Reviewerly/exHarmony huggingface dataset:
Moving vehicle video detection combining ViBe and inter-frame difference

It is difficult with traditional methods to realise real-time and robust detection of moving vehicles under complex traffic scenes. In this paper, a moving vehicle video detection method that combines ViBe and inter-frame difference is proposed. The proposed method improves the background update efficiency of the traditional ViBe method by adding a multi-threshold comparison step to the inter-frame difference method. The improved background update strategy can judge whether the detected pixel point belongs to the foreground or background, and dynamically adjusts the background update rate according to the inter-frame difference results. Experimental results showed the proposed method can effectively remove of "ghosting" phenomenon that occurs in traditional ViBe method and realise accurate and complete detection of the moving vehicle in video.
Similarity Matching of Pairs of Text using CACT Algorithm

In data mining, shorter text analysis is performed more widely for many applications. Based on the syntax of the language, it is very difficult to analyze the short text with several traditional tools of natural language processing and this is not applied correctly either. In short text, it is known that there are rare and insufficient data available and further it is difficult to identify semantic knowledge with the great noise and ambiguity of short texts. In this paper, the authors proposed to replace the coefficient of similarity of Cosine with the measure of similarity of Jaro-Winkler to obtain the coincidence of similarity between pairs of text (source text and target text). Jaro-Winkler does a better job of determining the similarity of the strings because it takes an order into account when using the positional indices to estimate relevance. It is presumed that the performance of CACT driven by Jaro-Wrinkler with respect to one-to-many data links offers optimized performance when compared to the operation of CACT driven by cosine. In this paper, the ensemble algorithm CACTS and SAE is adopted with Jaro-Winkler similarity approach. The new algorithm is employed for short text analysis and better results. An evaluation of our proposed concept is sufficient as validation.
SiamON: Siamese Occlusion-Aware Network for Visual Tracking

Occlusion has been proven to be one of the most challenging factors faced by most visual trackers. There are mainly two difficulties, the first one is that the number of occlusion samples are very limited even though collecting a large-scale training data set, and another one is how to correctly learn the features of the target when comes to occlusion situations. In this paper, we tried to solve these two problems together in our proposed model. To this end, we propose a novel Siamese Occlusion-aware Network (SiamON) for high-performance visual tracking. In particular, we predefine some soft-masks to solve the problem of fewer occlusion samples, which perceive patterns of occlusion contents at different locations and take these masks as the conditions to guide occlusion-aware feature learning. Meanwhile, we propose a target-aware attention mechanism allows the model to pay more attention to the target and further weaken the impact of occlusion. Extensive experiments on several popular benchmarks show that our tracking method exceeds many state-of-the-art trackers especially in the presence of occlusion and meets the requirements of real-time.
Automated Student Community Portal

Student Portal is an interface used to associate with the students in a university. The application is basically classified into two categories such as Community Portal and Registration Desk. The application permits the enrolled user to login and a new user is allowed to enroll. Community portal contains forums including event queries, placement queries, fresher queries, sports queries. Event designer creates an event, all users willing to take part in the event must register by filling out the form. Event managers sign in and download registered users data from application
A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Recommender System or Recommendation Engine gaining popularity as it can tackle information overload problem. Initially it was considered as a domain of Information Retrieval system and was limited to few applications. With the advancement of different state-of-the-art modeling approaches recommender system can be applicable to many application domains. Movie Recommender System (MRS) is widely explored domain and used by many streaming service providers like Netflix, Amazon Prime, YouTube and many more. This system makes use of users’ data to explore and recommends personally as per their taste. In this paper a detailed study on recently published article related to movie recommendation is carried out. Popular techniques for MRS are commonlycategorized into collaborative filtering, content-based and hybridmethod. Neighborhood-based, latent factor based, neural network based and deep learning based techniques have been continuously evolved with application to MRS. Recently proposed models have been reviewed and it is found that hybrid method performs better as compared to individual model.
The Dependency Treebanks for Ancient Greek and Latin

