Reviewerly/exHarmony huggingface dataset:
Captcha Recognition Based on Deep Learning

The captcha is a Turing test used to distinguish between machines and humans. It is considered as the verification code for the security on many websites. In recent years, deep learning has been widely used in the related field such as data analysis and computer vision. By applying deep learning method to captcha recognition, the vulnerabilities of the current captcha can be discovered, and the captcha can be further improved to reach a higher security level. This paper focuses on the captcha recognition based on deep learning, which is conducive to find the loopholes of current captcha to design more secure captcha. In this paper, the AlexNet neural network is analyzed in detail first, then the model is implemented to achieve captcha recognition for the simple and complex captcha data. The experiment results show that the recognition accuracy of the simple captcha and complex captcha data is close to 99%, and captcha recognition based on deep learning has a satisfied effect for recognition of captcha with different complexity.
Improvement of Mathematical Tools for Controlling the Group of Unmanned Aerial Vehicles

The task of controlling unmanned aerial vehicles to solve the problems of territorial connectivity is updated. A complex of interrelated problems of organizing a joint flight of a group of aircrafts is formulated. A mathematical toolkit for calculating the control of an unmanned aerial vehicle that allows solving the problems of both the joint safe synchronous functioning of a group of aircrafts and their individual flight has been developed. To expand the intellectual capabilities of the designed technical system and to ensure human communication with it in natural language as well, it is proposed to use the apparatus of the theory of fuzzy sets that formalizes the operation with linguistic variables and fuzzy concepts. For immersion of flying objects into physical space the concepts of "friend", "foe" are introduced. Using the theory of pattern recognition, the mechanism of identification and operational response of aircrafts to various non-standard and force majeure situations is revealed. The concept of "traffic rules" for aircrafts is introduced and the meaning of their formation and use in airspace is revealed. The possibilities of using game theory for solving conflict situations are analyzed. The place of the catastrophe theory in mathematical description of the processes of aircrafts interaction with each other and with "nature" is indicated. The totality of flying vehicles is considered from the perspective of the theory of multi-agent systems and the theory of active systems that forms emergent (ant, swarm) intelligence in their environment.
Photovoltaic Power Prediction Method Based on Markov Chain and Combined Model

Photovoltaic power generation prediction is very helpful for real-time balanced operation in the power market, which in turn will benefit both energy suppliers and customers. Aiming at the prediction of photovoltaic power generation, the paper proposes a prediction method based on Markov chain and combination model. First, this paper preprocesses the data. Then, taking into account the variability and intermittency of photovoltaic power generation, discrete wavelet transform is used to de-noise the data. Next, the paper uses random forest for feature selection to extract feature vectors that are highly related to photovoltaic power generation. Finally, different prediction models are combined, and the weights of different prediction models are dynamically determined through Markov chains. The linear combination of the prediction values of the different prediction models under the dynamic weights is used as the final prediction value. The method in this paper is compared with other methods on real photovoltaic data sets. The experimental results show that the proposed method has a better prediction effect.
Automatic Detection of Malicious URLs using Fine-Tuned Classification Model

URLs are frequently used to surf the Internet in modern society. Especially after the outbreak of the COVID-19, quarantine makes needs for the Internet and usages of URLs reach an unprecedented level. Unfortunately, not every URL is believable, because some of them can attack your computer, steal your personal information, and even spread computer virus like Trojan. This project designs machine learning algorithms to detect malicious URLs efficiently and protect Internet users from malicious URLs. In this project, the goal is to get a fine-tuned machine learning model, I will first introduce the dataset of URLs which is crucial to train the model. Then I will show the procedure and some findings of my data exploration. After that, I will present the methods including the algorithms and some improvements I make to optimize my model. Finally, I will show the results and the conclusion. To make my models better, I not only apply hyperparameter tuning, but also data resampling and cross validation to the model. The procedure is repeated several times to ensure the stability. In order to evaluate the performance of my models accurately, I adopt multiple methods. After improving the algorithms, by using the F1 score to evaluate performance, the result boosts significantly from original 0.14 to around 0.90. With the ultimate well-trained model, we can predict the safety of all the URLs on the Internet accurately, which can secure our personal information and data.
Blockchain-Assisted Collaborative Service Recommendation Scheme With Data Sharing

