Reviewerly/exHarmony huggingface dataset:
Preliminary design of wave energy utilization model simulation for buoy type seawater pumps

Abstract This study aims to obtain a preliminary design of simulation of a wave-powered seawater pump model with a buoy &amp; vertical motion piston. The concept works are the mechanism of pumping seawater through the piston push and the apparent motion of the buoy up and down by the waves. The hypothesis, that the flow rate and pumping head as research variables were strongly influenced by wave height and period (H &amp; T). Also, it was estimated that it was influenced by the weight and dimensions of the buoy (W &amp; A) and the diameter of the piston tube. Preliminary experiments are carried out to see the performance of the test model and select the experimental variable magnitudes that are adjusted to the capacity of the available equipment and the scale of the model for similarity needs to the prototype conditions. This study will use a wave flume of 15 m long, 30 cm wide, and 50 cm effective height. The wave characteristics of the model to be used are the wave period in 3 variations (T1: 1.3 seconds, T2: 1.4 seconds, T3: 1.5 seconds), the range of wave heights in 3 variations (4 cm, 5 cm, and 6 cm) and water depth in 3 variations (d1: 25 cm, d2: 27 cm, d3: 29 cm). From the simulation design of this model, it is expected that the equation of the discharge and the height of the pumping head will be formulated in the form of dimensionless parameter relationships.
Intra-Model Collaborative Learning of Neural Networks

Recently, collaborative learning proposed by Song and Chai has achieved remarkable improvements in image classification tasks by simultaneously training multiple classifier heads. However, huge memory footprints required by such multihead structures may hinder the training of large-capacity baseline models. The natural question is how to achieve collaborative learning within a single network without duplicating any modules. In this paper, we propose four ways of collaborative learning among different parts of a single network with negligible engineering efforts. To improve the robustness of the network, we leverage the consistency of the output layer and intermediate layers for training under the collaborative learning framework. Besides, the similarity of intermediate representation and convolution kernel is also introduced to reduce the reduce redundant in a neural network. Compared to the method of Song and Chai, our framework further considers the collaboration inside a single model and takes smaller overhead. Extensive experiments on Cifar-10, Cifar-100, ImageNet32 and STL-10 corroborate the effectiveness of these four ways separately while combining them leads to further improvements. In particular, test errors on the STL-10 dataset are decreased by 9.28% and 5.45% for ResNet-18 and VGG-16 respectively. Moreover, our method is proven to be robust to label noise with experiments on Cifar-10 dataset. For example, our method has 3.53% higher performance under 50% noise ratio setting.
Text-Based Sentiment Analysis with Classification Techniques—A State-of-Art Study

Social media acts as a bridge between people to widely share any data and communication. In recent years, the textual data content is increasing rapidly, where the text can contain any kind of information about people, product or service. Manually reading each text from online is not possible, and also, it is a challenging task to decide whether the user has positive stance or negative stance on the topic. To solve this problem, text processing techniques and algorithms are required. Sentiment analysis is the technology that processes any online text and classifies it into positive, negative and neutral. To analyze online content, new models are proposed by incorporating the machine learning concept. The unstructured information from online documents is analyzed and classified as results, which has been described as user sentiment analysis. The outcome of sentiment analysis can be used for business development, understand the customer expectations and also to know the public opinion toward a particular product or service. This paper focuses on various sentiment analysis processes and also the most used classification techniques from machine learning concepts.
Quantifying apt of RNN and CNN in Image Classification

This paper presents a comparative study of understanding the apt of Recurrent Neural Network (RNN) and feed forward artificial neural network like Convolutional Neural Network (CNN) algorithms from deep learning area in the image classification process. We have considered MNIST handwritten digits dataset for image classification using RNN and CNN classification techniques from deep learning to understand the nature of their working in classification. The results of these two classification techniques have shown their own sway with set of inputs. The pros and cons of each technique are recorded during the process. This work edifies the researchers the strength of different classification techniques and supports them in selecting suitable algorithm.
Mutual Secrecy of Attributes and Authorization Policies in Identity Federation

