Reviewerly/exHarmony huggingface dataset:
Intelligent video surveillance of sand mining based on object detection and tracking

Abstract In order to improve the regulatory capability of sand mining and make full use of the video resources collected by video surveillance systems, an object detection and tracking algorithm that combines YOLOv4 and Kalman filter is proposed to identify sand dredgers. Initially, an object detection dataset of common ships is finely established through camera capture, manual and automatic annotation, and data augmentation. Then, the obtained dataset is used to train YOLOv4. The results show that the trained YOLOv4 achieves a mean Average Precision of 92.26% over all classes. Although Kalman filter does not increase the precision of YOLOv4, it supplements the association information between images in an frame stream, so as to solve the target loss problem of YOLOv4 caused by occlusion. Finally, the proposed algorithm is deployed in edge computing terminal devices to form an intelligent video surveillance system for sand dredgers. The intelligent system realizes the 24-hour real-time monitoring of sand mining, which can help to stop illegal sand mining in time, and provide strong technical support for relevant departments to fulfill their management obligations.
ExaPPC: a Large-Scale Persian Paraphrase Detection Corpus

This paper describes the creation of Exa Persian Paraphrase Corpus (ExaPPC), a large paraphrase corpus consisting of monolingual sentence-level paraphrases using different sources. ExaPPC is the first large-scale paraphrase dataset used in Persian paraphrase detection to the best of our knowledge. There are 2.3M labeled sentence pairs in the corpus consisting of a 1M paraphrase label and 1.3M non-paraphrase label. Efforts were made manually and semi-automatically to construct this corpus using techniques such as subtitle alignment, translating existing parallel English-Persian corpus and similarity corpus on English tweets. In addition to enriching the corpus, candidate sentence pairs among tweets have been extracted via NLP tools and labeled by two Persian native speakers. The advantages of this corpus compared to the existing ones are the number of pair sentences, sentence Length variation and textual diversity, including formal and dialogue sentences. The result on the provided test corpus shows that ExaPPC achieves 94% accuracy on paraphrase detection task. The corpus is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/exaco/exappc
Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-move forgery.Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification.Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques.With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique.The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered, and then localize the copy moved regions.To perform the feature extraction process, the political optimizer (PO) with Mobile Networks (MobileNet) model has been derived for generating a set of useful vectors.Finally, an enhanced bird swarm algorithm (EBSA) with least square support vector machine (LS-SVM) model has been employed for classifying the digital images into the original or the forged ones.The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine (MSVM) technique and thereby enhance the classification performance.For ensuring the enhanced performance of the ODTL-CMFD technique, a series of simulations have been performed against the benchmark MICC-F220, MICC-F2000, and MICC-F600 datasets.The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.
Enumerable Learning-Based Machine Learning Techniques for Sentiment Analysis

People express their views on goods, services, governments, and events via words and phrases on social media. Sentiment analysis is a technique used in natural language processing to gather positive and negative feedback on a piece of writing on social media. Academics have been forced to research sentiment analysis by exponential growth in business and ecommerce companies. This study examined emotions in two distinct domains: one for electronics product evaluations and another for film reviews. Due to the enormous volume of data gathered for analysis, it is referred to as massive data. This vast data is multidimensional and includes useless information. Furthermore, this data complicates the analysis since it reduces the effectiveness of machine learning categorization algorithms. As a consequence, the data must be cleansed and pertinent features identified. Numerous methods have been used to eliminate noise from data and identify essential components. There are two methods for determining suitable features: feature selection and feature extraction. The research is split into three stages: the first step involves converting unstructured data to structured data and choosing relevant and feature information. Second, machine learning classification algorithms are employed in conjunction with feature selection approaches, and third, machine learning algorithms are used in conjunction with ensemble learning methods.
Artistic Design Method of User Interaction Experience of Mobile System Based on Context Awareness

This work combines context awareness to explore the art design technique of user interaction experience of mobile system and alters the conventional art communication and art appreciation methods in order to increase the impact of artistic interactive experience. Furthermore, this paper proposes that the spatial pattern will influence the aggregation value in wireless sensor networks, as well as the influence of the pattern of single-point extrapolation function and information coverage function measurement, which is quantified as a nonsampling error in sampling theory. Then, this paper proposes a hierarchical sampling theory to optimize the nonsampling error and improve the effect of artistic situation perception. Finally, on this basis, this paper constructs an art design system of context awareness and interactive experience. The findings of this study indicate that the state-of-the-art mobile system user interaction experience based on context awareness suggested in this work has a strong context awareness effect and user interaction experience.
Underwater Image Enhancement and Super Resolution based on Deep CNN Method

