Reviewerly/exHarmony huggingface dataset:
An Automated Post-Exploitation Model for Cyber Red Teaming

Red teaming is a well-established methodology for ensuring and augmenting cyber system security; however, the training, expertise, and knowledge of appropriate tools and techniques required to perform effective red teaming come with a significant cost in time and resources. Large organizations such as the Department of Defense (DOD) use vulnerability assessment to identify software patches and other remediations for cyber systems to mitigate cyberspace exploitation. If a patch cannot be applied in a timely manner, for instance to minimize network downtime, measuring and identifying the impact of such unpatched vulnerabilities is left to scarce red teaming services. These services typically concentrate on initial access exploitation, which stops short of exploring the larger security impacts of cyber threats performing post-exploitation actions. This gap in post-exploitation red team analysis results in increased susceptibility to adversary offensive cyberspace operations (OCO) against DOD systems. This research extends the Cyber Automated Red Team Tool (CARTT), developed at the Naval Postgraduate School, by implementing automated red team post-exploitation analysis. The intent of this extended capability is to reduce the workload on limited DOD red teams and penetration testers by providing system administrators with the ability to perform deeper system analysis for the impacts of exploited vulnerabilities.
The digitalization of cultural experiences in physical and hybrid spaces prior to covid-19

Between 2020 and 2021, different urban centers around the world restricted their activities after the waves of infections and the increase in deaths caused by covid-19, from essential activities to those that allow human development, such as social interaction, recreation, and culture. As a result of this measure, pre-pandemic projects are described that have already given new meaning to physical and virtual space and audiovisual design, while standing out as post-pandemic alternatives to be reinterpreted by artists or designers to diversify the user experience in more flexible, open, hybrid and even mobile environments.
The Digital Change Detection for Low Resolution Satellite Images in Weathering Estimation

The detection and estimation of weathering conditions have become a very important daily necessity in our life. For this purpose, several satellites of low resolution imagery were launched by the weathering and environmental agencies. The important weather paremeters are temperuter, wind direction, velocity, clould and humidity, etc. The low resolution images often deal with large-scale phenomena and the interpretation and projection of the produced data requires continuous development of tools and criteria. In this paper, the low spatial resolution data generated by the moderate resolution imaging spectroradiometer (MODIS) were used to monitor the cloud density and direction above Iraq and its neighboring countries for the period from 14-12-2021 to 23-12-2021. The MODIS reflectance and surface temperature (HDF format) data were used and processed to achieve the purpose of this research. The reflectance data were projected according to a sinusoidal system due to the presence of three Earth zones distributions (37, 38, and 39, N). The cloud density was estimated from the digital values and the wind direction was estimated from the bands animation. The direction of wind for the cloudy condition was from the North-East. The digital data processing included data preparation and selection, georeference (adding a projection system), features extraction, and displaying the final results. All data were evaluated using ENVI (Environment for Visualizing Images, ver. 4.5.)
Combination of picturebooks and toys for development of children’s literacy: advantages and limitations in the context of play-based pedagogy

Educators and parents are constantly searching for new ways to develop children’s literacy. Playing in a rich environment enables even the youngest children to explore literacy and develop their literacy skills. With the rapid development of technologies in book publishing, picturebooks are becoming an increasingly common element of such an environment. This article analyses how a picturebook can be used in the context of play-based pedagogy and linguistic diversity. The results of the study on reading a picturebook with children under 9 years of age are presented (N68). The research data demonstrated that reading a picturebook with a toy helped to raise interest and retain the attention of preschool children and primary school pupils, stimulated their linguistic activity, and helped to better understand the book; yet in some situations it had a negative effect on teacher-child interaction, distracted from the written work, and hindered the possibilities to develop active reading skills offered by the picturebook. Reading a picturebook with a toy could be seen as a means of individualising education when working with pre-schoolers or children with learning difficulties. The research shows that reading with a toy can both foster the interaction between the teacher and the learner and hinder it; so, the integrity of all the elements of the educational situation is important: the learner’s capabilities, peculiarities of the picturebook and the toy, as well as the teacher’s skillfulness./ Keywords: picturebooks, play-based pedagogy, linguistic diversity, elementary education, early education.
CLASSIFYING IMBALANCED DATA BASED ON DOS AND DDOS ATTACKS

