Reviewerly/exHarmony huggingface dataset:
Implementation of creative projects in the conditions of digitalization of educational environment

In this paper, the problem of digitalization of the educational environment is actualized. In this regard, the basic concepts of the problem under consideration have been clarified. The author analyzes the digitalization process, factors in the development of the digital educational process, didactic principles of digital pedagogy, the use of an interdisciplinary approach in higher education. The hypothesis of the research is that the use of such a method as a creative project in the learning process contributes to an increase in the level of digital culture of students, the formation of creative thinking and creativity, flexibility and the ability to make decisions in the face of constant changes. The organization of creative projects on digital platforms will diversify the professional training of students. The main problems of this paper are related to the fact that, despite the existing methods of forming a digital culture, the level of its formation remains extremely low. Not all modern teaching methods serve the needs of the learner. The paper presents one of the possible solutions to the problem presented on the example of the methodology for conducting classes in the developed courses of disciplines Recreational geography, Recreational resource studies, Resortology, Art history: Cinema history with students majoring in social and cultural activities. The features of the organization of classes, the implementation of practical tasks by students, the variety of forms, methods and means of teaching are presented. The work emphasizes the importance of organizing conditions for creative activity in the context of digitalization of the educational environment.
A News Application Based on the Computer Language Application Framework

At present, no news app is mainly oriented to college students, which makes it difficult for college students with great demand for news to obtain the latest realtime information. The “look around” app is committed to making a model that passes the real name authentication of college students in the front end and stores it in the background database, provide accurate information for college students, and confirm the identity of the news parties and witnesses through their authentication information. Use Dubbo, layer UI and react JS to make and improve the system. This development needs to produce native app and webapp respectively, and the required languages for both are Java and HTML.
Entropy-Based Fuzzy Weighted Logistic Regression for Classifying Imbalanced Data

Logistic regression is a popular classification method that has disadvantages when it is applied to large data. Truncated Regularized Iteratively Reweighted Least Square (TR-IRLS) is a method that overcomes this problem. This method is similar to Support Vector Machine (SVM) because both of them have similar loss functions and parameters that can adjust the bias and variance. Both methods were designed with the assumption of balanced data, so that they are not suitable to be applied on imbalanced data. Both methods were developed to overcome problem on imbalanced data. TR-IRLS was developed into Rare Event Weighted Logistic Regression (RE-WLR) and SVM was developed into Fuzzy Support Vector Machine (FSVM). Both RE-WLR and FSVM use weights based on class differences, so that RE-WLR had better performance than TR-IRLS on imbalanced data whereas FSVM was better than SVM. Then, Entropy-based Fuzzy Support Vector Machine (EFSVM) was developed by obtaining weighting values not only based on class differences, but also based on entropy. EFSVM further enhanced minority class interest in imbalanced data than SVM and even FSVM. Therefore, Entropy-based Fuzzy Weighted Logistic Regression (EFWLR) is proposed by adopting the success of Entropy-based Fuzzy Membership (EF) as weight on SVM. This study applied EF as weight on Weighted Logistic Regression for binary classification. Experiments on 20 simulation data and 5 benchmark data with various rarity schemes validated that the EFWLR outperformed TR-IRLS and RE-WLR based on AUC. EFWLR had more efficient AUC than RE-WLR on imbalanced data.
A Robust QR and Computer Vision-based Sensorless Steering Angle Control, Localization and Motion Planning of Self-driving Vehicles

Autonomous path following has gained tremendous popularity during the last few decades. Numerous researchers have contributed to the development of highly automated navigation systems using different types of sensors and their combination. However, their proposed approaches do not provide a cost-efficient solution because of the deployment of exorbitant and sophisticated sensors, which remains a challenging problem for customized vehicles used in academic research. To overcome this issue, this study presents an economically efficient sensorless steering angle approach that employs a single camera for steering control and quick response (QR) based localization of a vehicle. Moreover, we used SONAR for object detection in a defined route to avoid possible collisions. The proposed technique combines a probabilistic Hough transform for lane detection and QR codes, which helps the vehicle stay in its lane for stabilized control. To prove the efficiency of our approach, we tested it on our developed prototype vehicle named EMO. To validate the proposed approach through in-field testing, we designed a customized test track within the campus. The experimental results show the benefit of our proposed approach compared to existing methods available in the literature.
Siamese Attention and Point Adaptive Network for Visual Tracking