With the rapid development of cloud computing, a large number of web services have been emerging quickly, which brings a heavy burden for users to choose the services they preferred.In order to suggest web services for users, recommendation algorithms are needed and many of them have been investigated recently.However, most of the existing recommendation schemes are based on centralized historical data, which may lead to single point of failure.Generally, the data contains a lot of sensitive information that cloud may expose the privacy of users, which makes most cloud platforms reluctant to share their own data.In order to solve the above issues, the secure data sharing among cloud platforms is necessary for better recommendation, which can maximize the profits.In this paper, we propose a blockchain-assisted collaborative service recommendation scheme (BC-SRDS).Specifically, we adopt the ciphertext-policy attribute-based encryption (CP-ABE) algorithm to encrypt the data, which ensures the data confidentiality and realizes secure data sharing.Then, we utilize the blockchain to share data, such that the DoS attack, DDoS attack and single point of failure can be avoided.Meanwhile, the data integrity, tampering-proof of data are guaranteed through the blockchain.And we use locality-sensitive hashing algorithm to recommend the services for users.Finally, it is proved through the security analysis that BC-SRDS is capable of achieving data confidentiality, data integrity and tampering-proof.A series of experiments show that BC-SRDS achieves better recommendation accuracy compared with the existing schemes.
Improving Semantic Coherence of Gujarati Text Topic Model Using Inflectional Forms Reduction and Single-letter Words Removal

A topic model is one of the best stochastic models for summarizing an extensive collection of text. It has accomplished an inordinate achievement in text analysis as well as text summarization. It can be employed to the set of documents that are represented as a bag-of-words, without considering grammar and order of the words. We modeled the topics for Gujarati news articles corpus. As the Gujarati language has a diverse morphological structure and inflectionally rich, Gujarati text processing finds more complexity. The size of the vocabulary plays an important role in the inference process and quality of topics. As the vocabulary size increases, the inference process becomes slower and topic semantic coherence decreases. If the vocabulary size is diminished, then the topic inference process can be accelerated. It may also improve the quality of topics. In this work, the list of suffixes has been prepared that encounters too frequently with words in Gujarati text. The inflectional forms have been reduced to the root words concerning the suffixes in the list. Moreover, Gujarati single-letter words have been eliminated for faster inference and better quality of topics. Experimentally, it has been proved that if inflectional forms are reduced to their root words, then vocabulary length is shrunk to a significant extent. It also caused the topic formation process quicker. Moreover, the inflectional forms reduction and single-letter word removal enhanced the interpretability of topics. The interpretability of topics has been assessed on semantic coherence, word length, and topic size. The experimental results showed improvements in the topical semantic coherence score. Also, the topic size grew notably as the number of tokens assigned to the topics increased.
A path planning model of a tiltrotor for approaching an aircraft carrier during landing

A path planning model concerning a tiltrotor approaching an aircraft carrier is established in this study. In the model, the characteristic of the tiltrotor, the landing task, and the environment of the carrier are taken into account. First, the motion equations and the maneuverability of the tiltrotor in each flight mode are presented, and the constraints of control variables and flight envelope are given. The returning flight of the tiltrotor is divided into three phases corresponding to the three flight modes of the tiltrotor, and the constraints in each phase and the goal are set. Considering the flight safety of the tiltrotor, the environment of the carrier is described as flyable space and no-fly zones, and the no-fly zones are set taking the influences of turbulence and wind field induced by the moving aircraft carrier into account. The path planning issue is formulated into an optimization problem under the constraints of control variables and state variables. According to the characteristic of the established model, a pigeon inspired optimization (PIO)-based path planning algorithm is developed integrating the “step-by-step” and “one effort” path search strategies. Simulation results demonstrate that the tiltrotor can reach the target point with a reasonable landing path. Comparison among different algorithms is also conducted to verify that the PIO algorithm is capable of solving this online path planning problem.
One-Shot learning based classification for segregation of plastic waste

The problem of segregating recyclable waste is fairly daunting for many countries. This article presents an approach for image based classification of plastic waste using one-shot learning techniques. The proposed approach exploits discriminative features generated via the siamese and triplet loss convolutional neural networks to help differentiate between 5 types of plastic waste based on their resin codes. The approach achieves an accuracy of 99.74% on the WaDaBa Database [1].
Attentive Excitation and Aggregation for Bilingual Referring Image Segmentation