In the first quarter of 2022, researchers witnessed over 2.8 million Distributed Denial of Service (DDoS) attacks, as reported by Info Security magazine on May 18, 2021.DDoS has become a serious issue for many organizations and individuals.Machine learning algorithms (MLAs) have become a tool to help thicken the layers of defense.To be effective, MLAs must be trained in ways that provide high confidence for detection and prevention.The goal is to develop models that can predict (i.e., classify) with high precision and accurately identify different types of DoS/DDoS attacks with low false positive/negative rates.In addition to dealing with the multiclass classification and extremely imbalanced problems.The derived model leverages two feature selection techniques to reduce the number of features and help improve the model's execution time.A combination of under-sampling combined with adjusting weights was applied to handle the imbalance problem.The extracted data was evaluated using supervised MLAs, including Random Forest, Decision tree, and Logistic regression.The experiments utilized the popular benchmark called NSL-KDD dataset.Random Forest achieved the best performance results, decreasing by 37% the training and testing time.In addition to solving the imbalance problem, it increased accuracy by 6.25% and FPR 21%.The random forest model has achieved 99% accuracy and 0.0001 for the False-Positive rate.Furthermore, using this setup, we can detect minor classes with more than 80% accuracy.
A Review on Data Breaches in Healthcare Security Systems

Providing security to Health Information is considered the topmost priority compared to any other field.After digitalizing patients records in the medical field, the healthcare/medical field has become a victim of several internal and external cyberattacks.Data breaches in the healthcare industry have been increasing rapidly.Despite having security standards such as HIPAA (Health Insurance Portability and Accountability Act), data breaches still happen on a daily basis.All various types of data breaches have a similar harmful impact on healthcare data, especially on patients' privacy.This paper aims to understand the aspects that led to healthcare data breaches via ransomware incidence and their impact on the patients and healthcare providers.In addition, the paper reviews the current possible solutions to improve the healthcare security system by analyzing the efficiency of these solutions.We studied the most significant healthcare data breaches via ransomware attacks that occurred in the U.S. from 2015 to 2020.We analyzed the obtained data from different academic and business sectors resources that target the reasons for the healthcare data breaches.
Review of Breast Cancer Prediction using Machine Learning Techniques

Breast cancer is a malignant neoplasm that develops in the breast tissues.Among the top causes of mortality in women, it is the most frequent form of cancer in females worldwide.Machine and deep learning approaches based on AI can successfully locate the prediction model.In this study, we examine the state of the art in applying machine learning to forecast breast cancer.The prediction model's important parameters are its accuracy and error rate.
From Web Scraping to Machine Learning: Approaches and Tools for Social Network Mining

Social network mining is the process of extracting, analyzing, and modeling social networks from online platforms.It has become an important tool for understanding the structure and dynamics of online communities and for predicting the spread of information, influence, and behaviors.In this article, we review the background, methods, and applications of social network mining, with a focus on the most common techniques and approaches.We also discuss some of the challenges and ethical issues related to social network mining and suggest directions for future research.
A Method for the Prediction of Breast Cancer using Deep Convolutional Neural Networks

After lung cancer, breast cancer is the most common kind of the illness.In terms of prevalence, lung cancer is far and by the winner.When comparing males and women of reproductive age, breast cancer disproportionately affects women.Since the root cause of breast cancer remains unknown, early diagnosis is crucial for lowering mortality rates.Cancer survival rates might improve by as much as 8% if diagnosed and treated early.X-rays, mammograms, and even MRIs may fall under this category.Exactly what is the problem Extremely small masses and lumps may be difficult for even the most experienced radiologists to detect, which can lead to a high rate of false positives and negatives.To put it mildly, this is a very worrying development.Many individuals are working to improve breast cancer detection applications so that the disease may be detected at an earlier stage.New technology allows for the analysis of photographs, which may then be used to teach itself.In order to distinguish between calcifications, masses, asymmetry, and carcinomas, we used a Deep Convolutional Neural Network (CNN) in this study.Studies that came before us often used simple methods to achieve this end.Cancer was classified as benign or malignant, allowing for more targeted therapy.Previous training has been completed on a model.We first use this strategy for completing transfer learning effectively.ResNet50.Our model was similarly improved to better accommodate deep learning.The significance of a neural network's learning rate during its training phase cannot be emphasised.Using the technique we provide, the learning pace may be adjusted to fit new circumstances.An individual learning anything new is certain to make a few blunders along the way.
Visual attention learning and antiocclusion-based correlation filter for visual object tracking (Erratum)