In modern Web services, authentication federation that separates the Identity Provider (IdP), which centrally manages authentication information such as user passwords, from the service provider (SP) is commonly used. Authorization federation in which the IdP further manages user attributes, the IdP provides attribute values to the SP, and the SP decides whether to provide the service, is used as well. However, more information about attribute values is often passed to the SP than is necessary for the authorization decision. If an authorization policy of the SP is logical expression of predicates, the expression needs to be disclosed to the IdP. There also are cases in which it is necessary to keep the authorization policies secret from the IdP and the user. Information that should be concealed may be narrowed down through multiple authorization processes, even if attributes and authorization policies can be kept secret from each other in a single authorization process. In this work, we point out and formulate these problems and provide some protocols to solve them.
PE‐EDD: An efficient peer‐effect‐based financial fraud detection approach in publicly traded China firms

Financial fraud arises from the exaggeration of business interests, and an accurate detection or prediction is a useful tool for both corporate management and capital market systems. A collection of computer technologies has been made on this problem so far, and one of the most important solutions is unsupervised learning algorithms. Among them, most approaches work by analysing the internal relations in financial data and finding a new description of non-fraud firms. However, current studies focus a lot on the geometry attribute of financial data, while overlooking the obvious behaviour patterns and peer effects among firms. This has limited the accuracy of representation and furthermore the detection performance. In this work, a very general class of functions is allowed to represent firms, constraining them by peer effects between firms and presenting an error-distribution-based financial fraud firm detection approach. Experimental results have shown great performance of the proposed approach.
Visibility Restoration of Diverse Turbid Underwater Images- Two Step Approach

Underwater Images are of degraded quality due to the scattering and absorption. The color cast and turbidity that hinder the visibility of such images are due to the sediments present that vary for diverse environments. Shallow water images are very turbid. The images too suffer from negative effects of artificial illumination when capturing data. Here a two-step approach is formulated to restore and enhance the underwater images from different locations. The images are then blended using a wavelet fusion considering the mean of the images. The output images demonstrate reduced haze, improved contrast and enhanced sharpness with adequate removal of the color cast. The results project better visibility on both subjective and objective measures compared to recent restoration and enhancement methods.
Focus on Inherent Attributes for Temporal Knowledge Graph Completion

In the last few years, the availability of temporal knowledge graphs has stimulated extensive research in temporal knowledge graph completion (TKGC) and temporal knowledge graph embedding (TKGE), where temporal information is added to static knowledge graphs that have been widely applied previously. However, most existing methods, such as current state-of-the-art DE-SimplE and TeRo, learn embeddings of temporal-evolving attributes, overlooking the inherent attributes inside entities, where some essential and inherent features are included. In this paper, we introduce a novel method utilizing Inherent Attributes with a Graph Attention network (IAGAT) for TKGC. Our IAGAT extracts inherent attributes from sufficient features corresponding to various facts at different time stamps, to obtain the inherent embeddings. And we take advantages of previous rotation based methods to obtain the temporal-evolving embed-dings. Through extensive experiments and sufficient comparisons, we demonstrate our model outperforms the current state-of-the-art models on link prediction task. Furthermore, we evaluate and prove the necessity of the inherent attributes in performance improvement, and study how our model functions in extracting inherent features.
Optimization of Nadam algorithm for image denoising based on convolutional neural network

By studying the structure of feedforward denoising convolutional neural network (DnCNN), image denoising technology further integrates deep neural network structure, learning algorithm and regularization method, at the same time, in order to spee <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> d up the network training process and improve the denoising of the model Performance, the paper imports the residual learning and batch normalization. This paper uses the Nadam optimization algorithm that is different from the common gradient descent algorithm SGD and Adam algorithm. The DnCNN-N model can solve the Gaussian denoising with a certain degree of noise. This paper analyzes the three noise levels: $\sigma =$15, 25 and 50. DnCNN-N uses residual learning methods to remove potentially clean images in the hidden layer. The experiments show that this model can show high efficiency in conventional image denoising tasks, and can be efficiently implemented through GPU computing.
Data Collection Using Virtual Reality: Contributions of Human-Centered Design for Research Practices

