Reviewerly/exHarmony huggingface dataset:
MPAA Rating Prediction Using Script Analysis for Movies Using Ensemble Modeling

Movies serve as a form of artistic expression, utilizing the medium of film to communicate ideas, stories, emotions, and atmosphere to create a specific experience for the viewer. The messages and themes in movies have the power to shape social attitudes and potentially bring about change. However, it is important to consider the potential negative effects that certain scenes and dialogues can have on vulnerable members of society, such as children. They can be considered a reflection of society’s current attitudes. The Motion Picture Association film rating system (MPAA) has been utilized since 1945 to classify movies and TV series based on factors such as violence, language, and sexual content. This research aims to use data analysis and machine learning to predict the MPAA rating of a movie based on its script by splitting the script into categories such as “angry,” “sad,” “happy,” “surprised,” and “fear” and examining the impact of each on the rating. An ensemble method is proposed to improve the accuracy of the prediction. Additionally, a bidirectional LSTM-based model with attention is proposed to classify the movie into one of five classes: R, G, PG, PG-13, and NC-17.
Software Defect Prediction Using Cellular Automata as an Ensemble Strategy to Combine Classification Techniques

The concern about the software development and maintenance costs increased the interest in defect predictions. It is possible to create classifiers capable of identifying software modules prone to failure from machine learning techniques. The choice of the most appropriate techniques relies on the characteristics of each problem/context and the previous knowledge of the available methods. One way to address this limitation is to combine several techniques to improve the final overall performance. This work proposes a defect prediction ensemble solution that combines several classifiers using a self-organizing cellular automaton. The proposed solution presented satisfactory accuracy compared to common methods and literature ensemble solutions to public datasets. The cellular automaton ensemble solution has shown promise for generic prediction problems.
Designing an autonomous robot prototype for detecting traffic violations

To ensure safety on roads inside the cities, smart traffic system is an efficient technique for managing road traffic. In this paper, a prototype of an autonomous robot for traffic management is designed and implemented. Two algorithms are proposed and tested (by using the proposed robot prototype) for warning the driver regarding the traffic rules violation and further recording the video evidence of the traffic violation. The main hardware components of the proposed prototype are: a distance sensor, a servo motor, a DC motor and a microcontroller (Arduino UNO), which are simple and cost-effective devices, Arduino programmed with open source language (C++).The designed robot is equipped with ultrasonic sensors measure the distance between the robot and the first car on the white line. This paper discusses the main differences between the static robot and the mobile robot and the main points of similarity and difference between the proposed scenarios are demonstrated in a comparison table.
A novel hybrid face mask detection approach using Transformer and convolutional neural network models

Face and face mask detection are one of the most popular topics in computer vision literature. Face mask detection refers to the detection of people's faces in digital images and determining whether they are wearing a face mask. It can be of great benefit in different domains by ensuring public safety through the monitoring of face masks. Current research details a range of proposed face mask detection models, but most of them are mainly based on convolutional neural network models. These models have some drawbacks, such as their not being robust enough for low quality images and their being unable to capture long-range dependencies. These shortcomings can be overcome using transformer neural networks. Transformer is a type of deep learning that is based on the self-attention mechanism, and its strong capabilities have attracted the attention of computer vision researchers who apply this advanced neural network architecture to visual data as it can handle long-range dependencies between input sequence elements. In this study, we developed an automatic hybrid face mask detection model that is a combination of a transformer neural network and a convolutional neural network models which can be used to detect and determine whether people are wearing face masks. The proposed hybrid model's performance was evaluated and compared to other state-of-the-art face mask detection models, and the experimental results proved the proposed model's ability to achieve a highest average precision of 89.4% with an execution time of 2.8 s. Thus, the proposed hybrid model is fit for a practical, real-time trial and can contribute towards public healthcare in terms of infectious disease control.
Information technologies as organizational support for the COVID-19 coping actions: Nurses’ discourse