The goal of referring image segmentation is to identify the object matched with an input natural language expression. Previous methods only support English descriptions, whereas Chinese is also broadly used around the world, which limits the potential application of this task. Therefore, we propose to extend existing datasets with Chinese descriptions and preprocessing tools for training and evaluating bilingual referring segmentation models. In addition, previous methods also lack the ability to collaboratively learn channel-wise and spatial-wise cross-modal attention to well align visual and linguistic modalities. To tackle these limitations, we propose a Linguistic Excitation module to excite image channels guided by language information and a Linguistic Aggregation module to aggregate multimodal information based on image-language relationships. Since different levels of features from the visual backbone encode rich visual information, we also propose a Cross-Level Attentive Fusion module to fuse multilevel features gated by language information. Extensive experiments on four English and Chinese benchmarks show that our bilingual referring image segmentation model outperforms previous methods.
Automatic extension of corpora from the intelligent ensembling of eHealth knowledge discovery systems outputs

Corpora are one of the most valuable resources at present for building machine learning systems. However, building new corpora is an expensive task, which makes the automatic extension of corpora a highly attractive task to develop. Hence, finding new strategies that reduce the cost and effort involved in this task, while at the same time guaranteeing quality, remains an open and important challenge for the research community. In this paper, we present a set of ensembling strategies oriented toward entity and relation extraction tasks. The main goal is to combine several automatically annotated versions of corpora to produce a single version with improved quality. An ensembler is built by exploring a configuration space in search of the combination that maximizes the fitness of the ensembled collection according to a reference collection. The eHealth-KD 2019 challenge was chosen for the case study. The submitted systems' outputs were ensembled, resulting in the construction of an automatically annotated collection of 8000 sentences. We show that using this collection as additional training input for a baseline algorithm has a positive impact on its performance. Additionally, the ensembling pipeline was used as a participant system in the 2020 edition of the challenge. The ensembled run achieved a slightly better performance than the individual runs.
Erasable Virtual HyperLogLog for Approximating Cumulative Distribution over Data Streams

Many real-world datasets are given in the stream of entity-identifier pairs, and measuring data distribution on these datasets is fundamental for applications such as privacy protection. In this paper, we study the problem of computing the cumulative distribution for different cardinalities (i.e., the number of distinct entities owning the same identifier). However, previous sketch-based methods cost large memory space especially when there are a large number of identifiers, and sampling-based methods require much time for cardinality estimation. A recent work KHyperLogLog combines both sketch and sampling methods but it is wasteful to separately build a HyperLogLog sketch of large size for identifiers with small cardinalities. To address these challenges, we propose a memory-efficient method EV-HLL, which designs a shared structure to store all sampled identifiers and their entities and utilizes additional sketches to track value updates during the sampling procedure. Meanwhile, EV-HLL provides real-time unbiased estimations according to value changes whenever a new entity-identifier pair arrives. We evaluate the performance of EV-HLL and other state-of-the-arts on real-world available datasets. Experimental results demonstrate that comparing to other methods, EV-HLL effectively reduces their memory usage with the same estimation accuracy and has higher accuracy with the same memory usage.
Research of security and safety culture within an organization. The case study within the Slovak Republic

Abstract Security and safety culture is an increasingly important term in assessing the security and safety of organizations. It is an internal security and safety factor of an organization that can directly influence its overall security and safety. In many cases, however, organizations do not know, nor do they have a way of assessing the level of their security and safety culture. It is therefore necessary to know the content, elements, and sectors in which it can be identified in order to gain a deeper knowledge of security and safety culture in more detail and be able to examine it. Firstly, we focused on creating a model for evaluating the safety and safety culture and its application in selected organizations. Secondly, we devoted to the description and design of an organization’s security and safety culture assessment model. The proposed model is based on the identification of subsectors and indicators of security and safety culture that will gain their value by using information obtained from combining the four proposed methods. Accordingly, six security and safety culture sectors were defined for the purposes of security and safety culture assessment in selected organizations. These were compared to each other to determine their mutual weight. Finally, using these research methods, security and safety culture indicators were also identified and included in the defined security and safety culture sectors. The research needed to design a security and safety culture assessment model was realized in six selected organizations, which wanted to participate in the research. These were divided according to the size of the organization into two small, two medium, and two large organizations. Crucial element in our research was data collection conducted by means of controlled interviews and questionnaire surveys in these organizations. Based on this survey, indicators of security and safety culture could be identified. As a result of this, security and safety culture values are determined for all analyzed organizations. The conclusions will point up the possible way of evaluating security and safety culture in organizations and it also contains a specific example of their usage in the practice. Thus, by using the resulting values, it is possible to decide the further direction for the organizations, focusing on the sectors of the security and safety culture, which yielded unsatisfactory results.
Evaluating Design Choices for Deep Learning Registration Networks

