Reviewerly/exHarmony huggingface dataset:
Stackless Processing of Streamed Trees

Processing tree-structured data in the streaming model is a challenge: capturing regular properties of streamed trees by means of a stack is costly in memory, but falling back to finite-state automata drastically limits the computational power. We propose an intermediate stackless model based on register automata equipped with a single counter, used to maintain the current depth in the tree. We explore the power of this model to validate and query streamed trees. Our main result is an effective characterization of regular path queries (RPQs) that can be evaluated stacklessly---with and without registers. In particular, we confirm the conjectured characterization of tree languages defined by DTDs that are recognizable without registers, by Segoufin and Vianu (2002), in the special case of tree languages defined by means of an RPQ.
A computationally intelligent neural network‐based nonlinear autoregressive exogenous balancing approach for real‐time processing in industrial applications using big data

Summary Deep learning based neural networks and their variants have gained popularity due to their inherent flexibility to handle unforeseen especially when a chaotic time series big data are required to be dealt with. There are innumerable applications that are beneficiary of vast interest in computational intelligent approaches that include but not limited to robotics, healthcare, transport, industrial, decision making, and gaming. This paper attempts to investigate the effectiveness of using a neural nonlinear autoregressive with exogenous inputs (NARX) controller in an emerging application field of balancing systems like inverted pendulum (IP) using big data. This paper's aim has been to control an IP cart system by designing a neural NARX controller, and the focus is primarily on real‐time processing in industrial applications grounded on big data ecosystems. In the proposed work, an IP system is mathematically modeled and first controlled utilizing a combination of classical proportional‐integral‐derivative (PID) controllers for cart and pendulum. Second, a chaotic time series input–output data are obtained and are used to train two NARX controllers for cart and pendulum, respectively. Both the controllers are designed as single‐input single‐output systems with one layer each at input and output with suitable number of hidden layers and neurons. Performance comparison of NARX system behavior with PID controller indicates that the NARX controllers successfully adapt to two different kinds of unknown inputs and effectively stabilize the plant. Simulation results confirm that NARX controllers follow the training parameters and exhibit superior performance and overall system stability than PID control. Experimental results demonstrate the effectiveness of the approach.
Trace Representation of &lt;i&gt;r&lt;/i&gt;-Ary Sequences Derived from Euler Quotients Modulo 2&lt;i&gt;p&lt;/i&gt;

We introduce the r-ary sequence with period 2p2 derived from Euler quotients modulo 2p (p is an odd prime) where r is an odd prime divisor of (p-1). Then based on the cyclotomic theory and the theory of trace function in finite fields, we give the trace representation of the proposed sequence by determining the corresponding defining polynomial. Our results will be help for the implementation and the pseudo-random properties analysis of the sequences.
Artificial intelligence and database technology work together

Abstract Ai - database integration is an effective application for many artificial intelligence technologies as well as major technologies for database development, and for the next generation of computing It supports intelligent information systems based on distributed cooperative tasks. The relationship between AI and large data and the potential values of Bayes in AI are described.
Skill Requirements Analysis for Data Analysts Based on Named Entities Recognition

Currently, with the surge in demand for data analysts, it is beneficial to grasp the social requirement for talents by deeply mining the job requirements of data science. This study used crawlers to acquire the job advertisements from 51job.com and selected data science related positions. Then, the specialty and skill entities in job requirement were annotated manually, and two kinds of Named Entities Recognition models, the Bert-BiLSTM-CRF model and BiLSTM-CRF were applied to extract skill entities and specialty entities from the text data of job requirement. It was found that, the model based on Bert pre-training vector was better. At last, the characteristics of the demand from the perspective of skills and profession were analyzed.
Image generation using generative adversarial networks

Ever heard of generation of image datasets, human faces, cartoon characters, 3D objects, image-to-image and text-to-image translation, face aging, photo blending, and others? How are the computers able to perform the tasks by achieving mastery results? Yes, the answer to all these tasks is generative adversarial networks (GANs). GANs are an amazing artificial intelligence innovation fit for making pictures, sound, and recordings that are unclear from the real thing. GANs employ self-supervised learning consisting of two neural networks. The setup includes two neural network systems in opposition to one another—one to create fakes (generator) and one to spot them (discriminator). The term generative indicates the idea of creating new data depending on the training data. The term adversarial indicates a gamelike framework with two networks, i.e., generator and discriminator. The generator produces the realistic data, which is similar to the training data, whereas the discriminator's task is to identify fake data produced by the generator from the real data coming from the training sample. The chapter is divided into three sections providing a brief introduction about generative deep learning and variational autoencoder giving the flexibility of data generation with slight variations compared to the original data followed by the workflow, training problems, and real-world applications using different flavors of GANs.
Genetic Algorithm for Image Recognition in Smart Systems Applications

