Reviewerly/exHarmony huggingface dataset:
Automatic Attendance System Using Face Recognition with Deep Learning Algorithm

This project aims to develop an attendance system that is more efficient and convenient than traditional attendance methods currently used in schools and universities. Therefore, this paper proposes an automatic attendance system using face recognition. In this face recognition attendance system, the university does not need to install any additional devices in the classroom, which makes it a cost-effective system. The system consists of three parts: attendance system, student profile system, and training. First is the training stage where the student's photo should be captured and stored in a separate folder. Second is the attendance system. Here the lecturer needs to take a photograph of the student and then upload it to the system. The system will automatically recognize the student's face and store his/her name in an excel sheet (CVS file). The third system is the student's profile. This system is to help the lecturer retrieve the student's data by only capturing a picture of the student. A GUI has been made to simplify the usage of the system. The face recognition system has been developed using a combination of two deep learning algorithms: Multi-Task Cascaded Convolutional Neural Network (MTCNN) and FaceNet. To train the system, 908 pictures from 21 different students were collected and used, and 108 pictures were used for testing. The testing result showed 100% for face detection and 87.03% for face recognition.
Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

The concept of searching and localizing vehicles from live traffic videos based on descriptive textual input has yet to be explored in the scholarly literature. Endowing Intelligent Transportation Systems (ITS) with such a capability could help solve crimes on roadways. One major impediment to the advancement of fine-grain vehicle recognition models is the lack of video testbench datasets with annotated ground truth data. Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos and even less so for vehicle search and localization models. In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input textual descriptions. An efficient hashgraph algorithm is introduced to compute valid target information from textual input. This work further introduces two novel datasets to advance AI research in these challenging areas. These datasets include (a) the most diverse and large-scale Vehicle Color Recognition (VCoR) dataset with 15 color classes—twice as many as the number of color classes in the largest existing such dataset—to facilitate finer-grain recognition with color information; and (b) a Vehicle Recognition in Video (VRiV) dataset, a first of its kind video testbench dataset for evaluating the performance of vehicle recognition models in live videos rather than still image data. The VRiV dataset will open new avenues for AI researchers to investigate innovative approaches that were previously intractable due to the lack of annotated traffic vehicle recognition video testbench dataset. Finally, to address the gap in the field, five novel metrics are introduced in this paper for adequately accessing the performance of vehicle recognition models in live videos. Ultimately, the proposed metrics could also prove intuitively effective at quantitative model evaluation in other video recognition applications. T One major advantage of the proposed vehicle search and continuous localization framework is that it could be integrated in ITS software solution to aid law enforcement, especially in critical cases such as of amber alerts or hit-and-run incidents.
Designing Pedagogical Agents Toward the Recommendation and Intervention Based on Students' Actions in an ITS

This full paper aims to present a model of the Pedagogical Agents architecture to assist in activities and tutorial interventions for mastering the resolution of first-degree polynomial equations. With the impact of the educational situation in various parts of the world due to the Covid-19 pandemic and the difficulties identified in the interaction between student and teacher, this research uses a multi-agent approach to improve the architecture of an Intelligent Tutoring System aiming to assist in the context of distance education. Exploratory research in the literature was carried out, aiming at the problem and situation, which implies the process of development of the technological artifact and from the elaboration of a product based on the gaps found. As pedagogical support, Vygotsky&#x0027;s social interaction theory for instructional support is used. The implementation of this proposal will result in the improvement of Tutoring Systems, allowing to guarantee conditions for the teacher and the student to carry out the teaching and learning process flexibly and intelligently, aiming at the construction of knowledge.
KGAT-SR: Knowledge-Enhanced Graph Attention Network for Session-based Recommendation