to analyze nurses' discourse about the potentialities in using information technologies as organizational support for the COVID-19 coping actions in Primary Health Care.a qualitative and exploratory study conducted in the Family Health Strategy units from the city of João Pessoa, Paraíba, Brazil. Data collection was carried out from September to November 2021 with 26 nurses selected through the snowball technique, resorting to a semi-structured interview script. The empirical material was organized in the Atlas.ti 9 software and grounded on the theoretical-methodological contribution of Discourse Analysis, French Line.three discursive blocks were evidenced: innovation based on social media; health education actions; resoluteness in organizational actions, presenting the relevance of the WhatsApp®, Instagram® and Facebook® apps as strategic resources, in order to collaborate in the Primary Health Care are with the organization of health actions against COVID-19 by nurses.health units have the potential to strengthen the assistance provided through digital organizational devices; however, they need political support that invests in the structure and strategies to enhance organization of the health actions.
Development of a Hybrid Policy Development Framework to Combat Cyber Threats During Crisis Events

From the early months of 2019 to present day, the spread of the SARS COVID-19 virus has affected every aspect of modern-day life while having an especially substantial impact on the way that businesses carry out their day-to-day operations. The large spike in cybercrime can be accredited to the fact that most workers were thrust into a scenario that they were not prepared for. This could be due that most office workers lack technical skills that link into cyber security awareness and had previously relied on their in-office administrator to protect their business from outsider threats. Each office worker became an individual weak point that could be targeted outside of the protection of a configured office network. To minimise threats that companies may face when put into a crisis situation, this project will be conducted by carrying out research into cyber threat experiences that companies faced during the COVID-19 pandemic, analysing previously adopted methods and conducting surveys with various office workers to propose a solution that ensures workers are put at minimal risk.
Adaptive Incident Response Plans for Cyber Resilience in Small and Medium Enterprises

This study addresses the lack of cyber-security present in a small petrol station, with a supermarket on the inside, in Dorset, England. A mixed-methods approach is implemented to create a more in-depth study to gather as much information as possible. Suggestions for implementing anti-virus software or the appropriate firewall setup are provided to enhance those problem areas. Detailed evidence for gathered information in the process is included. The solution addresses the problem by tackling the lack of cyber-security and incident response at the enterprise by providing the company with a detailed and industrial standard incident response plan. The plan consists of contact details, steps in the response process, and additional steps for some of the most likely cyber-attacks in retail, such as ransomware. A conclusion consisting of future improvements and drawbacks to set objectives and success criteria is given.
Cybersecurity Workforce Development Through Innovative High School Programs

In 2022, there were more than 4,100 data breaches that were brought to public attention, which resulted in nearly 22 billion people's information being compromised. When compared to 2021, when the number of cybersecurity specialists stood at 2.72 million, this shows a significant rise in the severity of the problem. Many businesses run the danger of having their organizations put at a “moderate” or “severe” risk of being attacked by cybercriminals due to a lack of workers. This study explores the value of cybersecurity skill and workforce development programs in high schools, especially those on Native American reservations, rural areas, urban areas, and low-income areas.
Information Security Threats in the Internet of Things (IoT)

Automatic number plate recognition (ANPR) cameras are used by local authorities and law enforcement to monitor traffic flow and collect data. ANPR captures the registration plates on passing vehicles. This chapter will consider their use against existing policies and inherent risks associated with the management of the data they produce, focusing on economic theory and considerations, predominantly within law enforcement. The chapter finds that creating well-written policies and legislation ensures accountability and responsibility. Ensuring the correct budgeting models are applied both locally (microeconomy) and at the government (macroeconomy) level, the ANPR infrastructure can remain secure and effective. The policy must also include sharing security information and event management (SIEM) logs between law enforcement agencies to share actor intelligence as there is no risk of IP disclosure, reducing the risk of a tragedy of the commons. This will also improve public confidence.
Benefits of Information Security Awareness Training Against Phishing Attacks

Phishing attacks are human-targeted attacks, and it may not always be possible to counter them with technical measures alone. By their nature, humans have a natural weakness of desiring to believe. Cyber attackers who analyze this vulnerability well have been exploiting this in order to achieve success in their respective attack targets. This study focuses on reviewing the benefits of information security awareness training against phishing attacks and aims to provide insight on this through the results of a field study. The field work was carried out in Turkey on four different scenarios on the success of information security trainings against phishing attacks. The data obtained as a result of the field study were compared with the data obtained in international studies, and the maturity of the non-regulated institutions in Turkey against phishing attacks was measured. When the data obtained as a result of the field study were compared with the international results, it was evident that the test group subject to the study exhibited a success below the international values.
Cybersecurity Leadership Ethics in Healthcare