Siamese-based trackers have achieved excellent performance on visual object tracking. Most of the existing trackers usually compute the features of the target template and search image independently and rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target. This paper proposes Siamese attention and point adaptive head network referred to as SiamAPN for Visual Tracking. Siamese attention includes self-attention and cross-attention for feature enhancement and aggregating rich contextual inter-dependencies between the target template and the search image. And Point head network for bounding box prediction is both proposal and anchor-free. The proposed framework is simple and effective. Extensive experiments on visual tracking benchmarks, including OTB100, UAV123, and VOT2018, demonstrate that our tracker achieves state-of-the-art performance and runs at 45 FPS.
Leveraging Reinforcement Learning and WaveFunctionCollapse for Improved Procedural Level Generation

This work presents a novel approach to training Reinforcement Learning (RL) agents to serve as heuristics for the WaveFunctionCollapse (WFC) algorithm in the production of procedurally generated video game levels. While the algorithm’s original minimal entropy heuristic is sufficient for constructing levels that look visually appealing, it is often the case that these levels suffer when playability is concerned. The approach presented in this work involves replacing this heuristic with a set of deep neural networks trained using RL to direct the algorithm in the construction of playable levels for the original Super Mario Bros. (SMB). We evaluate the performance of our models using a game-playing A*-based agent provided by the Mario AI Competition Framework and designate a simple reward function which reflects the quality of generated levels based on the A* agent’s ability to successfully navigate them. Results using this approach show an increase in the percentage of playable levels generated using our learned heuristics over those using minimal entropy.
Mastering the Body and Tail Shape of a Distribution

The normal distribution and its perturbation have left an immense mark on the statistical literature. Several generalized forms exist to model different skewness, kurtosis, and body shapes. Although they provide better fitting capabilities, these generalizations do not have parameters and formulae with a clear meaning to the practitioner on how the distribution is being modeled. We propose a neat integration approach generalization which intuitively gives direct control of the body and tail shape, the body-tail generalized normal (BTGN). The BTGN provides the basis for a flexible distribution, emphasizing parameter interpretation, estimation properties, and tractability. Basic statistical measures are derived, such as the density function, cumulative density function, moments, moment generating function. Regarding estimation, the equations for maximum likelihood estimation and maximum product spacing estimation are provided. Finally, real-life situations data, such as log-returns, time series, and finite mixture modeling, are modeled using the BTGN. Our results show that it is possible to have more desirable traits in a flexible distribution while still providing a superior fit to industry-standard distributions, such as the generalized hyperbolic, generalized normal, tail-inflated normal, and t distributions.
Generative Multi-View Based 3D Human Pose Estimation

Large amounts of annotated data is essential for modern Human pose estimation. We propose using a semi supervised learning scheme to estimate the 3D poses from adversarial multi-views generated representations from a single RGB image. Our GAN generated views are the result of training that aims to create authentic and less degenerated outputs. Our method targets the shared latent space between the 3 dimensional and 2 dimensional poses and aims to simplify it by constraining the latent distribution. This resulted in a noticeable increase in the method generalization and exploitation of unlabeled depth maps. We utilized heatmaps to visualize the attention robustness under variety of poses. Our method competes with state of the art performances among semi supervised approaches and excels in some challenging poses as evaluated on Human3.6M, MPII-INF-3DHP and Leeds SportsPose challenging datasets. 1
Interaction Toolkit for Programming Interactions with Marker-Based Tangibles in Virtual Reality

This project focuses on the development of a tangible interaction programing toolkit for Virtual Reality (VR). Marker-based tangible interactions have been explored before, but no development libraries exist for facilitating developers’ work. Particularly for accessible web-based VR frameworks such as A-Frame, the complexity of the interactions is limited by programming difficulties, due to the inexistence of high-level programmatic abstractions. As such, in this project the goal is to develop a set of abstractions that facilitate the development of tangible experiences in the A-Frame platform. We have developed an initial version of a component library and have started evaluating it through task-based API usability testing. We report preliminary results from this usability testing.
The Effectiveness of Google Form as A Medium For Assessing Students’ English Learning Outcomes in High School Level

ICT has become an important topic in language education. ICT facilitates foreign language learning can also be used in assessment activities. The application that can be used for assessment is Google Form. The effectiveness of using Google Form as a medium for student assessment in learning English will be discussed in this journal. The aim of this study to determine the effectiveness the use of Google Form as medium assessing students English Learning outcomes of Class XII MAN 2 Banjarnegara. This research applied descriptive qualitative research. The qualitative descriptive is the research that presents the descriptive data of an object observed. The data were collected through concuscting in-depth interviews with research subject, the teachers of 12th grade in MAN 2 Banjarnegara school year 2020/2021. The result obtained is google form as an assessment in 12th grade at MAN 2 Banjarnegara Is quite effective to use because easier for students and teachers during the learning activities. google forms are considered easy to access and also helping the teacher for assessment. In this study it can be concluded that Google Form is effective as a medium for student assessment.
Fast Rotation Matching Method for SAR and Optical Images

