Reviewerly/exHarmony huggingface dataset:
On Graph-Based Verification for PDF Table Detection

Many non-editable documents are shared in PDF (Portable Document Format). They are typically not accompanied by tags for annotating the page layout, including table positions. One of the important challenges of the analysis and understanding of such documents is table detection. This paper outlines a novel two-phase approach to the table detection in untagged PDF documents. The first phase uses deep neural networks (DNN) to predict some table candidates. The second phase selects probable tables from the candidates by verifying their graph representation. We build a weighted directed graph from text blocks inside a predicted area of a table. A set of such graphs produced from the “ICDAR 2013 Table Competition” dataset allowed us to train a verification model based on the Random Forest technique. The empirical results for competitive dataset demonstrated high performance of our implementation of this approach. We showed that additional verification enables reduction of errors and improvement of results of the PDF table detection.
Preserving interaction design principles while implementing Material Design Guidelines

The paper presents recommendations for a responsive user interface (UI) implementation. Google guidelines for UI implementation - Material Design, are briefly presented and compared against the Nielsen's design principles. We have identified limitations in preserving interaction design principles while implementing Material Design Guidelines. With objective to achieve flexible and responsive layouts respective improvement recommendations are discussed. Sample design case is presented, illustrated by screens before and after the implementation of the recommendations on Android devices with different resolutions.
Artificial Neural Network based Virtual Energy Meter

The concept of working of virtual meters has been emerged after evolution of IoT technology and cloud computing. In the meanwhile, the neural network models based on Python have also been emerged. In this paper, the concept of artificial neural network based virtual energy meter have been put forward. The logical AND gate based anomaly for virtual meter is presented. Secondly, using neural network based regression method, the novice method for power factor correction have been introduced. The computation of errors and adjustment of synaptic weights has been conducted using Python 3.0 version.
State-space modeling of sound source directivity: An experimental study of the violin and the clarinet

A method is presented for simulating the free-field, frequency-dependent directivity of linear sound sources for use in real-time within geometric acoustic environments. The method, which is applied to modeling the directivity of a violin body and a clarinet air column from experimental acoustic data in this study, is based on using minimum-phase measurements to design a state-space filter, allowing the interactive simulation of a time-varying number of radiated sound wavefronts, each toward a time-varying direction. With applicability in sound synthesis and/or auralization within virtual environments, where sound sources change position and orientation dynamically, techniques are proposed for modeling and simulating directivity profiles on perceptual frequency axes with alternatives for representing directivity on a per-vibration-mode basis while incorporating relative phase terms or by reduced-order efficient representations comprising separate components for the signature resonant structure and the associated directivity on an adjustable frequency resolution.
Hierarchical Self-Attention Hybrid Sparse Networks for Document Classification

Document classification is a fundamental problem in natural language processing. Deep learning has demonstrated great success in this task. However, most existing models do not involve the sentence structure as a text semantic feature in the architecture and pay less attention to the contexting importance of words and sentences. In this paper, we present a new model based on a sparse recurrent neural network and self-attention mechanism for document classification. Subsequently, we analyze three variant models of GRU and LSTM for evaluating the sparse model in different datasets. Extensive experiments demonstrate that our model obtains competitive performance and outperforms previous models.
UUV On-Board Path Replanning using PRM-A

Path planning algorithms for unmanned underwater vehicles (UUVs) are broadly categorized into two sub-areas: global path planning and local path planning. A global path planner employs known information about the operational environment to return a path from the start point to the goal while avoiding any obstacles. Here obstacles are static, such as islands, docks, ship wrecks, etc. The path is determined prior to the UUV's departure. In contrast, a local path planner recalculates the path returned by the global path planner as needed to avoid unexpected moving obstacles such as ships, boats, swimmers, other UUVs, etc. In this paper, we present a novel local path planner for UUVs using an adaptation of the global path planning algorithm PRM-A* discussed in a companion article in this same conference [8]. It is assumed that the algorithm is implemented on-board the UUV and works in conjunction with sensors that can detect the position of the moving obstacles. The proposed local path planner allows the UUV to follow the original path returned by the global path planner, with deviations only as needed to avoid the newly detected obstacles. Thus, only the segment of the path being blocked by the obstacle is avoided, and the original path is otherwise retained. While the focus here has been on UUVs, the same approach can be applied for unmanned surface vehicles (USVs).
Application Research of Quadrotor UAV Motion Planning System Based on Heuristic Search and Minimum-Snap Trajectory Optimization