The significance of exploration of underwater resources in the development and use of underwater autonomous operations are becoming more essential to protect against the risk of high-pressure deep-sea environments. To operate underwater autonomously intelligence computer vision will be the key technology. In a submerged environment light is weak and poor-quality image enhancement, used as pre-processing procedures, are required to enable underwater vision. The paper presents a mix that enhances images using CNN and CBAM, super resolution. This paper proposes to utilize attenuation module for adaptive residual learning to filter out the features which are irrelevant from the previous layers. It should be mentioned that both wavelength-driven multi-contextual design and attentive residual learning are not proposed for UIR. This paper presented a detailed study on how the performances of a few high-level vision tasks, such as diver&#x0027;s 2D pose estimation and underwater semantic segmentation, have been improved when presented with the enhanced images produced by Deep Wave Net.
Fuzzy User Access Trust Model for Cloud Access Control

Cloud computing belongs to a set of policies, protocols, technologies through which one can access shared resources such as storage, applications, networks, and services at relatively low cost.Despite the tremendous advantages of cloud computing, one big threat which must be taken care of is data security in the cloud.There are a dozen of threats that we are being exposed to while availing cloud services.Insufficient identity and access management, insecure interfaces and Applications interfaces (APIs), hijacking, advanced persistent threats, data threats, and many more are certain security issues with the cloud platform.APIs and service providers face a huge challenge to ensure the security and integrity of both network and data.To overcome these challenges access control mechanisms are employed.Traditional access control mechanisms fail to monitor the user operations on the cloud platform and are prone to attacks like IP spoofing and other attacks that impact the integrity of the data.For ensuring data integrity on cloud platforms, access control mechanisms should go beyond authentication, identification, and authorization.Thus, in this work, a trust-based access control mechanism is proposed that analyzes the data of the user behavior, network behavior, demand behavior, and security behavior for computing trust value before granting user access.The method that computes the final trust value makes use of the fuzzy logic algorithm.The trust value-based policies are defined for the access control mechanism and based on the trust value outcome the access control is granted or denied.
Adaptive Masreliez–Martin fractional interpolatory cubature Kalman filter with recursive noise estimation

In this study, we propose a Masreliez–Martin fractional interpolatory cubature Kalman filter and an adaptive Masreliez–Martin fractional interpolatory cubature Kalman filter to design the estimators for fractional-order discrete-time nonlinear systems under non-Gaussian measurement noise and unknown process noise covariance. Ther Masreliez–Martin fractional interpolatory cubature Kalman filter is developed by extending the interpolatory cubature Kalman filter to the fractional nonlinear discrete system to increase the robustness of estimation under noisy environment by applying the Masreliez–Martin method, including both time update and measurement update. Meanwhile, the adaptive Masreliez–Martin fractional interpolatory cubature Kalman filter is put forward to enhance the state estimation adaptive to the fractional system with uncertain process noise through recursive estimation. Simulation results on the re-entry target tracking system have demonstrated the adaptiveness, robustness and effectiveness of the propose filters.
When do Saccades begin? Prediction of Saccades as a Time-to-Event Problem

We present a novel view on gaze event classification by redefining it as a time-to-event problem. In contrast to previous models, which consider the classification as discrete events, our redefinition allows for estimating the remaining time until the next saccade event. Therefore, we provide a feature analysis and an initial solution for compensating the latency of wearable eye-trackers build in today’s head-mounted displays. Similar to previous classifiers, we utilize oculomotor features such as velocity, acceleration, and event durations. In total, we analyze 104 extracted features of three datasets and apply different regression methods. We identify optimal window sizes for each feature and extract the importance of all extracted windows using recursive feature elimination. Afterwards, we evaluate the performance of all regressors using earlier selected features. We show that our selected regressors can predict the time-to-event better than the baseline, indicating the potential usage of time-to-event prediction of saccades.
Language-Independent Text Tokenization Using Unsupervised Deep Learning