Image recognition in smart systems and internet of things applications is rapidly developing. Significant advances in mobile computing technology and machine learning are expanding horizons to use image recognition in smart systems and IoT applications. This paper use the smart system based image matching system utilizing genetic algorithm to design an integrated service to improve the management of image recognition. An efficient genetic algorithm used to match an unknown pattern which represents a partial image with original image. This technique uses a parameter, namely, chromosome. It transformed the spatial coordinates position into a sequence of bits of small length depending on the dimension of the original image, whereby the matching will be obtained in a short time. The system was tested on a sample images showed high matching rate (97%).
Approximate query processing over static sets and sliding windows

Indexing of static and dynamic sets is fundamental to a large set of applications such as information retrieval and caching. Denoting the characteristic vector of the set by B , we consider the problem of encoding sets and multisets to support approximate versions of the operations rank ( i ) (i.e., computing ∑ j ≤ i B [ j ] ) and select ( i ) (i.e., finding min ⁡ { p | rank ( p ) ≥ i } ) queries. We study multiple types of approximations (allowing an error in the query or the result) and present lower bounds and succinct data structures for several variants of the problem. We also extend our model to sliding windows, in which we process a stream of elements and compute suffix sums . This is a generalization of the window summation problem that allows the user to specify the window size at query time . Here, we provide an algorithm that supports updates and queries in constant time while requiring just ( 1 + o ( 1 ) ) factor more space than the fixed-window summation algorithms.
Image High Frequency Information Restoration Algorithm Based on Deep Learning

Abstract In order to effectively recover the high-frequency information of images, this paper proposes a deep neural network based on feature compensation to reconstruct super-resolution images. In this method, intensive deep convolutional neural network and residual network are combined, and the high-frequency information of the original image is extracted separately after up sampling and fused with the reconstructed super-resolution image to form high-frequency feature compensation, which can improve the image quality. Through experimental comparison, the effect of the super resolution image reconstructed by the proposed algorithm is improved by about 1dB compared with that reconstructed by the SRCNN algorithm.
Local and Global Feature Based Hybrid Deep Learning Model for Bangla Parts of Speech Tagging

Parts of Speech (pos) tagging corresponds to techniques of tagging accurate pos tags of each word of a sentence. It is a pivotal and indispensable part of sentiment analysis, synthesizing text to speech, analyzing textual data and many more Natural Language Processing (NLP) tasks. Though there are many researches done in the field of languages like English, Chinese etc., it is still comparatively unexplored for Bangla. An excellent pos tagger in Bangla language can accelerate NLP of Bangla textual data. In this study, we have developed a novel hybrid Deep Learning (DL) model with two parallel input networks with Convolutional Neural Network (CNN), and Bidirectional Long Short Term Memory (BiLSTM), and an output network of CNN. CNN works well regarding the local dependencies of words in sentences, and BiLSTM works well with the global dependencies of words regarding the context in a sentence. No other work has been done with local features of textual data along with long term word dependencies in Bangla pos tagging studies. The model can work with any external Word Embeddings. In this study, it is implemented with both Embedding Layer and Word2Vec layer. In the evaluation process, Mathews Correlation Coefficient (MCC), Precision-Recall curve are used along with accuracy and F1-score due to the dataset's imbalanced characteristic. Our proposed model accompanied by Word2Vec layer has outperformed Vanilla Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), BiLSTM networks, and also one recent study using the same dataset with the highest accuracy of 0.974, F1-score of 0.883, and MCC value of 0.936.
Quantitatively Examining Service Requests of a Cloud-Based On-Demand Cybersecurity Service Solution for Small Businesses

With the rise of cybercrimes, limited by the funding and in-house technical resource, the small business sector is already known for struggling with cybersecurity-related issues. Regardless of 65% acknowledging they have been the target of a cyberattack and 86% believing digital risk will upsurge, only 4% of small and medium-sized business owners have executed all of the U.S. Small Business Administration's cybersecurity best practices, rendering to a survey by Nationwide [1]. To meet this challenge, we have proposed a cloud-based on-demand cybersecurity service solution for small businesses (CODCSSSB) to provide a cost-effective cybersecurity resolution for small businesses. This paper has explored how to apply a quantitative examination approach to validate the security service requests sent to and processed by the CODCSSSB to discover the weakness of design with low cost in terms of the time of development and accuracy of identifying the root cause of the design issues.
Improved Model Compression Method Based on Information Entropy