The rotation matching between synthetic aperture radar (SAR) and optical images is essential for their registration. In this paper, we introduce a novel fast rotation matching method for SAR and optical images. In the method, the ADDHs of SAR and optical images can be used to reflect the rotation difference between them, and then the rotation difference is removed by the cross-correlation of the two images absolute directional derivative histograms (ADDHs). Subsequently, we generate the ADDHs by oriented Gaussian-Gamma-shaped (OGGS) operator. Then, the cross-correlation based on FFT is used to deal with the ADDHs of SAR and optical images in order to determine the rotation difference of the two images. The experiment results demonstrate that the proposed method can detect the rotation difference in various conditions and achieves rotation matching with high speed.
Research on Visualization Method of Large-scale User Location Distribution Based on CesiumJS

In order to cater to the advanced user positioning function of the Beidou satellite navigation system, the simulated domestic positioning point data is used to realize the visualization of user distribution in the form of a heat map through the Webside 3D visualization technology. In the case of a large amount of rendering data, by optimizing the kernel density estimation algorithm and optimizing the calculation method of the data points in the raster, the effect of real-time rendering of the heat map in the optimal display form is realized with the change of the viewpoint position, and the rendering Performance has been significantly improved.
Retinal Encryption Using Snnipet Pixel XOR with Huffman Sequential Encoding for Privacy Augmentation

The escalated utilization of digital platforms in recent times using digital platforms, web applications and multimedia files such as pictures, audio, and video data have triggered the demand for faster mechanisms with resilient working methodologies. Unauthenticated breaches have become a common problem and therefore require access audits frequently. To achieve impeccable security, encryption is a solution rendered for inimitable and mitigated infringement of data from unauthorized users. The increase in Personal Health Records (PHR) and Electronic Health Records (EHR) has been under constant scrutiny for their data breach. The previous indagation pertaining to encryption have been focused primarily on data to a large extent, and although image encryption has entailed several features in the past, this paper pivots on the encryption of an individual’s retina. This study is aimed at improving security of biometric modalities taken from a patient for further diagnosis and protection in terms of standardized and lossless data protection. The methodology of retinal encryption is thus implemented by using the fragmented compartment-based XOR encryption method along with the sequential Huffman coding method. The symmetric key encryption method thus enhances the content concealment of the image for the respective owner and their sub-assigned authorized users. The simulation for this study is performed in MATLAB, and the results are successfully procured. MATLAB and the results are procured successfully.
A Deep Learning Artificial Neural Network Algorithm for Instance-based Arabic Language Authorship Attribution

One of the common examples of cybercrime are identity theft and violating of intellectual property that commonly occur in social media. Authorship attribution (AA) techniques are used to extract and use several features of the text in order to identify the original author. These features are used to differentiate the writing style of one author from others. Several machine learning methods have been used to identify the AA using different languages. Few studies were conducted for Arabic AA. This paper aims to investigate the performance of deep learning-based artificial neural network (ANN) for identifying the attribution of authors using Arabic text. The applied model helps protect users in social media from identity theft and violating of their intellectual property. The experiments of this study used a dataset that includes 4,686 Arabic texts for 15 different authors. The performance of the deep learning method was compared with several machine learning methods. The experimental results showed the superior performance of deep learning for AA in Arabic language using different evaluation criteria such as F-score, accuracy, precision, and recall measures.
Trust Evaluation Mechanism of Service Nodes Based on Blockchain

The blockchain is a modern digital ledger that not only records currency transactions, but also records anything of value. Once the transaction is verified and submitted to the blockchain, it is difficult to recover. Therefore, in order to prevent malicious behavior, there should be a mechanism to evaluate the trust between participants before any transaction, which can effectively reduce the malicious behavior of malicious nodes. There are some nodes that provide low-quality services to quickly get rewards, and even malicious nodes may appear to deliberately provide extremely low-quality or invalid services. This paper proposes a blockchain-based trust mechanism for service nodes, which takes into account the privacy and security of the nodes, and is used to evaluate the confidence situation between the task assignment node and the task participating node. Experiments show that the mechanism we proposed can not only improve the service quality of service nodes, but also effectively distinguish high-quality nodes, low-quality nodes and malicious nodes, which provides an effective reference for selecting high-quality and credible service nodes.
THE PLACE OF SOCIAL ENGINEERING IN THE PROBLEM OF DATA LEAKS AND ORGANIZATIONAL ASPECTS OF CORPORATE ENVIRONMENT PROTECTION AGAINST FISHING E-MAIL ATTACKS

