Reviewerly/exHarmony huggingface dataset:
Threat Modeling Through Detection, Prevention &amp; Classification of Leading-to-Vulnerability Code Smells (LVCs)

Code smells are usually ignored as they are neither a bug, nor a vulnerability. Quality engineers and, specially, security architects ignore them. As some of the code smells may lead towards vulnerability which may further be exploited by the hackers, therefore, such vulnerable code smells must be considered and further mitigated by threat modelers. In order to provide a repository of such code smells to security designers, a process had been devised and experimented. During the execution, various web applications had been passed through SAST and resulting code smells had been extracted and then inserted into a new dataset via Python. Later on, the code smells deposited in the dataset had been classified into various categories. Finally, machine learning algorithms had been assessed through WEKA and the fastest as well the most accurate algorithm had been selected. Current security standards do not ensure mitigation of threats caused by leading-to-vulnerability code smells, till to date. Typically, threat modelers assess security of a system through modeling threats via CIA, STRIDE and LINDDUN standards on its DFD and various architectural / infrastructural diagrams. Unless, they do not know that exploitable vulnerability still exists even after performing all secure design principles, the system would still be open to attacks. Our hypothesis was that vulnerable code smells still exist even after complying with all threat modeling standards. In the end, descriptive and inferential statistics had been used to analyse the results as well as test our hypothesis.
Authorship Attribution using Tf-Idf weight with Machine Learning Approaches

Abstract Authorship attribution means automatically determining the authorship of an unrecognize document based on a given sample of text. Authorship attribution has a long history and a wide range of applications. As digitalization is being increased Authorship attribution is playing a vital role in forensics, plagiarism detection, authorship dispute, and research fields. It is very helpful when two persons claim ownership of the same document written by him/her. Authorship attribution is a classification problem, although it uses the techniques of text classification for the text pre-processing despite that it is far different from the goal of text classification because it purely depends on the non-deterministic writing style of the author. The success of the author attribution task highly depends on the writing style characteristics of the authors. Several researchers proposed many different kinds of features such as linguistic, syntactic, semantic, and content-based features to identify the writing style of the authors and many of them have been used by different classifiers to classify the documents. In this paper, we applied tf-idf frequency with FW and sylometric features with support vector machines and parametric and nonparametric methods with supervised and unsupervised classification techniques in authorship attribution. We conducted various experiments with the English corpus gathered from PANCLEF'12. We performed experiments on different feature sets extracted from Corpus with different classifiers and combined these results to improve our success rates. We identified which classifiers give good results on which feature sets. According to experiments, the success rates dramatically change with different combinations of feature sets, however, the test among them are support vector classifier with a bag of words, and Gaussian with function words.
Safe House App: Development of Mobile Application for Handling Household Emergency

Many people have a poor level of emergency preparedness, especially in their home environment. A previous study has stated that only a small percentage of people were well prepared in case of an emergency happening in their home while others are either do not know what to do or do not even think about it. This ignorance can lead to a harmful accident. For example, if an emergency such as a kitchen fire happens and is poorly handled by the people in the house it can lead to bigger accidents such as fire outbreaks. Thus, this study aims to identify the requirement, design, and develop a mobile application that can help users learn about household emergencies and increase their preparedness level for emergencies. Compared to other mobile applications for an emergency, Safe House App provides multimedia elements such as videos, and quiz activity which can attract users to use the application more to learn about the household emergency. The methodology implemented for this study is the Agile Model. Functionality Evaluation is conducted on 10 respondents aged 18 and above and most of the respondents are satisfied with the overall performance of Safe House App and agree that Safe House App can help the user increase their emergency preparedness level and attract them to learn about the household emergency.
Utility of hashing and salting algorithms in quality improvement studies

Owing to the complexity of our health care system, patients access care in various ways and settings, and their medical information is captured in an array of electronic medical records (EMRs) that do not always communicate with one another.[1][1] Thus, if you want to gather information about
Analysis of medical insurance in China by neural network