The article aims to be an introduction to the dependency treebanks currently available for Ancient Greek and Latin, i.e., the Ancient Greek and Latin Dependency Treebank (AGLDT), the Index Thomisticus Treebank (IT-TB), the PROIEL Treebank, and the SEMATIA Treebank. Their pipelines for creation of morphosyntactic annotations are presented so as to highlight major commonalities and differences. All treebanks share the same basic underlying formalism, whereby syntactic words are connected to each other to form labeled directed acyclic graphs, and their annotation schemes, although different, are comparable to a very large extent.
Grammar

Grammars with rules that make distinctions between correct and incorrect forms are defined as ‘prescriptive’ grammars. The rules are more like a blueprint for building well-formed structures, and they represent speakers’ unconscious knowledge, or ‘mental grammar’ of the language. Models of grammar differ greatly, depending on whether they are formal grammars or functional grammars. Formal grammar is concerned with the forms themselves and with how they operate within the overall system of grammar. Approaches influenced by formal theories such as generative grammar tend to view language learning as rule acquisition and, therefore, focus on formalized rules of grammar. Corpus studies have also led to an increased interest in analyses of ‘discourse grammar’, that is, analyses of the functional roles of grammatical structures in discourse. Second language acquisition research in both naturalistic and classroom environments has informed modern perspectives of grammar learning.
Blind Steganalysis for JPEG Images using SVM and SVM-PSO Classifiers

Blind steganalysis or the universal steganalysis helps to identify hidden information without previous knowledge of the content or the embedding technique. The Support Vector Machine (SVM) and SVM- Particle Swarm Optimization (SVM-PSO) classifiers are adopted for the proposed blind steganalysis. The important features of the JPEG images are extracted using Discrete Cosine Transform (DCT). The kernel functions used for the classifiers in the proposed work are the linear, epanechnikov, multi-quadratic, radial, ANOVA and polynomial. The proposed work uses linear, shuffle, stratified and automatic sampling techniques. The proposed work employs four techniques for image embedding namely, Least Significant Bit (LSB) Matching, LSB replacement, Pixel Value Differencing (PVD) and F5 and applies 25% embedding. The data to the classifier is split as 80:20 for training and testing and 10-fold cross validation is carried out.
Smart city video surveillance using fog computing

Conventional video surveillance systems require infrastructure including expensive servers with capability to process images and store video recordings. These surveillance systems produce and need to store a huge amount of data and to execute in real time to detect safety events. The problems of the anti-social activities which gradually increasing across the country especially in the urban areas in recent times which lead to the need for technological innovations in the security and surveillance system. The proposed system is based on cloud computing. In this paper the application has been modelled and simulated using iFogSim. The results predicts that the fog-based model is more secured and efficient compared to cloud computing parameter energy consumption. The proposed system helps to increase the effectiveness of the intelligent agencies and thereby increase crime safety at public places.
IoT Based Human Intrusion Detection System using Lab View

Recent times there is an increase in thefts in the recent past. This creates a very bad environment for people to live in fear. The problem with home security in the modern world is a cause for concern. The conventional intruder detection system now we are using are highly expensive and there can be a possibility of false alarms. This problem is fixed by building a home intruder detection system that can accurately detect a human intruder, while filtering out movements that are caused due to any other moving objects using LabVIEW and Python. The images that were acquired and analyzed through frame comparisons are converted to gray scale images and then processed to detect an intruder. Here LabVIEW works as server and Python works as a client. At client video is acquired continuously, video is converted into images. Images are processed and information is send to the server. Server displays the status of the intruder with date and time. If the intruder is present then the system compares the intruder’s data with the data in the system. The images that were acquired and analyzed through frame comparisons converted to gray scale images that represent change, and then filtered through a series of image refining VI’s, helping to enhance our change detection results. If the data matches then the processing stops, if not then the system alerts the user through SMS or email if any intruder has been detected and sends the image to the user app. Through app we can make an alarm.
Retraction Note to: A New Trust Evaluation Algorithm Between Cloud Entities Based on Fuzzy Mathematics

The Editor-in-Chief has retracted this article [1] because it overlaps substantially with the following articles by the same authors [2, 3], amongst others. The authors have not responded to any correspondence about this retraction.
The Core Python Language I

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
E-Commerce Product Classification using Lexical Based Hybrid Feature Extraction and SVM