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.
Organising the ERUA Open Science Meet-Ups:

The field of Open Science is subject to constant change and expansion. In order to stay up to date, exchange is paramount – not only within universities, but also within larger spheres, such as academic alliances. This paper analyses the development of a format of international Open Science-related exchange called “ERUA Open Science Meet-Ups” within the Re:ERUA project (research trajectory of the European Reform University Alliance). We will look at in which contexts the format came to be, how we got the format started and which measures of promotion and dissemination we took. Moreover, we will discuss the individual sessions’ scope and provide insight into which lessons we learned when performing the Open Science Meet-Ups. Specific recommendations for everyone wanting to set up a similar format as well as an outlook on future sessions will conclude the paper.
MASA Microconic Over-Grip Spindle Collet Range Grown

Expanded range of MASA Microconic over-grip spindle collets; plus, new Norelem pneumatic clamping
The Effect of using inductive, Deductive, and Interactive Approach on Students’ Achievement in teaching Grammar to develop writing skills

This study aims to implement grammatical competence strategies (inductive, inductive, and interactive) in developing students' writing in grammar and their attitudes towards English as a foreign language.To achieve the purpose of the study, the researchers applied the tools to target a sample of the second intermediate grade female students at Maysaloon Girls School in two groups, Experimental and control were used in this study.The experimental group was taught the inductive, deductive, and interactive strategies, and then the control freeware was studied in the traditional way method.While at a time when new trends in language teaching encourage the use of interactive methods of stimulation and keeping student interest, and the constant tendency to keep a teacher-centered approach to teaching English grammar is counterproductive.The most effective methods currently available are monotonous by heart.Learning needs no longer dominate the main part of the lesson.The interactive strategy is multi-skilled and learner-centered and designed to teach a specific grammatical concept will also be introduced.To answer the main research question of whether inductive, deductive, and interactive methods are so the researchers used a more effective introductory method of teaching grammar instead of the traditional method, so grammatical competence plays a significant role in student language.When students have Grammatical Competence, they will be able to develop their writing comprehension.The present study aims to.
Literature Review: Association of Iron Deficiency Anemia with Cognitive Function in Adolescents

The prevalence of anemia in adolescents in Indonesia shows a high number with a percentage that is close to the category of severe public health problems. Anemia has an impact on the decline in cognitive function which in turn will affect intelligence, mental health, psychosocial, and the maturation process. A literature review study design by searching for research articles on the parameters of the relationship between iron deficiency anemia and cognitive function of adolescents aged 10-19 years using Google Scholar and ScienceDirect databases in the range of publication years between 2017-2022. 10 selected articles with 8 articles being conclusive and 2 articles being inconclusive, within parameters of learning achievement, school performance, concentration, cognitive response, cognitive behavior, irritability, expressed emotion, EEG, and mediation models In general, there is a significant relationship between anemia and cognitive function. However, this function itself has various aspects and measurement parameters so that further systematic studies are needed to find deeper patterns of relationships in a wider range of age groups.
SPOTTING FORGE NEWS USING ML TECHNIQUES

This project aimed to develop a tool using natural language processing and machine learning techniques to detect fake news.Humans can often fail to detect fake news, so automated tools can be valuable in this task.However, there are cases where even reliable sources release fake news, making it challenging to detect.This project successfully develops a model that can identify language patterns that characterize fake and real news, showing the potential of machine learning in this task.The model is capable of detecting many indicators of fake and real news, making it a valuable tool for detecting fake news.
CWE Ontology

CWE is a community-supported database of known weaknesses. It is under permanent update and development. MITRE Corporation hosts this database. It consists of several viewpoints structured at several abstract levels. CWE database is the base of CWE ontology. It redefines weaknesses in terms of the Semantic Web. This ontology is a part of ontology ecosystem developed to capture cybersecurity knowledge on known vulnerabilities, weaknesses, and attacks patterns. CWE ontology classifies CVE/NVD vulnerabilities. It is useful for research and investigation on new vulnerabilities and weaknesses using reasoners. In addition, CWE is useful for cybersecurity incident forensic investigations, software acquisition and development.
Exploration on the Construction of "New Engineering Ability" for Local Universities to Serve Regional Economic Development