This article presents research practices for a biometric data collection protocol involving virtual reality equipment, based on principles that guide human-centered design (HCD) and experience design (UX), with the aim of exploring the potential for applying design methodologies and their tools for research practices in data collection in the context of Virtual Reality and others. With the know-how in research and design of the team of the Creativity Laboratory of the Scientific and Technological Park of PUCRS (Tecnopuc Crialab), a research protocol and a participant’s journey map were conceived based in the principles of HCD and UX. These were improved based on feedback from tests carried out with researchers and subjects, in an iterative process throughout the research. Based on theoretical contributions and the practical experience described, practices are presented that can be considered in research with virtual reality equipment, and in any type of experiment involving people.
A Preliminary Study on Common Programming Mistakes that Lead to Buffer Overflow Vulnerability

When vulnerabilities are exploited, the impact can be insignificant or detrimental, depending on the attack’s nature. Research found that buffer overflow is one of the most widespread and frequently reported vulnerabilities that result in system crashes. This study investigates the frequent errors in the source code of production software that lead to buffer overflow such that its causes can be determined. The findings of the study can help guide developers to avoid these programming errors. Therefore, our study’s primary objective is to analyze vulnerable code components of software repositories and extract the developers’ frequent programming mistakes that have resulted in a buffer overflow attack. Sixteen vulnerable code components and relevant resolutions were selected from three popular and well-known systems: Android, Eclipse, and Red Hat, to be analyzed. The results show that lack of input sanitization, improper checking of array bounds and parameters, and the lack of value and range checks on variables are the most common programming issues that lead to a buffer overflow in these systems. We also found improper use of "If" and "While" loop conditions frequently contributed to the errors in bounds and variable checks.
Edge Map Extraction of an Image Based on the Gradient of its Binary Versions

Edge detection plays a very important role in many image processing and computer vision applications. In this paper, we propose a new non-deep learning edge detection technique based on three binary images with three different thresholds instead of gray or colored images. The proposed edge detection technique extracts the majority of the true edges. Extensive experiments are carried out and the results show that the proposed technique provides a performance ratio better than that of the existing non-deep learning techniques.
Smooth Stitching Method for the Texture Seams of Remote Sensing Images Based on Gradient Structure Information

Traditional smooth stitching method for the texture seams of remote sensing images is affected by gradient structure information, leading to poor stitching effect. Therefore, a smooth stitching method for the texture seams of remote sensing images based on gradient structure information is proposed in this research. By matching the feature points of remote sensing images and introducing a block link constraint and shape distortion constraint, the modified stitching image is obtained. By using remote sensing image fusion, the smooth stitching image of texture seams is obtained, and the local overlapping area of the texture is optimized. The main direction of texture seams is determined by calculating the gradient structure information of texture seams in horizontal and vertical directions. By selecting the initial point, the optimal stitching line is extracted by using the minimum mean value of the cumulative error of the smooth stitching line. By using the method of boundary correlation constraints, matching the feature points of the texture seams of remote sensing images and selecting the best matching pair, a smooth stitching algorithm for the texture seams of remote sensing image is designed, which realizes the smooth stitching of the texture seams of remote sensing images. Experimental results show that the design method has good performance in stitching accuracy and efficiency in the smooth stitching of the texture seams of remote sensing images. Specifically, the Liu et al. and the Zhang et al. methods that are the benchmark studies in the literature are introduced as a comparison, and the stitching experiment is carried out. The test is carried out according to accuracy and time and the proposed method achieves better results by almost 25%.
Research on Text Visual Effect of Multimedia Courseware for Mobile Online Learning

The purpose of this study is to make the multimedia courseware, which designed on the computer, can terminal obtain satisfactory visual effect on the mobile screen. Methods interviews and questionnaires were conducted to online learning students to determine the factors that affect visual effect. Then an experiment on visual effect with font, font size, color and spacing as variables was conducted with 20 subjects. Results the influence of each variable on visual effect was obtained. Conclusion the study gives the clear suggestions on the parameterization of font designed on computer to ensure good visual effect on the mobile screen.
Path Planning Based on Improved Ant Colony Algorithm

