to grasp the public festival sentiment. Text sentiment analysis is an important research content in the field of machine learning in recent years. However, at present, there are few studies on festival sentiment, and sentiment classifiers are also limited by domain or language. The Chinese text classifier is much less than the English version. This paper takes Sina Weibo as the text information carrier and Chinese festival microblogs as the research object. CHN-EDA is used to do Chinese text data augmentation, and then the traditional classifiers CNN, DNN, and naïve Bayes are compared to obtain a higher accuracy. The matching optimizer is selected, and relevant parameters are determined through experiments. This paper solves the problem of unbalanced Chinese sentiment data and establishes a more targeted festival text classifier. This festival sentiment classifier can collect public festival emotion effectively, which is beneficial for cultural inheritance and business decisions adjustment.
Doxing as Audience Vigilantism against Hate Speech

Douglas’ chapter further explores these conditions in relation to the possibility of Hine’s (1998) understanding of non-violent vigilantism. Deanonymisation of hate speech through doxing is presented as a viable practice, notably when coupled with de-radicalisation programmes and other forms of the potential reintegration of the target. Vigilantism is always context-specific, and even progressive forms of engagement may raise unanticipated outcomes. Yet the arguments considered in this section are especially helpful in beginning to decouple acceptable from unacceptable forms of denunciation.
Virtual reality based digital chain for creating a knowledge base of hand gestures in maintenance tasks

Hand gestures are key aspects in maintenance work. In this paper, we present a methodology for creating a digital knowledge base of maintenance hand gestures by applying virtual reality technology. Our motivation is rooted in the need to create work instructions that shall illustrate details of hand gestures that are the appropriate for particular maintenance operations. In this research, we propose an approach of recording, classifying and clustering such hand gestures in a library of gesture. Within this library, we can characterize each gesture and relate them to mechanical assembly and disassembly operations with respect to their appropriateness. Through this relationship, we create a knowledge base that can be used for suggesting hand gestures, as well as for rating them in e.g. training tasks. Furthermore, key elements of work instructions for operators can be derived from it. As a demonstration, we showed the process chain applied to the process of registration of hand gesture in simply maintenance task.
Comparative Study of KPower Means, Ant Colony Optimization, Kernel Power Density-based Estimation, and Gaussian Mixture Model for Wireless Propagation Multipath Clustering

In radio communications, channel modeling has a very significant impact.Most of the system's performance relies on the behavior and characteristics of a radio channel.Due to its mobility features, radio channels are time-variant that change over time, making channel characterizations to be dynamic and very challenging.To ensure maximum data rate and reliable communication, accurate channel models are necessary, which requires a correct grouping of wireless multipaths.Accurate clustering of wireless multipath components (MPCs) is essential for cluster-based channel modeling resulting in a more reliable wireless channel system.Currently, determining the best clustering technique is still a challenge as there is no standard way of evaluating and comparing the performance of the various clustering algorithms.This work presents the comparative study on the accuracy performance of the four clustering algorithms namely K-Power Means (KPM), Ant Colony Optimization (ACO), Kernel Power Density-based Estimation (KPD), and Gaussian Mixture Model (GMM) in grouping the wireless MPCs using datasets generated from COST 2100 channel model (C2CM) which represent different Indoor and Semi-urban channel scenarios.Using the Jaccard index, an evaluation metric that compares the calculated data with the reference data, the accuracy of each algorithm is determined as well as their corresponding computational duration.A comparison of their performance is presented, and results show that KPM outperforms other clustering techniques in all channel scenarios of Indoor and Semi-urban environments, making it a right candidate for further improvements to develop a more accurate and computationally efficient clustering technique for wireless propagation multipaths.
Predicting Remediations for Hardware Failures in Large-Scale Datacenters

Large-scale service environments rely on autonomous systems for remediating hardware failures efficiently. In production, the autonomous system diagnoses hardware failures based on the rules that the subject matter experts put in the system. This process is increasingly complex given new types of failures and the increasing complexity in the hardware and software configurations. In this paper, we present a machine learning framework that predicts the required remediations for undiagnosed failures, based on the similar repair tickets closed in the past. We explain the methodology in detail for setting up a machine learning model, deploying it in a production environment, and monitoring its performance with the necessary metrics. We also demonstrate the prediction performance on some of the repair actions.
Strong, Strongly Universal and Weak Interval Eigenvectors in Max-Plus Algebra