The rapid development of deep learning has promoted more and more complex neural network models that require high computing power. Even though researchers have proposed various lightweight network models such as MobileNet, SqueezeNet and ShuffleNet, the amount of calculation is still huge. In order to further reduce the amount of model calculations, model compression is an effective means to reduce the amount of model parameters and calculations. Channel pruning is the most effective and direct means to accelerate model calculations and reduce model parameters. However, due to its radical approach, the effect of pruning is affected by the basis for determining the importance of the channel, and the accuracy cannot be guaranteed. Furthermore, pruning When the filter is smaller than the set threshold value is completely deleted, it is possible to discard important parameters. Therefore, this article intends to propose a channel pruning model compression method based on information entropy. The actual test results give convincing experimental results, which prove the effectiveness and practicability of the method.
Methods of Reducing Metal Damager Defect in Back End of Line for Semiconductor in 28Nm Technology

As technology keeps shrinking to 28nm and below, airborne molecular contamination has become a critical element of cleanroom management, which would influence the performance of device, but also decrease the yield and productivity in the semiconductor manufacturing process. As we know that in the Fab, wafers are always stored in the Front-Opening-Unified-Pod (FOUP) while waiting for process. The outgassing or contamination distribution within the FOUP has a strong negative impact on wafers. So additional precautions need to be taken to prevent external contamination during FOUP door opening or closing. This study investigated that metal damager defect appeared after the Cu electroplating (ECP) process in M1 feature structure. It was susceptible that Cu seed exposing to atmosphere was easy to be damaged by ambient gas. Here we illustrated two methods to reduce the metal damager defect. Firstly, Annealing process was applied before Buried Seed deposition, which could help remove the outgassing on the surface of wafer. Secondly, using the diffuser FOUP during the process of Via Etch to ECP process, and making sure the copper was in a super pure gas environment. The experiments consistently demonstrated that using diffuser FOUP and simultaneously introducing Annealing before Buried Seed could significantly improve metal damager defect.
DS<sup>2</sup>: Handling Data Skew Using Data Stealings over High-Speed Networks

Distributed in-memory computing systems have dramatic performance improvement over traditional disk-based systems, which makes them widely used in large-scale data processing applications. Unfortunately, uneven and unpredictable data distributions caused by data skew have a significant impact on the performance. In Spark, when data skew happens, some tasks will process much more data than other tasks and become the performance bottleneck. The traditional approaches to handling data skew are based on sampling and repartitioning, which incur additional overhead. In this paper, we divide data skew in distributed data processing systems into intra-node and inter-node skew. Based on data stealing, we proposed DS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to handle both intra-node and inter-node data skew. It aims to improve the performance under data skew, without involving additional overhead. DS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> first balances the skewed data distribution in the local and then handles the inter-node skew by RDMA during execution. It achieves up to 2.96× speedup on the aggregation operator and 2.81× speedup on the join operator.
Single Shot Multi-Face Detection &amp;amp; Gender Recognition

The method is proposed single shot face detection and gender recognition using Convolutional Neural Network(CNN).The proposed method is using the YOLO algorithm to detect the human face and make gender recognition. The face detection and gender recognition are very interesting since the past two decades. This can be used in future for the security purpose, biometric, digital cosmetic and many more. As human face is a dynamic object having a high degree of variability in its appearance, that make the face detection problem difficult in the computer vision task. The goal of this paper is to multiple face detection and its gender recognition in one shot of image is passed in the network and give the better performance in term of speed and accuracy.
An Improved Cultural Algorithm for Scheduling of Precedent Tasks

In order to efficiently process some tasks with precedent constraints, a cultural algorithm based on greedy stochastic process is proposed. The improved cultural algorithm firstly gives an initial population with obvious features, which provides a precondition for accelerating the convergence speed of cultural algorithm. The cultural algorithm introduces the belief space, in which some excellent features produced by generations of individuals are stored. The culture formed by these features and the group space can influence each other, thereby improving the convergence speed and the overall optimization ability. The final simulation results show that the cultural algorithm based on greedy stochastic process can obtain a relatively better scheduling scheme in an acceptable time.
Correcting Perspective Distortion In Incremental Video Stitching