The design scheme proposed in this paper includes three key aspects, path planning and trajectory optimization, and system state control. First, through a heuristic algorithm based on dynamics optimization, an optimal path is found in the current environment that meets the specific constraints of dynamics. Furthermore, according to the searched path, a polynomial function is used to express its trajectory, and the trajectory is quickly generated by the method of the smallest displacement derivative for optimization, which provides input to the quadrotor UAV control module. By discussing the constraints of the quad-rotor UAV flight, it is transformed into a convex quadratic programming problem for solution. Finally, use the state machine to manage the current state of the drone, and set up the security module for high-frequency collision detection. The ultimate goal is to allow UAVs to independently complete motion planning in unknown environments.
The Art of Dance from the Perspective of Artificial Intelligence

Abstract With the continuous improvement of artificial intelligence application level, computer data processing technology has gradually become the main and classic auxiliary technology application method in the daily work and life of the majority of society members. This article is based on artificial intelligence technology, using computer animation technology, combining artificial intelligence, computer music and graphics disciplines, to show the musical image in the virtual dance of characters, involving the analysis and recognition of musical elements, the establishment of character models, and motion control And other issues. This paper explores the Hidden Markov Model (IIMM, HiddenMarkovModel) model characteristics and algorithm training methods, and proposes an improved HMM training method model algorithm to identify common dance movement models and the control and synthesis of basic dance gestures. This system uses animation as a virtual dance character to show more dance artistic movements for the issues related to the display and scoring of dance movements. Based on the dance movement recognition algorithm and other related algorithms (such as score and error judgment algorithm, error reduction algorithm, etc.), this paper constructs a set of dance movement technical feature statistics and dance movement training. This system realizes the technical statistics and dance training of dance competitions. Two major functions. The above research solves the technical problems of software and hardware engineering from data collection to algorithm programming, and provides a functional platform for intelligent analysis and auxiliary training of dance art, which can be promoted and applied to more professional dancers and dance teachers, thereby accumulating a large amount of data and refining Produce more technical index parameters in line with the rules of dance art, and finally form a set of professional products for dance art intelligence analysis and auxiliary training.
GCNGAN: Translating Natural Language to Programming Language based on GAN

Abstract Cross-language translating has been well solved with the help of the processing of natural language processing(NLP). However, there are a few studies done about the domain of translating the natural language to programming snippets. Traditional method are mostly rule-based and limited to specific areas. In this paper, we propose a model based on Graph Convolutional Network(GCN)-Generative Adversarial Networks(GAN) to translate natural language to programming language. The generator is the encoder-decoder framework in which the encoder and the decoder are all bidirectional RNNs and GCN. And discriminator in GAN is also bidirectional RNNs and GCN. To improve the performance of semantic parsing, we also apply the attention mechanism to it. The experimental results indicate that our model has achieved comparable performance with some other state-of-the-art methods and has the stronger generalization ability.
Bengali Handwritten Isolated Compound Characters Recognition by Applying Transfer Learned Deep Convolutional Neural Network

Optical character recognition (OCR) has been an area of interest for researchers for decades. Many researchers have contributed largely to the development of OCR for script-specific recognition. Handwritten characters recognition has been a big part of this field. Although some literatures have received well-established outcomes, others still haven't experienced remarkable outcomes yet. Despite being the fifth most spoken language of the world by 228 million people, Bengali has not yet received a remarkable contribution to handwritten characters recognition. Some researchers have offered some promising results for basic Bengali handwritten characters recognition but very few researches offered the recognition of Bengali compound handwritten characters. These few researches have applied support vector machine classifier and some deep neural network classifiers for classification but the outcomes were not much satisfactory. In this research, we considered 171 Bengali compound handwritten characters and apply a modified ResNet-50 model for recognition. We achieved an overall accuracy of 96.15% which outperformed the previous best result of 90.33% by a remarkable margin.
Z-Hist: A Temporal Abstraction of Multivariate Histogram Snapshots