The optimization problems, such as scheduling or project management, in which the objective function depends on the operations maximum and plus, can be naturally formulated and solved in max-plus algebra. A system of discrete events, e.g., activations of processors in parallel computing, or activations of some other cooperating machines, is described by a systems of max-plus linear equations. In particular, if the system is in a steady state, such as a synchronized computer network in data processing, then the state vector is an eigenvector of the system. In reality, the entries of matrices and vectors are considered as intervals. The properties and recognition algorithms for several types of interval eigenvectors are studied in this paper. For a given interval matrix and interval vector, a set of generators is defined. Then, the strong and the strongly universal eigenvectors are studied and described as max-plus linear combinations of generators. Moreover, a polynomial recognition algorithm is suggested and its correctness is proved. Similar results are presented for the weak eigenvectors. The results are illustrated by numerical examples. The results have a general character and can be applied in every max-plus algebra and every instance of the interval eigenproblem.
Security Auditing Tools: A Comparative Study

Purpose -This paper concentrates on the comparison of security auditing tools specifying password cracking tools based on different matrices.Passwords are the most popular and dominant means of access control in every authentication process.Every password is vulnerable in the virtual world; all we can do is to delay it for one to break into us.Password cracking used in two opposite intentions; either it can be used for an administrator to protect from unauthorized access and for users to recover forgotten passwords or for an intruder to break into a secure system.Method -A great number of attacks on many systems are related to passwords.Awkwardly, the randomness and length of user-chosen passwords remain the same over time, but in contrast, hardware enhancement continuously gives intruders increasing computational power.So, password cracking has been one of the favorite vulnerable aspects for intruders to gain access to any unauthorized system.Among all available freeware password cracking tools, we choose five renowned tools based on offline and online categories.Results -Cain and Abel is the winner in the offline category, and TCH-Hydra is the winner in the online category in their performance among the tools we have tested. Conclusion -In this paper, the data has been collected by testing each tool several times in different systems as well as all tools in the same system based on different matrices.We have come to a knowledgeable result by comparing data among themselves.The 408 results of the comparison will help in the adoption and usage of these tools and also promote the development and usage of security auditing tools.Recommendations -The results of the comparison will help in the adoption and usage of these tools and also promote the development and usage of security auditing tools.
Expert System for Diagnosing Dental and Mouth Diseases with a Website-Based Certainty Factor (CF) Method

Expert Systems were knowledge-based programs that provided expert quality solutions to problems in a specific domain. The concept of this expert system was a computer program that mimics the thought process and expert knowledge in solving a particular problem. The implementation of expert system was widely used in then health field because the expert system was seen as a way of strong expert knowledge in certain field in a computer program. So that decisions could be given in intelligent reasoning. This work used certainty factor (CF) method implemented in a web-based expert system to diagnose various dental and mouth diseases. The experimental result indicated 99% in accuracy of the testing compared compared to the experts diagnosis.
Poisson denoising under a Bayesian nonlocal approach using geodesic distances with low-dose CT applications

Poisson noise tends to be the primary source of image degradation in several applications. When reducing the exposure time, the image may be severely degraded by noise. One of the strategies is to use adaptations of Non-Local Means for filtering. The NLM algorithm filters an image using a non-local average weighted by a function of the Euclidean distance between two patches of the image. Since geodesic distances induce a metric for the dissimilarity between two distributions, they can be used to compare two patches of an image. This paper alters the NLM algorithm to filter Poisson noise by changing the distance metric with a geodesic one that is more representative of this type of noise, adopting a general, and computationally efficient Bayesian approach. We evaluate the proposed method in the context of low-dose sinogram denoising. Among the geodesic distances evaluated, we found a closed solution for the Shannon entropy for Gamma distributions. Comparisons are made with other NLM strategies as well as state-of-the-art methods, achieving competitive results.
Natural Language Querying System Through Entity Enrichment