In video stitching, only incremental video stitching technology can typically be used due to the limitations of computer memory and processing speeds. However, if frames with perspective distortion are selected as reference planes, perspective distortion will accumulate continuously during incremental video stitching, making the resulting panorama image appear unnatural. Existing solutions to correct perspective distortion rely on the content of scenes or external hardware, which lack generalization and yield poor performance when stitching UAV (unmanned aerial vehicle) videos. This paper proposes a novel type of feature called “displacements of matching feature points” to correct perspective distortion. A displacement vector composed of matching feature points on two adjacent frames contains the information of perspective distortion, which allows us to correct any perspective distortion. We correct the displacements of matching feature points with perspective distortion in a reference plane to one without perspective distortion. Then, this plane is taken as a global reference plane in incremental video stitching to further correct the panorama. Using the proposed method, correction of perspective distortion is no longer dependent on image content or computational hardware. Experimentally, the proposed method performs better than other state-of-the-art methods, yielding more natural panoramas.
Role of Cryptocurrency in Digital Marketing

This chapter explores the role of cryptocurrency in digital marketing. Throughout the most recent years, cryptocurrency has developed, both in worth and ubiquity. Indeed, numerous industry leaders trust that cryptocurrency can change money and promote it until the end of time. In any case, as computerized cash, bitcoin turns out to be more ordinary ; cryptocurrency may introduce a few issues for advertisers hoping to gather shopper information. The cryptocurrency market is an appropriate environment dependent on the distributed network innovation. Decentralization is a distinguishing characteristic of this framework, and it is an impression of how there is no national bank or another case that authorizes power over the organization. The exchanges are led and checked to employ an appropriated blockchain system that relies upon clients' assets called diggers.
Pruning Techniques in LinCbO for Computation of the Duquenne-Guigues Basis

We equip our algorithm LinCbO with a pruning technique similar to that of LCM. Our experimental evaluation shows that it significantly improves the performance of the algorithm.
Chinese Verb-Object Collocation Knowledge Graph Construction and Application

Verb is the core of a sentence. It can not only reflect the syntactic structure and semantic framework of the whole sentence, but also restrict the nominal elements which co-exist with them. They play a significant role in sentence. Verb-Object Collocation has received more and more attention owing to its high frequency, complexity and flexibility of using. Domestic researches on verb object collocation mainly focus on automatic recognition and construction of corresponding collocation knowledge base. Nevertheless, most of the existing Verb-Object Collocation knowledge base lacks semantic information and classification information. Thus, the application scenarios will be greatly limited. It cannot meet the semantic needs of natural language processing, either. In view of this situation, this paper deeply analyzes the semantic relationship of Verb-Object Collocation, and uses the related technology of knowledge graph to construct a Verb-Object Collocation Knowledge Graph, which obtains 40704 specific examples of Verb-Object Collocation and 20000 extraction templates of Verb-Object Collocation. At the same time, this paper constructs a Verb-Object Collocation Knowledge Graph to manage and maintain our knowledge through two parts: ontology layer and data layer. Finally, the effectiveness of the Verb-Object Collocation Knowledge Graph is verified on the task of entity recognition.
Selection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic

Abstract PPA (Academic Achievement Improvement) scholarship acceptance Institut Teknologi Del is open annually for students who have improved academic performance but are economically disadvantaged. Every year there is significant increasing of the number of students to apply for the scholarship. As a result, it becomes more difficult to determine students who are entitled to receive the PPA scholarship since the procedure was done manually. As a result, the selection time needed to produce a decision is longer. In response to this, the authors propose the implementation of Genetic Algorithms and Fuzzy Logic in determining scholarship recipients as well as using data from the Institut Teknologi Del Information System (CIS) using student data of 2016, 2017 and 2018. The data obtained is done by data pre-processing, then being input into the system. In the system the state has been set with CR = 0,9, MR = 0,1 and fuzzy parameters will be used. Following the genetic algorithm stages of population initialization, fitness evaluation, selection, crossover, mutation, and elitism, the fuzzy logic process is carried out in the form of fuzzyfication, inference and defuzzyfication. After the fuzzy process is completed, 10 scholarship recipient student data are obtained. Since the results of the membership function provided by the genetic algorithm always changed, it caused different result in each experiment. Four data similar to the original data of the scholarship recipient was finally shown in the 37 th experiment from the total of 50 experiments carried out.
Augmented Reality as a Medium for Learning Measurements and Quantities

Abstract This study aims to provide students with a special understanding of material magnitude and measurement by utilizing augmented reality. The method in this research utilizes natural feature tracking-Multi Marker Target, as well as system development model namely ADDIE model consisting of Analysis, Design, Develop, Implementation, Evaluate. As well as using the Fast Corner algorithm. The research resulted in an application in the form of displaying 3D objects of layer magnitude and measurement. The designed UI is as attractive as possible for use in students. The application can run well on android operating system. The results of this study obtained the response of students got an average score of 81.87% with the category “Excellent”, so this media deserves to be used as a learning medium based on augmented reality in the field of Material Physics Magnitude and Measurement.