Multivariate histogram snapshots are complex data structures that frequently occur in predictive maintenance. Histogram snapshots store large amounts of data in devices with small memory capacity, though it remains a challenge to analyze them effectively. In this paper, we propose Z-Hist, a novel framework for representing and temporally abstracting histogram snapshots by converting them into a set of temporal intervals. This conversion enables the exploitation of frequent arrangement mining techniques for extracting disproportionally frequent patterns of such complex structures. Our experiments on a turbo failure dataset from a truck Original Equipment Manufacturer (OEM) demonstrate a promising use-case of Z-Hist. We also benchmark Z-Hist on six synthetic datasets for studying the relationship between distribution changes over time and disproportionality values.
Student Behavior Recognition in Remote Video Classrooms

With the development of high-definition camera equipment and Internet communication technology, remote video teaching methods have emerged. This new type of teaching method has led to changes in traditional classrooms. On the one hand, remote video teaching has broken the constraints of time and space and effectively improved the inequality of educational resources. On the other hand, in actual video courses, teachers cannot effectively monitor students’ learning actions and states, so they cannot communicate well. In order to solve this problem, this paper proposes an algorithm based on deep learning to monitor the behaviors of students in real time during the learning process, understand the learning status of students, and strengthen the real-time communication between teachers and students.
Image Denoising using Deep Auto-Encoder Network for Production Monitoring in Real-Time

Industrial operations today are being smoothed by monitoring the entire process using latest Image Processing (IP) techniques, especially the bottlenecks in the process. The challenges that need to be addressed in order to commercialize and optimize these solutions are equally growing, particularly in image monitoring of the machining and assembly lines in production plants as well as mechanical workshops. One such hindrance in monitoring the jobs on a Lathe machine or a Computer Numerical Control (CNC) machine is the motion blur noise introduced in the images due to the real-time on field vibrations within the setup. In this paper, this problem is addressed with Image denoising in real time frames. Many practical solutions have been addressing the problem of Image denoising or Image reconstruction using different Computer Vision (CV) techniques in the last two decades. Herein, we propose Image restoration of such blurred image using a specific architecture of Deep Autoencoder network with Convolutional Neural layers so as to obtain a fully constructed image without any prior knowledge of the corresponding clean image. For this we have used an averagely deep encoder-decoder neural network to minimize the typical motion blurring noise that gets introduced in the input image captured by the camera setup on the production lines. This particular technique eliminates the need to identify the type of noise, its intensity or any other statistical characteristics. The results evidently show that the reconstructed images are visually convincing, with large improvements retaining the blur image details.
An improved model of document retrieval efficiency based on information theory

Abstract Literature retrieval is an important auxiliary work of scientific research. The form of literature retrieval is becoming more and more abundant, the number of literature is huge, and the storage set is complex. The algorithm of retrieval tools directly affects the accuracy and efficiency of retrieval. Based on the analysis of the problems existing in various literature retrieval technologies, an improved TF-IDF literature retrieval efficiency model is proposed. It provides a new idea for the design and implementation of literature retrieval system.
A novel linguistic steganalysis method for hybrid steganographic texts

Abstract Most of the existing linguistic steganalysis methods mainly focus on detecting steganographic texts which are generated by embedding secret information into a type of text medium using one steganographic algorithm. But in practical applications, a large number of the steganographic texts may be hybrid ones which are generated by embedding secret information into different types of text media using different steganographic algorithms. In this paper, inspired by transfer learning, a novel linguistic steganalysis method is proposed to detect hybrid steganographic texts. The proposed method first uses the pre-trained BERT language model to obtain initial context-dependent word representations. Then the extracted features are fed into attentional Long Short-Term Memory (LSTM) to get the final contextual representations of sentences. The experimental results show that the proposed method can better satisfy the practical application demands than the existing linguistic steganalysis methods.
The Compromise of Data Privacy in Predictive Performance

Privacy-preservation has become an essential concern in many data mining applications since the emergence of legal obligations to protect personal data. Thus, the notion of Privacy-Preserving Data Mining emerged to allow the extraction of knowledge from data without violating the privacy of individuals. Several transformation techniques have been proposed to protect the privacy of individuals. However, their application does not guarantee a null risk of an individual being re-identified. Furthermore, and most importantly, for this paper, the application of such techniques may have a considerable impact on the utility of data and their use in predictive and descriptive tasks. In this paper, we present a study to provide key insights concerning the impact of privacy-preserving techniques in predictive performance. Unlike previous work, our main conclusions point towards a noticeable impact of privacy-preservation techniques in predictive performance.
Reuse estimate and interval prediction using MOGA-NN and RBF-NN in the functional paradigm