As the number and percentage of phishing attacks on company employees and regular users have tended to increase rapidly over the last two years, it is necessary to cover the issue of protection against this type of social engineering attacks. Throughout the pandemic, intruders are finding more and more new ways to cheat, so even experienced Internet users can become a victim to their scams. Due to the fact that e-mail is used in almost all companies, most fishing attacks use e-mail to send malicious messages. The article discusses the main methods used by attackers to conduct phishing attacks using e-mail, signs that the user has become a victim to social engineers, and provides recommendations how to increase the resilience of the corporate environment to such attacks using organizational methods. Because the user is the target of phishing attacks, and the tools built into the browser and email clients in most cases do not provide reliable protection against phishing, it is the user who poses the greatest danger to the company, because he, having become a victim of a fishing attack, can cause significant damage to the company due to his lack of competence and experience. That is why it is necessary to conduct training and periodic testing of personnel to provide resistance to targeted phishing attacks. Company employees should be familiar with the signs of phishing, examples of such attacks, the principles of working with corporate data and their responsibility. The company's management must create and communicate to the staff regulations and instructions that describe storage, processing, dissemination and transfer processes of information to third parties. Employees should also report suspicious emails, messages, calls, or people who have tried to find out valuable information to the company's security service. Raising general awareness through hands-on training will reduce the number of information security incidents caused by phishing attacks.
Lateral Path Optimization for Autonomous Driving in Highly Constrained Environments

This paper presents a lateral path optimization method to avoid collisions for autonomous driving in highly constrained environments. Firstly, the spatial model in road coordinate is established to describe the kinematic relationship between the vehicle and the reference road. The nonlinear optimization based on vehicle kinematic model in space domain is employed to smooth the reference road. Secondly, the multilayer searched method along the smoothed reference road is used to find the optimal node by using the designed cost function and then established the safety driving corridor in current passable region. Furthermore, the lateral path planning is formulated as the optimization problem based on the spatial model to improve driving safety and comfort. The proposed method considers the constraints of road boundaries and the constraint of curvature in order to provide the optimal path for the collision avoidance maneuver. Finally, the effectiveness of the proposed method is verified through simulation in different highly constrained scenarios.
Use of a Recurrent Neural Network to Identify Spammers on Twitter

A great many individuals all throughout the planet are engaged with informal communication locales. Client encounters with such systems administration media, like twitter, have huge and regularly undesirable consequences forever. Spammers have become an objective for the dispersal of huge quantities of unimportant and destructive data from the main informal communication locales. Twitter is among the most lavish weblogs administrations ever and is normally used to post nonsensical spam. Counterfeit clients give clients spontaneous tweets to embrace projects or sites that sway genuine clients, yet additionally stop the utilization of assets. Moreover, the potential has expanded to broaden clients with invalid data by means of wrong personalities, bringing about vindictive material. Spammers and phony client and phony tweets have as of late been distinguished on Twitter and turned into a pertinent examination field in online organizations. The techniques used to recognize Twitter spammers were recommended in this paper. Besides, Twitter’s spam separating techniques are ordered dependent on their recognition capacity bogus data, a URL, and pattern spam. Because of 12–9 capacities, including 6 as of late characterized capacities and 2 recently characterized capacities, two PC checked arrangements have been found to separate among clients and spammers in a constant dataset.Patil, Rahul A. Chaudhari, Chetana C.
Characterization and Construction of Generalized Bent Functions with Flexible Coefficients

In 2017, Tang et al. provided a complete characterization of generalized bent functions from ℤ2n to ℤq(q = 2m) in terms of their component functions (IEEE Trans. Inf. Theory. vol.63, no.7, pp.4668-4674). In this letter, for a general even q, we aim to provide some characterizations and more constructions of generalized bent functions with flexible coefficients. Firstly, we present some sufficient conditions for a generalized Boolean function with at most three terms to be gbent. Based on these results, we give a positive answer to a remaining question proposed by Hodžić in 2015. We also prove that the sufficient conditions are also necessary in some special cases. However, these sufficient conditions whether they are also necessary, in general, is left as an open problem. Secondly, from a uniform point of view, we provide a secondary construction of gbent function, which includes several known constructions as special cases.