Languages-independent text tokenization can aid in classification of languages with few sources.There is a global research effort to generate text classification for any language.Human text classification is a slow procedure.Consequently, the text summary generation of different languages, using machine text classification, has been considered in recent years.There is no research on the machine text classification for many languages such as Czech, Rome, Urdu.This research proposes a cross-language text tokenization model using a Transformer technique.The proposed Transformer employs an encoder that has ten layers with self-attention encoding and a feedforward sublayer.This model improves the efficiency of text classification by providing a draft text classification for a number of documents.We also propose a novel Sub-Word tokenization model with frequent vocabulary usage in the documents.The Sub-Word Byte-Pair Tokenization technique (SBPT) utilizes the sharing of the vocabulary of one sentence with other sentences.The Sub-Word tokenization model enhances the performance of other Sub-Word tokenization models such pair encoding model by +10% using precision metric.
Latin and Greek in Computing: Ancient Words in a New World

In computing, old words are reused by giving them new meanings. However, many people may not be aware of the origin of ancient words that have found new uses in computing. We advocate for basic computing science and engineering courses to cover the origins of the most common ancient words used in computing.
Browser-based Hyperbolic Visualization of Graphs

Hyperbolic geometry offers a natural ‘focus+context’ for data visualization and has been shown to underlie real-world complex networks. However, current hyperbolic network visualization approaches are limited to special types of networks and do not scale to large datasets. With this in mind, we designed, implemented, and analyzed three methods for hyperbolic visualization of networks in the browser based on inverse projections, generalized force-directed algorithms, and hyperbolic multi-dimensional scaling (H-MDS). A comparison with Euclidean MDS shows that H - MDS produces embeddings with lower distortion for several types of networks. All three methods can handle node-link representations and are available in fully functional web-based systems.
An innovative chain coding mechanism for information processing and compression using a virtual bat-bug agent-based modeling simulation

The continuous changes in the size of data create new challenges to design new techniques to reduce its size and encode it in a way that changes its original representation. In this article, we develop a bat-bug agent-based modeling simulation for chain coding and employ it in compressing bi-level image information. The system consists of agents that are classified into static and dynamic depending on their movements. Bugs are considered static agents, and they are distributed over the virtual environment according to the allocation of pixels in the original image. On the other hand, bats are dynamic agent, and their role is to move around to consume bugs while the algorithm tracks their movements. Bats are designed in a way to move within certain boundaries to avoid crashing into each other. Bats employ specific movements that allow them to move in relative directions. Therefore, the frequency of their movements can follow a certain pattern that can help in further size reduction. In other words, the integration of relative movements into our design proved to be advantageous because there is an observable pattern of repeated movements, which allows getting higher compression results. Finally, arithmetic coding is applied to the final strings that represent the movements of bats while searching for bugs to eat. To assess the performance of the algorithm, we compared the findings against standardized benchmarks used in the image processing community: G3, G4, JBIG1, and JBIG2. The outcomes show that we could outperform all these benchmarks using all the images we used for testing. Additionally, we conducted a series of paired samples t-tests, and they revealed that the mean differences between our results and those obtained from other benchmarks are statistically significant.
Human facial expressions recognition using artificial intelligence K-Nearest Neighbor (KNN) Algorithm

The research in the field of automatic face recognition, regarding any questions about the appearance and the location of the static and dynamic characteristics of the disturbance, or brown spots on the face. The results of using MATLAB classification tools. We took a train to a dataset of six words in the faces of the people and for the purposes of the train data set, we have a shared vision of border, pattern, and 90&#x0025; and 30&#x0025; of the mask border and pattern for the purpose of the test data set, and we make full use of the FIRST Invariant of the properties and to the structure for the analysis of the patient's symptoms, and then to rank with the help of a Proposed Balanced KNN method to observed maximum accuracy at 95.25&#x0025;.These are shows result and analysis section shows compressive method result and image analysis results.
Classification of Image using Deep Neural Networks and SoftMax Classifier with CIFAR datasets