This paper focuses on a domain expert querying system over databases. It presents a solution designed for a French enterprise interested in offering a natural language interface for its clients. The approach, based on entity enrichment, aims at translating natural language queries into database queries. In this paper, the database is treated through a logical paradigm, suggesting the adaptability of our approach to different database models. The good precision of our method is shown through some preliminary experiments.
Text-Preprocessing Model Youtube Comments in Indonesian

YouTube is the most widely used in Indonesia, and it’s reaching 88% of internet users in Indonesia. YouTube’s comments in Indonesian languages produced by users has increased massively, and we can use those datasets to elaborate on the polarization of public opinion on government policies. The main challenge in opinion analysis is preprocessing, especially normalize noise like stop words and slang words. This research aims to contrive several preprocessing model for processing the YouTube commentary dataset, then seeing the effect for the accuracy of the sentiment analysis. The types of preprocessing used include Indonesian text processing standards, deleting stop words and subjects or objects, and changing slang according to the Indonesian Dictionary (KBBI). Four preprocessing scenarios are designed to see the impact of each type of preprocessing toward the accuracy of the model. The investigation uses two features, unigram and combination of unigram-bigram. Count-Vectorizer and TF-IDF-Vectorizer are used to extract valuable features. The experimentation shows the use of unigram better than a combination of unigram and bigram features. The transformation of the slang word to standart word raises the accuracy of the model. Removing the stop words also contributes to increasing accuracy. In conclusion, the combination of preprocessing, which consists of standard preprocessing, stop-words removal, converting of Indonesian slang to common word based on Indonesian Dictionary (KBBI), raises accuracy to almost 3.5% on unigram feature.
Biomedical Named Entity Recognition via A Hybrid Neural Network Model

Biomedical named entity recognition (BNER) is one of the primary tasks of analyzing and mining biomedical resources. Recently, major neural network models such as convolutional neural network (CNN) and long short-term memory network (LSTM) have shown better performance than conventional methods in BNER. They do not rely on any feature engineering. However, CNN and LSTM have their own advantages for BNER. Combining them effectively based on these advantages may improve the performance of the BNER model. We present a hybrid model by combining CNN with bidirectional LSTM (BLSTM). The hybrid dilation convolution structure and the attention mechanism are adopted to improve the two networks respectively. They can obtain a wider range of contextual information and increase the weight of significant information. First, we convert the original data into the word vectors. Then the hybrid dilation CNN and the attention-based BLSTM are respectively used to capture features. Next, the output features are integrated by the merge operation. Finally, the conditional random field (CRF) layer is adopted to parse the tags. Compared with some previous single-task BNER methods, our hybrid model obtains the reasonable performance (F1-score:73.72%) on the JNLPBA corpus without any feature engineering, only with public general pretrained word embeddings and simple architecture. To the best of our knowledge, we are the first to adopt the hybrid structure which combines CNN with BLSTM in BNER.
MODEL FOR MONEY MULE RECRUITMENT IN MALAYSIA: AWARENESS PERSPECTIVE

Technology advancement has taken a new shape in leading the world into digital civilization, remarkably in e-commerce, communication, and financial sectors.&nbsp; Taking advantage of the technology, criminals have also digitalized their modus operandi targeting the digital society with fraud and cybercrimes, hence contributing illicit funds. To disguise the money trail to the illegal activities, illicit funds sourced from unlawful and fraudulent activities transformed into legal funds via a money-laundering scheme. Money laundering is perceived as a global threat where funds reverted to the criminal and enter a legitimate economy. To enable the criminals, maintain anonymity, and non-visible to the detection of law enforcement, money mules are positioned in the money laundering chain between actual criminal and illicit funds. Money mules are characters recruited by criminal networks to perform fund transfers by utilizing their accounts. Recruitment is done by offering a job with simple recruitment criteria and attractive income and rewards. This study will examine money mule recruitment and development of a model with related variables to establish the relationship between the aspect of job criteria awareness and the ability of the victims to detect the hidden criminal elements and exposure to law enforcement and derive into the decision to accept the job offer. In this research, both quantitative and qualitative approaches will be employed with surveys and interviews.