At present, online shopping has become a growing process, in which the profit statistics are posted by familiar e-commerce corporations like Amazon, Flipkart, Snapdeal, etc. However, this kind of online shopping unkindly omits the touch and feel of the products that can be used to estimate the product quality as the main factor while buying the commodities from the shops. The estimation of product quality is more significant during the purchasing of online products. Therefore, many opinion mining and sentiment classification methods were introduced to purchase the best products through online shopping. But, these classification methods haven’t attained the effective product classification with best reviews and ratings. In this paper, we propose a hybrid feature extraction method PCA (Principle Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding ) with SVM (Support Vector Machine) using lexicon-based method to classify and separate the products from the large set of different products depending on their features, best product ratings and positive reviews. In this process, the online products will be isolated and listed according to their high positive reviews. The data preprocessing is applied to the dataset to get the data accuracy before the execution of feature extraction and classification. The dimensionality reduction and best visualization of large data set are executed by applying the PCA and t-SNE method. The sentiments are also been extracted by this hybrid feature extraction method to acquire the best neighboring product ratings. The polarity of words is discovered using a lexical based approach to extract positive reviews for obtaining the best products. Finally, the SVM is exploited to the classification of products. The performance of the proposed method is estimated with precision, recall, accuracy and complexity that can provide the entire accurateness of the system.
Efficient Search for Optimal Diffusion Layers of Generalized Feistel Networks

The Feistel construction is one of the most studied ways of building block ciphers. Several generalizations were then proposed in the literature, leading to the Generalized Feistel Network, where the round function first applies a classical Feistel operation in parallel on an even number of blocks, and then a permutation is applied to this set of blocks. In 2010 at FSE, Suzaki and Minematsu studied the diffusion of such construction, raising the question of how many rounds are required so that each block of the ciphertext depends on all blocks of the plaintext. They thus gave some optimal permutations, with respect to this diffusion criteria, for a Generalized Feistel Network consisting of 2 to 16 blocks, as well as giving a good candidate for 32 blocks. Later at FSE’19, Cauchois et al. went further and were able to propose optimal even-odd permutations for up to 26 blocks.In this paper, we complete the literature by building optimal even-odd permutations for 28, 30, 32, 36 blocks which to the best of our knowledge were unknown until now. The main idea behind our constructions and impossibility proof is a new characterization of the total diffusion of a permutation after a given number of rounds. In fact, we propose an efficient algorithm based on this new characterization which constructs all optimal even-odd permutations for the 28, 30, 32, 36 blocks cases and proves a better lower bound for the 34, 38, 40 and 42 blocks cases. In particular, we improve the 32 blocks case by exhibiting optimal even-odd permutations with diffusion round of 9. The existence of such a permutation was an open problem for almost 10 years and the best known permutation in the literature had a diffusion round of 10. Moreover, our characterization can be implemented very efficiently and allows us to easily re-find all optimal even-odd permutations for up to 26 blocks with a basic exhaustive search
Design of multimedia engineering teaching system based on internet of things technology

In order to improve the multimedia control performance of teaching, a multimedia engineering teaching system design model based on internet of things technology is proposed. The overall structure model of the real estate engineering multimedia teaching system design is constructed based on the embedded ARM core. The system integration control is implemented through the decentralised control method, and the overall design framework of the real estate engineering teaching system is carried out. The internet of things networking technology is used for system network design, the VIX bus technology is used to carry out real estate engineering teaching data communication and information processing, and the software design of the multimedia engineering teaching system is realised in embedded environment. The test results show that adopting this method to design the multimedia engineering teaching system can provide better robustness, stronger human-computer interaction, and can realise multimedia integrated control of teaching system.
Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping

Abstract. This study introduces an efficient deep-learning model based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from 2D surface observations. The method was applied to delineate paleovalleys in an Australian desert landscape. The neural network was trained on a 6400 km2 domain by using a land surface topography as 2D input and an airborne electromagnetic (AEM)-derived probability map of paleovalley presence as 3D output. The trained neural network has a squared error &lt;0.10 across 99 % of the training domain and produces a squared error &lt;0.10 across 93 % of the validation domain, demonstrating that it is reliable in reconstructing 3D paleovalley patterns beyond the training area. Due to its generic structure, the neural network structure designed in this study and the training algorithm have broad application potential to construct 3D geological features (e.g., ore bodies, aquifer) from 2D land surface observations.
Computer based Registration for Skill Development