Medical insurance plays role in the wellbeing of population. However, the difficulty with the amount addition of participants increases dynamically. Especially, the increase of organizations including in this domain makes optimization of medical insurance complex. Thus, it is necessary to find the rule based on the information provided by the related to data as supplementary to support decision making. In order to finish this task, Neural Network, proved its robustness in data analysis, is included in the proposed data management frame. Input and cost as the most important attributes of medical insurance are set as target features. Then, the relationship between these two attributes must be considered. A skipping window is defined to adjust the proportion of the target features in training and testing stages. NN network with double direction of time window for medical insurance is given to foreseeing target attributes. Based on the simulation result, the largest average accuracy is 0.8333.
An efficient method against adversarial attacks

Previous studies to defense against adversarial examples mostly focused on refining the DNN models but have either shown limited success or required expensive computation. In this paper, we introduce a new detection method against adversarial attacks. Since L0 attackers have similar search patterns, to separate clean examples from adversarial examples, we found a new distance measure on output layer. These strategies have low time and computing costs and can be easily complementary to other defenses. Moreover, our method performs well on adversarial noise localization task.
[Tutorial] How to use Curvenote for writing papers

The purpose of this tutorial and article is to describe the process of writing data driven documents and scholarly papers using Curvenote and Jupyter Notebooks. Curvenote is a content authoring platform that lives on the web, similar to Google Docs and similar other platforms that allow you to author papers and documents. You can connect a Jupyter Notebook or a Jupyter lab document to this system so that you can keep codes and content separate but connected. In this sense it is similar to weaving documents such as knitr but simpler than that system. In this paper, we will learn how to use Curvenote to write an academic paper and include sections, paragraphs, text decorations, tables, figures, results of analyses, and citations and cross references. We will also cover how to export the document to PDF and _LaTeX_ for further processing.
Educational Platform to Improve Learning for Children with Autism

The research focuses on children with Autism, who cannot access education either because they do not have a school near their homes or because they are misinformed by their parents that they can access education regardless of their disability. By not receiving an education, children are deprived of their right to education. The objective is to design a prototype of an educational platform taking advantage of technological tools for the learning of children with special educational needs. A hybrid methodology and the Balsamiq tool were used, as they were appropriate for developing the project. The result is that children with disabilities can learn by interacting with technology and in the same way their parents can visualize some exercises and crafts that they can do at home with their children, since it is based on their educational needs. It is concluded that teachers are in total agreement with the use of technology for children with autism, since this will help to improve their learning. The research contributes to society, teachers and parents with children with autism problems.
Reducing elderly home health care cost by developing a cloud based digital platform

The population among elderly people is increasing day by day in the United States and also around the world. There are many devices, products and services which are available for elderly people to get care. But, none of them offer affordable services, especially in the USA where the average cost is enormously high. This has resulted in more elderly people working till their last breath in order to survive as they don't have enough savings to get care. This paper provides an overview of possible solutions where the cost of elderly care can be reduced using a cloud based digital application that can provide a marketplace of caregivers (Nurses, Veterans, Law Enforcements Officers, College Students) to serve the elderly people. Another solution which this paper will talk about is a wearable sensor device where it can track the real time data and through machine learning algorithm it can learn to predict/monitor the elderly’s health condition and send SOS.
Technology as a Key Driver in Enhancing Scientific Education during the Covid-19 Era at the University of Namibia

Abstract: Student profiling on the use of technology for teaching, learning and assessment has a potential to enable educators to enhance their transformational teaching strategies. Therefore, this study explored the views of students on use of technology to facilitate online learning during COVID-19. The study employed a quantitative approach to collect data using a Google Form questionnaire. Nineteen conveniently sampled respondents (38%) were drawn from a population of 50 students registered for first year BSc Microbiology and BSc Environmental Biology in 2021. The collected data was analyzed through descriptive statistics using Microsoft Excel 2010. The findings revealed that during online learning students experienced challenges related to subject knowledge acquisition (63%). Hence; majority (84%) of the students preferred face-to-face or blended teaching learning and assessment to ensure pedagogic access. It is concluded that students’ exposure to online learning helped improve skills such as typing (79%), internet information searching (95%) and general computer skills (89%). Thus, it is recommended that academics and student should be capacitated with training, and higher education curricula should encourage the use of digital technology, which prepares students to be relevant in the digital society.
A Study on the Benefits of Extracurricular Activities for Prospective Teachers Using the Pairwise Comparison Approach