As a main task of session-based recommendation, next interaction (item) recommendation aims to recommend the next possible interaction (e.g., click on a song) given a session context (i.e., a list of happened interactions). This task faces the challenge of how to generate accurate recommendations when only the intra-session dependencies are available. Existing recommendation models usually fail to exploit the knowledge about items to model intra-session dependencies. Therefore, this paper addresses the problem of next item recommendation given a session context of an ordered, single-type-action, and anonymous session, and investigates how to effectively model intra-session dependencies by leveraging the knowledge from a knowledge graph (KG). We propose a novel model called Knowledge-enhanced Graph Attention Network for Session-based Recommendation (KGAT-SR). Its core idea is that the knowledge about items from a KG is exploited via knowledge graph attention network to generate a knowledge enhanced session graph (KESG). After the KESG is aggregated via weighted graph attention, the node features and graph topology in the graph are utilized to generate accurate session embedding, which can be used to recommend the next item. Experiments show that KGAT-SR significantly outperforms the state-of-the-art models for next item recommendation. KGAT-SR’s source code is available at https://github.com/hu-dske/KGAT-SR.
Multimedia Image Encryption Analysis Based on High-Dimensional Chaos Algorithm

With the development of network and multimedia technology, multimedia communication has attracted the attention of researchers. Image encryption has become an urgent need for secure multimedia communication. Compared with the traditional encryption system, encryption algorithms based on chaos are easier to implement, which makes them more suitable for large-scale data encryption. The calculation method of image encryption proposed in this paper is a combination of high-dimensional chaotic systems. This algorithm is mainly used for graph mapping and used the Lorenz system to expand and replace them one by one. Studies have shown that this calculation method causes mixed pixel values, good diffusion performance, and strong key performance with strong resistance. The pixel of the encrypted picture is distributed relatively random, and the characteristics of similar loudness are not relevant. It is proved through experiments that the above calculation methods have strong safety performance.
An Improved 2D U-Net Model Integrated Squeeze-and-Excitation Layer for Prostate Cancer Segmentation

In this paper, we proposed an improved 2D U-Net model integrated squeeze-and-excitation layer for prostate cancer segmentation. The proposed model combined a more complex 2D U-Net model and squeeze-and-excitation technique. The model consisted of an encoder stage and a decoder stage. The encoder stage aims to extract features of the input, which contains CONV blocks, SE layers, and max-pooling layers for improving the feature extraction capability of the model. The decoder aims to map the extracted features to the original image with CONV blocks, SE layers, and upsampling layers. The SE layer is implemented to learn more global and local features. Experiments on the public dataset PROMISE12 have demonstrated that the proposed model could achieve state-of-the-art segmentation performance compared with other traditional methods.
Data Leakage Prevention Adoption Model &amp; DLP Maturity Level Assessment

Data is the most valuable resource organizations possess. Nowadays, intentional and unintentional data leakages to unauthorized entities have dramatically increased in the private and government sectors for different reasons and purposes. Data leak poses a severe threat to the data security of these organizations. In the private sector, such leakage can damage the reputation of a business and weaken its competitiveness, while a leakage in the government sector can severely impact its public and diplomatic relationships and even threaten national security. Therefore, private and government sectors have recently strived to adopt the Data Leakage Prevention (DLP) program to enhance data security and minimize data leakage. However, some organizations limit their DLP programs to DLP technology implementation. DLP technology represents only a part of the whole process and does not include other essential elements such as planning, policy, process, data ownership and classification, data access control, training, and awareness. A comprehensive adoption of these elements might prevent data leakage. This research paper aims to provide a model for DLP adoption and address the most critical elements of the DLP Adoption Model, which are correlated with the DLP Maturity Level Assessment, the “DLP Maturity Level Grid,” to measure the success of the model.
<scp>CPP</scp>: A <scp>content‐aware</scp> privacy protection method for <scp>location‐based</scp> service

Abstract Generally, a location‐based service (LBS) often contains the location attribute, content attribute, time‐stamp, and range. From the perspective of privacy protection, the location attribute and the content attribute are the key attributes and need to be protected. However, existing privacy protection methods focus excessively on the location attribute and ignore the content attribute contained in the LBS, which discloses the user's private information. In view of this challenge, a content‐aware privacy protection method, called the CPP method that considers the content attribute is proposed. Specifically, the CPP method is based on using k‐anonymity to generate dummy content attributes to protect the private content. As is shown in an experiment constructed on real‐world data, the CPP method can indeed improve the effect of privacy protection.
Multiple Active False Target Suppression Based on Distributed GM-PHD Filter