Context: Reusability estimation of a software component is beneficial in constructing a library of reusable components. The inherent characteristics of the functional paradigm in principle support reuse better than other paradigms. In this paper, we consider the reusability of functions written in the Haskell programming language, which is a functional programming language. Objective: We aim to develop a framework for measuring the reusability of Haskell functions. Given a function, we build a model that can provide a reuse estimate of that function using source code metrics derived solely from that function. This developed model provides a prediction interval for the reuse estimate, which is more useful than a point estimate because of the confidence associated with the prediction. This paper also compares two algorithms used for interval prediction viz. MOGA-NN and RBF-NN. Method: We have used the Indegree of a function and probabilities derived from the clustering of functions to construct our reuse estimate. We have used a multiobjective genetic algorithm trained neural network (MOGA-NN) and radial basis function neural nets (RBF-NN) combined with the k-nearest neighbor algorithm for making interval predictions. Results: The developed model successfully predicts the reuse estimate of three open-source Haskell packages. Both MOGA-NN and RBF-NN have quite similar performance in terms of Root mean square error values for training and testing data, but RBF-NN dominates in the prediction of the bounds of the reuse estimate. Conclusion: We have shown how source code metrics computed from a Haskell function can be used to predict the reuse estimate of a given function. We also conclude that radial basis function neural networks are more accurate in providing a prediction interval for reuse estimate.
A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

Summary The sensor nodes localization is very advantageous in wireless sensor network (WSN). This allows effective data transfer between the sensor node networks. Therefore, it saves energy and prolongs network life. Here, a hybrid FWCELM‐CPO method is proposed for solving the sensor node localization problem in WSN. The proposed hybrid method is executed in MATLAB, and the performance is analyzed with different existing algorithms like centroid, fuzzy centroid, and ELM. The simulation results show that the proposed FWCELM‐CPO method attains the higher detection rate of 14.117%, 5.435%, and 11.494%, higher segmentation accuracy of 9.556%, 26.41%, and 16%, lower execution time of 66.667%, 75%, and 70.37%, higher segmented region of 65.957%, 20%, and 44.444%, and higher precision of 34.72%, 18.29%, and 8.78% compared to the existing algorithms. The simulation results demonstrate that the proposed method can be able to find the optimal global solutions efficiently with accurately.
A Research on Different Type of Possible Attacks on Blockchain: Susceptibilities of the Utmost Secure Technology

Blockchain is a type of distributed ledger with a very precise technological support and A distributed ledger is a record that is consensually synchronized and public across various locations, geographies, or institutions, available for many individuals. The blockchain technology observed an extensive implementation and rapid progress in the last decade. A blockchain is a peer-to-peer network to store all the transactions in a decentralized environment with the help of a consensus algorithm. Blockchain helps us to communicate the data in a trustless shared ledger. Even though the ledger is shared in blockchain but the complete network is very secure. In today's scenario, blockchain is very demanding in many application areas like education, healthcare, supply chain, and government services for its unmatchable security features. As various application areas are trying to implement or currently using blockchain to deliver services for the user, the security of the blockchain is extensively worried by the organizations and extremely studied by researchers. Furthermore, the safety and security of blockchain will disturb the security of applications build on the blockchain platform. In this paper, the author discussed about the security and different possible attacks on blockchain. Firstly, the author explained blockchain technology and their building blocks, then explain different types of possible attacks that can be performed on blockchain.
A Survey on Watermarking and Its Techniques

In the present digital era, digital watermarking concept came into demand due to information sharing over Internet. Digital data such as image, voice and video can be easily modified by anyone. Most prone to such malicious attacks are the digital images published on the Internet. Watermarking is the art of hiding relevant information. It is the technique of embedding watermark into the digital records. The application of this is copyright protection, source finding, broadcast tracking, such as watermarked video from global news organization and hidden communication.
Quality Management and Blockchain Adoption in a Supply Chain

In the past, it was difficult to check, identify, and trace product quality. Quality violation regularly takes place when consumers know product quality is overstated. In this paper, we examine the motivation for and implication of a supply chain that adopts blockchain technology to improve product quality in supply chains. We build up a stylish model in which a two-echelon supply chain consisting of one manufacturer and one retailer. The manufacturer decides whether or not to adopt blockchain and the retailer sells products to consumers. Our results imply that the manufacturer always provides a low-quality product without blockchain but when the quality-cost ratio is sufficiently high with the affordable blockchain adoption cost, blockchain technology could encourage the manufacturer to produce high-quality products.