This survey study aimed to determine the benefits of extracurricular activities for prospective teachers by performing a scaling study with pairwise comparison. Participants were 365 prospective teachers studying at different branches of a higher education institution. The data collection tool was designed by taking the opinions of three domain experts and two measurement and evaluation experts about the benefits that emerged from the literature. Each student was asked to rank the benefits of extracurricular activities by making a pairwise comparison. In the analysis of the data, as the chi-square value for error calculated with the Case V model was higher than expected at the 0,05 significance level, the Case III model was used for scaling. In this way, the benefits of extracurricular activities for the development of prospective teachers were scaled through pairwise comparison. The results showed that the order of benefits from the biggest to the smallest were: supporting personal development, supporting teaching skills, supporting social development, instilling positive affective qualities, supporting academic development and supporting physical development. Recommendations for the future include conducting studies to explore the reasons for the less preferred benefits.
High-performance neural population dynamics modeling enabled by scalable computational infrastructure

Advances in neural interface technology are facilitating parallel, high-dimensional time series measurements of the brain in action.A powerful strategy for analyzing these measurements is to apply unsupervised learning techniques to uncover lower-dimensional latent dynamics that explain much of the variance in the high-dimensional measurements (Cunningham & Yu, 2014;Golub et al., 2018;Vyas et al., 2020).Latent factor analysis via dynamical systems (LFADS) (Pandarinath et al., 2018) provides a deep learning approach for extracting estimates of these latent dynamics from neural population data.The recently developed AutoLFADS framework (Keshtkaran et al., 2022) extends LFADS by using Population Based Training (PBT) (Jaderberg et al., 2017) to effectively and scalably tune model hyperparameters, a critical step for accurate modeling of neural population data.As hyperparameter sweeps are one of the most computationally demanding processes in model development, these workflows should be deployed in a computationally efficient and cost effective manner given the compute resources available (e.g., local, institutionally-supported, or commercial computing clusters).The initial implementation of AutoLFADS used the Ray library (Moritz et al., 2018) to enable support for specific local and commercial cloud workflows.We extend this support, by providing additional options for training AutoLFADS models using local clusters in a container-native approach (e.g., Docker, Podman), unmanaged compute clusters leveraging Ray, and managed compute clusters leveraging KubeFlow and Kubernetes orchestration.
Paradoxes of the Ironical in the Works of the Swedish Rock Band Samla Mammas Manna

The article is dedicated to manifestations of irony and humour in rock music as illustrated by the example of the Swedish progressive/avant-rock band Samla Mammas Manna (1969–2008). These manifestations often have a paradoxical (surprising, confusing) effect. What is paradoxical, for instance, is the combinations of artistic and stylistic elements which create a bright sound canvas. Samla Mammas Manna boldly and humorously combined twisted symphonic and rock structures with simple melodic and rhythmic fragments, avant-garde rock with folk elements and free-jazz improvisations. In relation to folklore, there is also a certain irony: it is not homage to ancient art, but its mocking rethinking. There is also a certain paradoxicality in the performing techniques, sometimes very unconventional (but always a brilliant display of technical mastery), and in stage appearance and live acts, inviting the audience to be a part of performance. The article provides a detailed analysis of Samla Mammas Manna’s artistic techniques, style evolution, musical and stage dramaturgy, and ways of interacting with the audience. It also discusses the influence of carnival and circus aesthetics on rock music.
Artificial Intelligence in Forensic Science

Artificial intelligence [AI] is a significant and well-established branch of current computer science that may frequently give a way of addressing computationally massive or difficult issues in a reasonable time period. Digital forensics is a growing field in computing that frequently necessitates the cognitive examination of massive volumes of complicated data. As a result, it appears that AI is an appropriate technique for dealing with many of the present difficulties in digital forensics. The goal of this study is to provide a high-level overview of AI as it may be applied in digital forensics.
A Systematic Literature Review on Vision-Based Hand Gesture for Sign Language Translation