Ethical leadership transforms and unites social systems around common purposes of ethicality leveraging organizational connectedness. Cybersecurity and the ethics around it create a variety of complexities. Often healthcare organizations do not openly disclose the extent and nature of cyber data thefts and breaches, which puts those whose information has been exposed at significant risk. It often is whistleblowing that leads to the public finding out the severity of the attack. This requires the need to understand the importance of ethical organizational cultures and the decision-making process that takes place when medical organizations have cybersecurity breaches. Leadership value systems mitigate subjectivity constituting ethical themes of moral character and virtues to advance organizational trust. Healthcare organizational cybersecurity leaders employ strategic foresight to forge connections with ethicality. Ethical leadership implores professional competence and exemplary service cultivating in social responsibility.
A study on the Affordance of Educational Contents based on Virtual Reality

The social changes sparked by the COVID-19 pandemic have highlighted the urgent need for rapid changes in the educational environment and teaching and learning systems, leading to a paradigm shift in the education sector as a whole. In response, this study aims to establish a system for virtual reality-based educational content by exploring ways to overcome the limitations of online education in existing e-learning environments and maintain the unique learning experiences of face-to-face education while enabling interaction between learners. Through a theoretical background, the concept and general characteristics of virtual reality content were examined, and factors that maximize the immersion experience of virtual reality were identified, including the sense of presence. In addition, the importance of affordance as a key factor for incorporating virtual reality into educational content was explored, along with its relationship with the sense of presence. Based on this, a system for constructing affordance in virtual reality-based educational content was diagrammed. In future research, experiments and surveys will be conducted to derive empirical results on the response of real learners based on the system derived from this study. We hope that this study will be helpful in providing more learner-friendly virtual reality education content in the future.
Case-Based Learning(CBL) Model Development and its Application for Data-Based UX Design Education

As people get used to using various types of data, more and more attempts are being made to use data as a material for creating design concepts in the design process. Because data can provide more objective insights, designers can design products in the right way through data. Recently, various studies have been conducted on data-based design. There are studies focused on the individual concepts of Data Driven, Informed, and Aware design, which can be said to be a classification of data-based design, but domestic studies conducted by classifying data-based design in practice are insufficient. Therefore, it is not easy for beginners who lack data-based design experience to use data-based design methods separately. This study aimed to learn the relationship between quantitative data and decision-making by having a mind set on how users who are not familiar with the design using data make decisions using data. Case-based learning was used to design educational programs that help beginners understand, and case papers and criteria that beginners can easily participate in to help them learn the processes and types of data-based design. The educational program conducted education for beginners in data-based design, and verified its usefulness through expert evaluation. The educational program developed in this study is expected to be used as a way for beginners to understand and find out how data-based design is implemented.
Clustered Scheduling and Communication Pipelining for Efficient Resource Management of Wireless Federated Learning

This article proposes using communication pipelining to enhance the convergence speed of federated learning in mobile edge computing applications. Due to limited wireless subchannels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computations, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different data sets and deep learning architectures.
Learning From Imbalanced Data Using Triplet Adversarial Samples

The imbalance of classes in real-world datasets poses a major challenge in machine learning and classification, and traditional synthetic data generation methods often fail to address this problem effectively.A major limitation of these methods is that they tend to separate the process of generating synthetic samples from the training process, resulting in synthetic data that lack the necessary informative characteristics for proper model training.We present a new synthetic data generation method that addresses this issue by combining adversarial sample generation with a triplet loss method.This approach focuses on increasing the diversity in the minority class while preserving the integrity of the decision boundary.Furthermore, we show that reducing triplet loss is equivalent to maximizing the area under the receiver operating characteristic curve under specific conditions, providing a theoretical basis for the effectiveness of our method.In addition, we present a model training approach to further improve the generalization of the model to small classes by providing a diverse set of synthetic samples optimized using our proposed loss function.We evaluated our method on several imbalanced benchmark tasks and compared it to state-of-theart techniques, demonstrating that our method can deliver even better performance, making it an effective solution to the class imbalance problem.
ProtoSimi: label correction for fine-grained visual categorization