Current research on radar anti-jamming by multi-static fusion is mainly based on centralized radar networks. A data-level fusion method based on distributed radar network to counter active false targets is proposed in this paper. Firstly, the random-finite-set-based probability hypothesis density (PHD) filter is adopted for each radar to track multiple real and false targets independently. Then, the generalized covariance intersection (GCI) method with zero-forcing property is employed in multi-sensor data fusion to suppress non-collaborative false targets. Furthermore, after introducing passive radars which are less likely to be jammed, the consensus algorithm is utilized to share the true information obtained by passive radars to the entire network for the purpose of suppressing collaborative false targets. The effectiveness of the proposed approach is validated by simulations.
Generalised Pattern Search with Restarting Fitness Landscape Analysis

Abstract Fitness landscape analysis for optimisation is a technique that involves analysing black-box optimisation problems to extract pieces of information about the problem, which can beneficially inform the design of the optimiser. Thus, the design of the algorithm aims to address the specific features detected during the analysis of the problem. Similarly, the designer aims to understand the behaviour of the algorithm, even though the problem is unknown and the optimisation is performed via a metaheuristic method. Thus, the algorithmic design made using fitness landscape analysis can be seen as an example of explainable AI in the optimisation domain. The present paper proposes a framework that performs fitness landscape analysis and designs a Pattern Search (PS) algorithm on the basis of the results of the analysis. The algorithm is implemented in a restarting fashion: at each restart, the fitness landscape analysis refines the analysis of the problem and updates the pattern matrix used by PS. A computationally efficient implementation is also presented in this study. Numerical results show that the proposed framework clearly outperforms standard PS and another PS implementation based on fitness landscape analysis. Furthermore, the two instances of the proposed framework considered in this study are competitive with popular algorithms present in the literature.
Research on Applied Undergraduate Education Resource Allocation System Based on Cloud Computing

At this stage, the total amount of teaching resources in our country is small, the quality is not high, the resources are duplicated and the sharing and application are low. In order to balance educational resources, create open teaching resources, realize cross-sharing of resources among various disciplines, and provide users with a new model of on-demand services. Based on the related technologies of cloud computing, this paper presents a cloud computing-based applied undergraduate education resource allocation model according to the user’s task demand for resources in the open undergraduate education resource allocation. And designed the architecture model of the application-based undergraduate education resource allocation system based on cloud computing.
Asymmetric Confidentiality in Blockchain Embedded Smart Grids in Galois Field

Economic growth requires a sharp increase in the utilization of energy. Since the initial mechanical era, financial development has been driven by industrialization, transportation, and, most important of all, electrification, majorly achieved by petroleum product ignition. This way of development has had malicious and abusive aftershocks on the environment since the beginning. Smart grids are an idea to slightly diminish the burden on our Mother Nature, but this idea is getting tainted by the anticipation of ferocious technophiles who may try to get the grid down using quantum computers in the coming years. Thus, security becomes one of the major concerns for the smart grid. In this paper, we propose a quantum-resistant framework for associating smart grids and blockchain embedded with a permutation-substitution-based public-key cryptosystem in Galois Field to prevent unauthorized access and perform encryption of the private information of the user and consumption statistics. Permutation and substitution are performed to increase the diffusion and confusion of the data. Expenditures are quantified from the dissipation particulars, and the payment of electricity bill is performed using our blockchain wallet. The prediction model of consumption data is generated availing stochastic gradient descent. The performance analysis of the proposed cryptosystem is predicted after a simulation of the smart grid.
Vectorized Reservoir Sampling

Reservoir sampling is becoming an essential component of realtime rendering as it enables importance resampling with limited storage. Chao’s weighted random sampling algorithm is a popular choice because of its simplicity. Although it is elegant, there is a fundamental issue that many random numbers must be generated to update reservoirs. To address this issue, we modify Chao’s algorithm with sample warping. We apply sample warping in two different ways and compare them. We further vectorize the modified algorithm to make reservoir sampling more useful for CPU rendering and give a couple of practical examples.
An intelligent fuzzy-based emergency alert generation for persons with episodic memory decline problems