Deaf and hard of hearing people use sign language to communicate. People around mute and deaf people have difficulty communicating with each other if they do not understand sign language. This problem has prompted many researchers to conduct studies on sign language translation. However, there is a lack of compilation of SLR on this topic. Therefore, this paper aims to provide a thorough literature review of previous studies on sign language to text translation based on the vision method. PRISMA (Preferred Reporting Items to writing a standard Systematic Review and Meta-Analyses) is used in this systematic review. Two primary databases, Web of Science and Scopus, have been used to search for relevant articles and resources included in this systematic literature review. Based on the outcome of the systematic review of the topic, the primary studies on sign language translation systems were conducted using self-generated datasets more than public datasets. More static action sign language was studied compared to dynamic action sign language. For the type of recognition, more alphabet sign language was studied compared to digit, word, or sentence sign language. Other than that, most studies used digital cameras rather than Microsoft Kinect or a webcam. The most used classification method was Convolution Neural Network (CNN). The study is intended to guide readers and researchers for future research and knowledge enhancement in the field of sign language recognition.
The Use of Social Media for Infrastructure Developer Company: Study of a New Revenue Stream

The culture of business construction, which currently focuses on winning new project tenders and revenue based on terms, is significantly affected when activity restrictions occur. What happened during the pandemic in 2019 noting that 25% of construction companies could not survive the pandemic. As a result, various innovations need to be deployed to save the business from bankruptcy, so that new revenue streams are needed outside of the ongoing business, one of which is the development of social media. Social media has an increasingly prominent role in impact interactions, and it has been proven that this sector does not experience a decline when almost all sectors are bad in the pandemic era. This research will examine how social media can have an impact on the construction industry, not only increasing participation and driving public opinion but can also be a new revenue stream for companies. This research will focus on companies engaged in toll road infrastructure developer in Indonesia. The case study was designed to combine both qualitative and quantitative methods. The results show that the use of social media in infrastructure development businesses that focus on corporate branding can be developed into a new revenue stream with a content focus, namely project progress, educational shows, and the use of technology impact the influence and awareness.
THE MAIN PEDAGOGICAL REQUIREMENTS AND CRITERIA FOR ORGANIZING TRAINING FOR THE DEVELOPMENT OF PUPILS' PROFESSIONAL COMPETENCE USING MULTIMEDIA ELECTRONIC EDUCATIONAL RESOURCES

In the conditions of modern development of education, the training of pupils with the skills and abilities of socio-psychological and professional adaptation in a rapidly changing world comes to the fore. Education becomes not only a means for the most adequate reflection of the requirements of a market economy and a new society, but also a way to achieve, form the creative, spiritual needs of the individual. Changing the foundations of education is accompanied by a global process of reorientation of the result of education. Therefore, the article presents the investigation of the pedagogical requirements and criteria for the organization of training in the development of professional competence of pupils with the usage of multimedia electronic educational resources. The result of the research depicts that multimedia electronic educational resources are valuable source in improving pupils' professional competence. In addition, the presented requirements and criteria are beneficial in organizing training in the development of professional competence of pupils for multimedia electronic educational resources.
INTRODUCING SCIENCE SUBJECTS IN ENGLISH AT PUBLIC SCHOOLS

In many English speaking countries the governments have already established various science subjects at public schools not only in their native language but also in an international language such as in English. In this proposal, in one of the local schools the process of establishing science subjects, for instance, biology, chemistry and physics in English will be explained. The project is expected to be successful and it also requires high quality teaching staffs who has acquired all language skills during the teaching process if it is carried out in many public schools and it upgrades the level of teaching English for Specific purposes.
POSSIBILITIES OF USING MULTIMEDIA TECHNOLOGIES IN THE EDUCATIONAL SYSTEM

The use of multimedia technologies opens up new opportunities in the organization of the educational process, as well as in the development of student's creative abilities. The joint efforts of educators, scientists, programmers, producers of multimedia instructional materials, and practitioners create a new educational information environment, in which the integration of educational and informational approaches to educational content, teaching methods, and technologies is decisive. is important.
Japanese Military “Comfort Women” Knowledge Graph

Materials related to Japanese military “comfort women” in Korea are managed by several institutions. Each digital archive has their own metadata schema and management policies. So far, a standard or a common guideline for describing digital records is not formalized.&#x0D; We propose a Japanese military “comfort women” knowledge graph to semantically interlink the digital records from distributed digital archives. To build a Japanese military “comfort women” knowledge graph, digital records and descriptive metadata were collected from existing digital archives. A list of metadata was defined by analyzing commonly used properties and a knowledge model designed by reusing standard vocabularies. Knowledge was constructed by interlinking the collected records, external data sources, and enriching data. The knowledge graph was evaluated using the FAIR data maturity model.