The process of image classification involves the feature extraction from the problem image, processing of those features and classification using the efficient classifier to obtain the necessary output. This work is also doing the same by extracting the features from the input image and the extracted information is processed with the help of Deep Neural Network (DNN) and the image classification is done using SoftMax Classifier. The combination of DNN and the SoftMax Classifier provides the efficient image classification and the data sets that used in this work is CIFAR 10, CIFAR 100 and MNIST to obtain the best results. The training accuracy and the validation accuracy of the image data should be increased and the training loss and the validation loss of the image data should be decreased in the image classification process for efficient results. The accuracy, precision value and those averages are listed, and the macro average and the weighted average of all the recall and precision values with their support values for the CIFAR datasets are also tabled.
A Better Decision Tree: The Max-Cut Decision Tree with Modified PCA Improves Accuracy and Running Time

Abstract Decision trees are a widely used method for classification, both alone and as the building blocks of multiple different ensemble learning methods. The Max Cut decision tree introduced here involves novel modifications to a standard, baseline variant of a classification decision tree, CART Gini. One modification involves an alternative splitting metric, Max Cut, based on maximizing the distance between all pairs of observations that belong to separate classes and separate sides of the threshold value. The other modification, Node Means PCA, selects the decision feature from a linear combination of the input features constructed using an adjustment to principal component analysis (PCA) locally at each node. Our experiments show that this node-based, localized PCA with the Max Cut splitting metric can dramatically improve classification accuracy while also significantly decreasing computational time compared to the CART Gini decision tree. These improvements are most significant for higher-dimensional datasets. For the example dataset CIFAR-100, the modifications enabled a 49% improvement in accuracy, relative to CART Gini, while providing a $$6.8 \times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6.8</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> speed up compared to the Scikit-Learn implementation of CART Gini. These introduced modifications are expected to dramatically advance the capabilities of decision trees for difficult classification tasks.
A Survey on Privacy Preserving Content Based Image Retrieval and Information Sharing in Cloud Environment

As the quantity of images has expanded significantly, commonly utilized Content Based Image Retrieval algorithms are commonly utilized in our ordinary routine. When it comes to processing and storing information, image retrieval systems are generally highly expensive. As a result, image owners should consider outsourcing these functions to a cloud server. However, since the cloud server can only be trusted to a certain amount, image owners' confidentiality might be jeopardized. This situation gives rise to the need of an algorithm/method which makes the images retrieval secure. For addressing this issue, the effort will be done to propose the Secure Content Based Image Retrieval method with less complexity, high speed of operation, high accuracy and high retrieval efficiency. For this, various works have been studied to find best approach for achieving the efficient retrieval scheme.
CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries

The abstractive methods lack of creative ability is particularly a problem in automatic text summarization. The summaries generated by models are mostly extracted from the source articles. One of the main causes for this problem is the lack of dataset with abstractiveness, especially for Chinese. In order to solve this problem, we paraphrase the reference summaries in CLTS, the Chinese Long Text Summarization dataset, correct errors of factual inconsistencies, and propose the first Chinese Long Text Summarization dataset with a high level of abstractiveness, CLTS+, which contains more than 180K article-summary pairs and is available online. Additionally, we introduce an intrinsic metric based on co-occurrence words to evaluate the dataset we constructed. We analyze the extraction strategies used in CLTS+ summaries against other datasets to quantify the abstractiveness and difficulty of our new data and train several baselines on CLTS+ to verify the utility of it for improving the creative ability of models.
GRACER: Improving the Accuracy of RACER Classifier Using A Greedy Approach

Rule-based classifiers play a prominent role in the field of data mining as they are used frequently in tackling challenges from medical to industrial applications. RACER (Rule Aggregating ClassifiER), a recently-introduced algorithm, is one of the rule-based classifiers that has had increased the accuracy of classification compared to some other well-known classifiers. RACER considers each instance in the training data as an initial rule. To form an applicable rule-set, however, RACER tries to merge the initial rules and replaces the merged rule with the two initial rules whenever it has a better fitness value than the initial rules. In this paper, we have changed the rule-merging phase of the RACER classifier using a greedy approach and have proposed a new classifier based on this modification called, GRACER (Greedy RACER). In order to approve the GRACER capability, six datasets from the UCI machine learning database repository have been applied. The performance of GRACER has been compared with that of RACER and some other well-known classifiers. Our experiments show the supenority of GRACER compared with other applied classifiers.