Abstract Deep models trained by using clean data have achieved tremendous success in fine-grained image classification. Yet, they generally suffer from significant performance degradation when encountering noisy labels. Existing approaches to handle label noise, though proved to be effective for generic object recognition, usually fail on fine-grained data. The reason is that, on fine-grained data, the category difference is subtle and the training sample size is small. Then deep models could easily overfit the noisy labels. To improve the robustness of deep models on noisy data for fine-grained visual categorization, in this paper, we propose a novel learning framework named ProtoSimi. Our method employs an adaptive label correction strategy, ensuring effective learning on limited data. Specifically, our approach considers the criteria of exploring the effectiveness of both global class-prototype and part class-prototype similarities in identifying and correcting labels of samples. We evaluate our method on three standard benchmarks of fine-grained recognition. Experimental results show that our method outperforms the existing label noisy methods by a large margin. In ablation studies, we also verify that our method is non-sensitive to hyper-parameters selection and can be integrated with other FGVC methods to increase the generalization performance.
Prediction model of students' learning behavior on learning effect in online live class based on machine learning algorithm

With the reform of information-based education, online learning has become a regular way of learning. In order to better help online learners to learn and promote the improvement of teachers' online teaching quality. In this study, the machine learning algorithm was used to mine the data such as the online live class learning behavior of university students on the Super Star Learning platform through Python software, and the online teaching effect prediction model was constructed to better predict the learning effect evaluation of online learners.
Blockchain-enabled Local Market for Sharing Storage in Energy Communities

This paper proposes a blockchain-based local market mechanism to allocate physical storage rights (PSRs). The proposed blockchain framework serves as the ideal platform to support the market operation and settle the payments among the market participants. As a market product, PSRs are provided by the storage owners, who are solely profiting by selling the PSRs, and enable the local market participants (including arbitrageurs, renewable producers, consumers and prosumers) to access the storage. That is, they can book storage in the form of PSRs and dispatch it at a given time aiming to maximize their utility function. Numerical results from case studies show the economic benefits of storage participation in the energy community, demonstrating at the same time the benefits of the proposed PSR allocation mechanism for both the system and the individual market participants.
Revealing interpretable object representations from human visual cortex and artificial neural networks

Predictive models are often limited by their strong focus on prediction accuracy, leading to potential for shortcut learning and limited out-of-set generalization. Recent interpretability methods have focused primarily on understanding the contribution of individual features or image regions to classification performance, but have placed less emphasis on the larger set of representational motifs that are being learned by predictive models. In this talk, I will highlight recent work from our own group aimed at revealing interpretable object representations from human behavior, patterns of brain activity, and artificial neural networks. Our approach operates at the level of triplet similarities and yields low-dimensional human interpretable embeddings with excellent reconstruction accuracy, providing both perceptual as well as semantic representational dimensions. By providing a trade-off between complexity, interpretability and performance, this approach can reveal important contributions to prediction performance that may be useful for improving future predictive models.
Pedestrain Detection Based on Improved CSP Network

This paper presents a method based on improved Cross Stage Partial (CSP) network. Firstly, the original backbone network Resnet50 of CSP is replaced by Resnet101, which better extracts features and solve the problem of pedestrian detection with occlusion. Secondly, a channel-based and compressed attention mechanism is introduced to obtain higher training speed and detection speed. Finally, the non-maximum suppression algorithm is used to form the candidate frames and retrain difficult samples. The experimental results show that the proposed approach achieves better results on the Cityperson dataset and improves the performance of pedestrian detection in complex scenes.
Effectiveness of Using OWASP TOP 10 as AppSec Standard

Application Security (AppSec) includes variety of practices included in software development process with aim to minimize software vulnerabilities. The goal of this research was to emphasize lack of AppSec standards, the importance of it and what are the consequences with main focus on nowadays most known project about web application security – OWASP top 10. Authors of this research tried to, using practical examples and real-life scenarios, represent the benefits of including OWASP top 10 awareness document in the process of AppSec programs development. As a result of this research, it would be demonstrated how OWASP top 10 actually has the most realistic representation of nowadays security threats.