In order to overcome the problems of low accuracy and long time-consuming in traditional heuristic path planning algorithm, a new path planning method is proposed based on the improved Ant Colony (AC) algorithm. Different from the traditional AC algorithm worked with fix pheromone, the updatable pheromone is adopted by the improved AC algorithm to increase the diversity performance. Based on this advantage, the proposed path planning method has better accuracy and convergence performance. Simulation results show that the time-consuming of the proposed method is reduced effectively. And, the shortest path length of the proposed method is also shorter than the traditional heuristic path planning algorithm obviously.
Adaptive direction-guided structure tensor total variation

Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional) images. In this study, we build a two-stage denoising framework that brings adaptivity to the DSTV based denoising. We design a DSTV-like alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of quality and computational efficiency.
Hierarchical Identity-Based Conditional Proxy Re-encryption Scheme Based RLWE and NTRU Variant

IB-PRE can perform fine-grained access control on the user's decryption rights based on the identity of the delegatee, while the proxy cannot obtain the identity information of the delegator and delegatee. The current identity-based proxy re-encryption scheme achieves the ciphertext conversion between users at the same level, while it does not further distinguish between different levels of user identity, which is not suitable for hierarchical user management system. This paper combines hierarchical identity encryption with proxy re-encryption, and uses RLWE encryption system and NTRU variant as the underlying encryption scheme. According to the difference of the key reversibility and the ciphertext share between the two systems, the control of the decryption authority of users' different levels was realized. The transformed cipher text still satisfies the rules of higher level to decrypt lower level cipher, and constructs a unidirectionality, collusion resistant, and non-interactive hierarchical identity-based conditional proxy re-encryption scheme(HIB-CPRE), which is IND-sid-CPA security under the RLWE difficult assumption.
Method for Forecasting Multidimensional Time Series Based on Neuro-Fuzzy Cognitive Temporal Models

The method of analysis and forecasting of multidimensional time series (MTS) based on the proposed type of Neuro-Fuzzy Cognitive Temporal Models (NFCTM) is considered. The method provides accounting for the direct, indirect and accumulated interaction of all MTS components by their various time lags relative to each other, allows modeling and predictive assessment MTS under conditions of uncertainty, nonlinearity of interaction, partial inconsistency and significant interdependence of MTS components as in in the absence and in the presence of an external influence on the values of MTS components and/or on fuzzy relations of mutual influence between them, and as a result, it allows to increase the accuracy of forecasting each component and the MTS as a whole without deteriorating the forecasting accuracy of its other components. Experimental studies were carried out and the results of using the proposed method for predicting the electrical load were obtained.
PTWA: Pre-Training with Word Attention for Chinese Named Entity Recognition

Recently, the character-based model that incorporates potential word information has proven effective for Chinese named entity recognition (NER). However, due to the independence of the pre-trained character model and the lexicon, it will cause the embedding space to be misaligned and cannot be combined well. Chinese pre-trained encoders usually process text as characters. It ignores the information carried by the larger granular information, so the encoder cannot easily adapt to certain character combinations. Because large-grained information is ignored and Chinese does not have clear character boundaries, this will lead to the loss of important semantic information, which is an important problem for Chinese. In this paper, we propose PTWA: pre-training with word attention for Chinese named entity recognition. PTWA uses multi-head word attention to form a word vector from multiple word vectors, and proposes a word length prediction task to better integrate the word vector into pre-training. With the powerful capabilities of the transformer, PTWA can explicitly make full use of potential word information without adding an external lexicon, and can coexist with pre-trained models that implicitly use word information (such as BERT-WWM, and ERNIE). Experiments conducted on four Chinese NER datasets show that the performance of PTWA is better than other word-word models and Chinese pre-training models.
Tabular Data Concept Type Detection Using Star-Transformers

Tabular data is an invaluable information resource for search, in-formation extraction and question answering about the world. It is critical to understand the semantic concept types for table columns in order to fully exploit the information in tabular data. In this paper, we focus on learning-based approaches for column concept type detection without relying on any metadata or queries to existing knowledge bases. We propose a model that employs both statistical and semantic features of table columns, and use Star-Transformers to gather and scatter information across the whole table to boost the performance on individual columns. We apply distant supervision to construct a tabular dataset with columns annotated with DBpedia classes. Our experiment results show that our model achieves 93.57 accuracy on the dataset, exceeding that of the state-of-the-art baselines.