The variety of recently proposed deep learning models for deformable pairwise image registration leads to the question how beneficial certain architectural design considerations are for the registration performance. This paper aims to take a closer look at the impact of some basic network design choices, i.e. the number of feature channels, the number of convolutions per resolution level and the differences between partially independent processing streams for fixed and moving images and direct concatenation of input scans. Starting from a simple single-stream U-Net architecture, we investigate extensions and modifications and propose a model for 3D abdominal CT registration evaluated on data from the Learn2Reg challenge that outperforms the baseline network VoxelMorph used for comparison.
Multidimensional Development and π-Type Trend of the Blockchain Research: A Collaborative Network Analysis

With the rapid growth of Internet technology, the blockchain has been developed fast in recent years with the wide expansions in finance, medicine, public welfare, and other fields. At the same time, the blockchain technology has also attracted researchers’ increasing attention, leading to a large number of scientific outputs that made significant contributions to blockchain research. However, few attempts can be found to investigate the development of blockchain based on the literature in this area. Therefore, this paper focuses on blockchain research by introducing bibliometric methods and comprehensively analyzes its status quo, emerging trends, and development path. Based on the above research, this paper draws two main conclusions. First, we made a comprehensive investigation of blockchain research in the past five years and then found that the main research directions in this area are Bitcoin, digital currency, and Fintech. Second, the development of blockchain research generally presents a trend of π-transformation based on the main path analysis. The above studies provide a brief view for beginners who want to learn blockchain research in a direct way and contribute to extend the theoretical foundation of blockchain research by giving a comprehensive bibliometric analysis.
Car Traffic Sign Recognizer Using Convolutional Neural Network CNN

Acknowledgment of traffic signs vary significantly in numerous applications, for example, in self-driving vehicle/driverless vehicle, traffic planning and traffic observation. Traffic Sign Recognition (TSR) framework is a segment of Driving Assistance System (ADAS). The TSR framework helps the drivers in safe driving as street signs give significant data of the street. The car business has built up a great deal and a portion of the organizations are attempting to assemble self-sufficient vehicles and in which traffic sign acknowledgment is one of the significant factors to be thought of. To perceive the traffic signs, a model utilizing convolutional neural network is fabricated and this model will perceive the traffic signs. This model can likewise be utilized in typical vehicles to caution the driver about traffic signs through content identification.
Gradient Coding With Dynamic Clustering for Straggler-Tolerant Distributed Learning

Distributed implementations are crucial in speeding up large scale machine learning applications. Distributed gradient descent (GD) is widely employed to parallelize the learning task by distributing the dataset across multiple workers. A significant performance bottleneck for the per-iteration completion time in distributed synchronous GD is straggling workers. Coded distributed computation techniques have been introduced recently to mitigate stragglers and to speed up GD iterations by assigning redundant computations to workers. In this paper, we introduce a novel paradigm of dynamic coded computation, which assigns redundant data to workers to acquire the flexibility to dynamically choose from among a set of possible codes depending on the past straggling behavior. In particular, we propose gradient coding (GC) with dynamic clustering, called GC-DC, and regulate the number of stragglers in each cluster by dynamically forming the clusters at each iteration. With time-correlated straggling behavior, GC-DC adapts to the straggling behavior over time; in particular, at each iteration, GC-DC aims at distributing the stragglers across clusters as uniformly as possible based on the past straggler behavior. For both homogeneous and heterogeneous worker models, we numerically show that GC-DC provides significant improvements in the average per-iteration completion time without an increase in the communication load compared to the original GC scheme.
Research on Intrusion Detection Method of Key Metro Areas based on YOLOv3

The metro station is one of the public places with the largest flow of people. It is very important for passengers and station staff to detect whether there is any intrusion in its key area. There are many defects and deficiencies in the past human detection methods. With the popularization and development of computers and the rise of deep learning methods, a large number of visual detection methods have emerged and gradually occupy an important position in the field of video detection. In this paper, we propose a regional intrusion detection method, based on the YOLOv3 target detection method of the deep learning method. The experiment is carried out on the monitoring video of a metro station to test the reliability of our method. The final experimental results show that our detection method has the advantages of fast detection speed, high detection precision, and can complete the detection task as well.