Student skill development system is a part of university course registration system. In University traditional approach course registration process involves filling registration forms manually and then they have to submit them. In traditional approach students don’t have choice to select their courses. Students need to study the courses which are offered by university and are very limited so students cannot improve their skills and the process is very time consuming. With the improvement of information technology from the last decade, the major focus has shifted from manual system to automated system. Various other applications like Railway systems, Hospital management etc., are also getting updated from manual to automated work. Although there are several approaches have been proposed in addressing student registration, this research paper provides a very user friendly and automated way of registration system. An Online Skill Development System has been developed to simplify the current manual procedure. In this project students can register for their courses based on their interest and they can also register for online moocs so that they can improve their skills and knowledge. Students can explore more technologies through the moocs. This system has been developed using ServiceNow Platform and its concepts like Client Script, Script Include, Form designer, Service portal, Workflow and Notifications. This system software is more secured, user-friendly and less time-consuming.
Sliding mode control of a class of nonlinear systems based on recurrent type-2 fuzzy RBFN

In this paper a type-2 fuzzy RBFN-based sliding mode control is used for a class of nonlinear systems. A new recurrent type-2 fuzzy radial basis neural network (we named RT2FRBFN) is used to approximate the conventional sliding mode control law. This method does not require prior information about the system; therefore, simultaneously, RT2FRBFN detects the system's dynamics, as well as the estimated dynamics in the sliding mode controller. Finally, the proposed RT2FRBFN sliding mode control system is used to tracking control design with regard to uncertainty in a class of nonlinear systems. Combination of backstepping method and sliding mode control helps to compensate the control signal and get a better performance. The backstepping method is used to improve the final threshold stability and use the sliding mode control to obtain high speed and unchangeable response to uncertainty. Simulation results show the proposed RT2FRBFN-based sliding mode control has better performance than RBFN-based sliding mode control in the presence of uncertainty.
Detection of extreme sentiments on social networks with BERT

Abstract Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined sentiments on these platforms to study their behavior in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work where an unsupervised approach to automatically detect extreme opinions/posts in social networks is proposed. The performance of the proposed approach is evaluated on five different social network and media datasets. In this work, we use a semi-supervised approach known as BERT to reevaluate the accuracy of our prior approach and the obtained classified dataset. The experiment proves that in these datasets, posts that were previously classified as negative or positive extreme are extremely negative or positive in many cases while using BERT. Furthermore, BERT shows the capability to classify the extreme sentiments when fine-tuned with an appropriate extreme sentiments dataset.
Geuine tripartite entanglement in three-flavor neutrino oscillations

The violation of Leggett-Garg inequalities tested the quantumness of neutrino oscillations (NOs) across macroscopic distances. The quantumness can be quantified by using the tools of the quantum resource theories. Recently, a new genuine tripartite entanglement measure [S. B. Xie et al., Phys. Rev. Lett. 127, 040403 (2021)], concurrence fill, is defined as the square root of the area of the concurrence triangle satisfying all genuine multipartite entanglement conditions. It has several advantages compared to other existing tripartite measures. Here, we focus on using concurrence fill to quantify the tripartite entanglement in three-flavor NOs. Concurrence fill can reach its maximum $0.89$ for the experimentally-observed electron antineutrino oscillations, but it cannot for the muon antineutrino oscillations. In both cases, we compare its performance with other three tripartite entanglement measures, including the generalized geometric measure (GGM), the three-$\pi$ entanglement, and the genuinely multipartite concurrence (GMC), in the neutrino propagation, and accordingly show that concurrence fill contains the most quantum resource. Furthermore, concurrence fill and the three-$\pi$ entanglement are always smooth, while GGM and GMC measures have several sharp peaks. The genuine tripartite quantification of the quantumness of three-flavor NOs represents the first step towards the further potential application of neutrinos on quantum information processing.