Local universities mainly serve regional economic development and cultivate new engineering is applied talents for local economic development. The construction of "new engineering capacity" was introduced from the aspects of regional economic development, school-enterprise cooperation and government support, and explores the characteristic innovation mode that adapts to local universities in serving regional economic development. It is realized the dynamic connection between the professional setting of localuniversities and the structure of the regional economy, and meeted the cooperative supply relationship between universities and enterprises.
Malayalam Verb Analyzer: An Alternative Computing Model

The Morphological Analyzer of any language is under increasing scrutiny based on its functional accuracy.In the context of functional inadequacies of Malayalam Morphological analyzers identified in currency, this study proposes a new model incorporating both linguistic and computational aspects for computational verb analysis.This maiden endeavor is a departure from the morphological approaches like suffix striping, paradigm appraoch, Directed Acyclic word diagram; Corpus based approach Finite state Transducer, Two level morphology and Finite State Automata.An alternate model for verb analysis is presented with the expectation of resolving the existing predicaments in computational verb analysis.Interlinking linguistic and computational aspects in establishing a verb analyzer model would be a welcome step to follow in agglutinative language based morphological models.
Detecting and Blocking Malicious URLs

Malicious URL, a.k.a.malicious website, is a common and serious threat to cyber security.Malicious URLs host unsolicited content (spam, phishing, drive-by downloads, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year.It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cyber security industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out important directions for future research.
A Survey of Video Steganography

For transmitting the secret data, security issues should be considered because hackers may use frail connection over correspondence system to take data.Steganography helps to send confidential data between two parties.Steganography can be classified as text, image, video and audio steganography.Video steganography is a field of steganography where videos are used to hide information.In a video steganography, we can hide large amount of data because it is the combination of image and sound.Therefore, image and audio steganography techniques can be occupied on the video.In this paper, we present a survey on different video steganography techniques.
Soma Segmentation Using U-Shaped Convolutional Neural Networks with Weight Boundary Mechanism

Soma segmentation is crucial for subsequent analysis of brain images, and can be used for analysis of neuronal morphology and other aspects.Although the results of manual soma segmentation are more accurate, this method is very time-consuming and labor-intensive, so an accurate method of automatic soma segmentation is needed.Existing optical imaging images of the brain tend to be large, and one way we segment somas is to take the approximate coordinates of the center of the soma we need, then cut the image into small cubes one by one, and segment the somas in the small cubes.However, in this cube, in addition to the soma roughly located in the center that we want to segment, there are often other somas in the periphery, especially when they are close to our target soma, and some of them are only left incomplete during the cutting process.In addition, due to some defects of optical imaging and other problems, it leads to many images with low signal-to-noise ratio, which severely affects the segmentation effect.Therefore, we incorporate two mechanisms in the training of U-shaped convolutional neural network to alleviate the above problems, one is soma edge identification, and the other is the enhancement of multi-soma construction with soma shifted.Compared with existing popular medical image segmentation models, our method is significantly better in soma segmentation.
Indonesia-Germany MathCityMap training: Shifting mobile math trails teacher training to a hybrid environment

The purpose of this study is to explore how MathCityMap training for teachers can be designed and implemented in a hybrid setting. Design research has been carried out involving teachers from 34 provinces in Indonesia. A four-day model training program for indoor and outdoor activities in a hybrid setting has been designed. The core concept of designing modeling tasks and math trails, as well as the introduction to MathCityMap, were delivered in a hybrid mode between Indonesia and Germany. The activity continued with the development of MathCityMap Trails in city parks. Post-training activities were conducted in their respective places of origin, in schools, and in city parks. Participants have successfully designed trails in both their schools and the city parks where they live supported using Learning Management System. The findings of the MathCityMap team's review indicate that most of the trails fulfilled the requirements. The trainee teachers then disseminate their knowledge and experience to their colleagues. They also used MathCityMap Trails for classroom learning as well as other activities such as MathCityMap competitions and public educational activities. The hybrid environment helps to accelerate the attainment of training objectives while also broadening the program's reach and network.