Many humans deal with problems that concern episodic memory decline. These problems can cause annoying, and sometimes, dangerous incidents, like failing to recall the name of a friend or forgetting to take a medicine or turn off the cooker. Concerning the above, this paper presents a fuzzy rule-based mechanism that generates emergency alerts when a dangerous situation is caused by an inconsistency in the human’s actions in the home environment. In such a way, the system protects persons with episodic memory decline problems or lapses of attention from dangerous situations that may be caused by their memory disorder and allows them to complete an everyday activity. For the paper’s needs the application of the presented mechanism is limited to the usage of a cooker. The system takes as input the time, the size and the content of the pot that is used to the cooker, calculates the degree of emergency and describes it using fuzzy sets and, finally, applies rules over the fuzzy sets to generate alert messages that notify the monitored person about the next action that s/he has to do in order to complete a particular activity. For the evaluation of the system, we have developed a simulation program that asks users to complete some activities during a specific time period. The system embeds the presented fuzzy rule-based mechanism and monitors the user’s actions and generated alerts, which concern the usage of the stove burner. The simulation software was used by 15 users. Their reactions and opinions about the system’s alerts and the assistance it offers, are positive.
A Novel Adaptive Fuzzy Deep Learning Approach for Histopathologic Cancer Detection

We proposed a novel model that integrates the fuzzy theory and group equivariant convolutional neural network for histopathologic cancer detection. The proposed fuzzy group equivariant convolutional neural network consists of the convolutional network, a novel fuzzy global pooling layer, and a fully connected network. In the fuzzy global pooling layer, the generated feature maps are transferred into the fuzzy domain by two different fuzzification methods. One of the fuzzy feature maps exploits the uncertainty information of histopathologic images, and the other keeps the original information. Furthermore, the fuzzy feature maps are processed by using Min-max operations. The experiments verified that the proposed method could always find the maximum fuzzy entropy and exploit and present the uncertainty of histopathologic images well. The experiments using the benchmark dataset demonstrate that the proposed model becomes more accurate and outperforms the existing models including the benchmark models. Compared to the benchmark model with 89.8% of accuracy, 96.3% of AUC, and 0.260 of negative log-likelihood loss, the proposed model obtained 91.7% of accuracy, 97.2% of AUC, and 0.214 of negative log-likelihood loss.
High-Brightness Image Enhancement Algorithm

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications.
X3D Field Trips for Remote Learning

Combinations of immersive media and graphic portrayals can enable the human subjective sense of Presence. This paper collects our experiences and evaluations from six projects that use Extensible 3D (X3D) interactive graphics to deliver spatial experiences across the WWW. X3D enables the combination of spherical panoramas with 3D models and maps to visually transport users to a specific real location at a specific time. Remote users have access to these worlds through a Web-browser or other immersive device; local users in a CAVE can collaborate with natural physical gestures;. We reflect on the graphical and interactive requirements of these projects and provide guidance for future applications. In the face of physical lock-downs and distancing due to the CoVID pandemic, such platforms illustrate the opportunities and challenges in the design and delivery of spatial visualizations, especially for remote learning.
A Clustering Algorithm in Stream Data Using Strong Coreset

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies.
Predicting Security Vulnerabilities using Source Code Metrics

Large open-source systems generate and operate on a plethora of sensitive enterprise data. Thus, security threats or vulnerabilities must not be present in open-source systems and must be resolved as early as possible in the development phases to avoid catastrophic consequences. One way to recognize security vulnerabilities is to predict them while developers write code to minimize costs and resources. This study examines the effectiveness of machine learning algorithms to predict potential security vulnerabilities by analyzing the source code of a system. We obtained the security vulnerabilities dataset from Apache Tomcat security reports for version 4.x to 10.x. We also collected the source code of Apache Tomcat 4.x to 10.x to compute 43 object-oriented metrics. We assessed four traditional supervised learning algorithms, i.e., Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), and Logistic Regression (LR), to understand their efficacy in predicting security vulnerabilities. We obtained the highest accuracy of 80.6% using the KNN. Thus, the KNN classifier was demonstrated to be the most effective of all the models we built. The DT classifier also performed well but under-performed when it came to multi-class classification.