Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition in Video Surveillance Using Long-Term Recurrent Convolutional Network

Identifying and classifying human activitiesbased on data collected from sensors such as accelerometers and gyroscopes is known as human activity recognition (HAR). HAR has potential applications in various domains, including healthcare, sports, and surveillance. Public safety and security have become increasingly important in today's society, leading to a growing demand for automated human activity detection systems that utilize real-time video surveillance footage. These systems can help detect and prevent potential threats such as vandalism, theft, violence, or trespassing, allowing for a prompt response to incidents. These systems must be able to handle complex scenes, such as crowded areas, occlusions, and changes in lighting conditions. It is a complex problem because of the large number of observations produced each second. Another challenge is that prediction of movements is a complex activity. It is challenging to monitor public spaces continuously; therefore, intelligent video surveillance is required to monitor human activity, classify it as usual or unusual, and generate an alert. The proposed system will use footage from the CCTV camera to monitor human behavior and warn of any suspicious event. This article uses computer vision and artificial intelligence to automatically identify and flag potentially suspicious or dangerous behavior in real-time video surveillance footage. The combination of two neural networks - CNN and RNN in LRCN enables the recognition of both spatial and temporal features of human activities, making it an effective tool for detecting abnormal events. Thus, the proposed model implements principles of human activity recognition by using video footage as input and identifies the activity being performed in the input video.
NLP-Driven Strategies for Effective Email Spam Detection: A Performance Evaluation

In the digital age, spam emails have emerged as a persistent and pervasive nuisance, inundating our inboxes and bringing with them an array of potential threats. These threats encompass phishing attacks, the distribution of malicious software, and breaches of privacy. With the rapid expansion of internet users, this problem has grown exponentially and has been exploited for illicit and unethical purposes. The act of sending unauthorized and potentially harmful links through email has seen a significant uptick in recent years, posing a direct threat to our systems and personal security. Recognizing the urgent need to identify and mitigate fraudulent emails, this project is dedicated to the development of robust techniques for spam email detection. Leveraging the power of machine learning and deep learning algorithms, including Multinomial Naive Bayes, Recurrent Neural Networks, and Support Vector Machines, we aim to tackle this issue head-on. Our approach involves applying these advanced algorithms to large datasets, enabling us to select the most effective solution for spam email detection. The criteria for our selection process are centered around precision and accuracy, ensuring that we employ the algorithm that offers the highest level of reliability and performance in safeguarding our email inboxes.
Harnessing Deep Neural Networks for Micro-Blog Sentiment Analysis: A Novel Approach

Analyzing sentiment in micro-blogs, such as tweets or short social media posts, is a challenging yet important task in natural language processing (NLP). Deep learning techniques have shown great promise in this domain. Here's a novel approach for sentimental analysis using a deep neural network for micro-blogs. With the exponential growth of user-generated content on online platforms, YouTube has emerged as a dominant space for both creators and viewers to interact. One crucial aspect of this interaction is the comment section, where viewers express their thoughts and sentiments. This research presents a robust sentiment analysis model tailored specifically for YouTube video comments, aiming to extract valuable insights from this rich source of user-generated content. Ultimately, our novel deep neural network approach for micro-blog sentiment analysis demonstrates promising results, offering valuable insights into public sentiment across various social media platforms Using a deep neural network, this study presents a sentiment analysis model for YouTube video comments. This research contributes to the advancement of sentiment analysis methodologies, addressing the unique challenges posed by the concise and informal nature of micro-blogs. Our method, a mathematical model, and individuals were utilized to categorize the comments on various blogs. It was assumed that the classification of humans was entirely accurate. The results showed that, with classification accuracy ranging from 90% to 97%, our model is more precise than the numerical model.
Security Testing in The Wild: An Interview Study

Modern software systems are increasingly complex and the risk of falling into security concerns is high if these systems are not developed with a proper security mindset. Despite the empirical studies and security-oriented approaches proposed by researchers and tool vendors, we still point out a lack of knowledge on the security testing processes applied by companies to reduce risks connected to software security. In this paper, we aim to bridge this gap of knowledge by performing an interview-based study with 19 security experts to understand how companies arrange security testing and how the process of security testing is actually performed in practice. Our results highlight that some companies incorporated the figure of the security tester in the software life cycle, yet practitioners reported a lack of standardized guidelines for security testing. From a management perspective, our results suggest that the introduction of formal communication between development and security testing teams may lead to better performance.
Improved Object Detection and Recognition for Aerial Surveillance: Vehicle Detection with Deep Mask R-CNN

Object detection and recognition methods are critical in aerial surveillance applications, allowing us to detect and recognize objects through unmanned aerial vehicles. However, most approaches rely on deleting the background to achieve accurate detection. Current object detection methods in aerial imagery, such as principal component analysis, suffer from low accuracy and high computational load. Therefore, accurate vehicle detection in aerial images is essential for various applications, including traffic nursing, urban planning, and disaster response, facilitating decision-making processes in these areas. This study aims to detect vehicles in aerial images using the Deep Mask-R-CNN deep learning framework. The dataset of aerial sensing images used in this study is from DOTA. The pre-trained VAID dataset is fine-tuned to make the model more suitable for aerial image-specific characteristics. The results indicate that the proposed method performs exceptionally in detecting automobiles from aerial photographs, with high accuracy, low false positives, and high recall rates. The Deep Mask R-CNN framework is well-suited for this task as it provides object detection and segmentation capabilities. It can be useful in applications where vehicle shape and size are important. Additionally, the proposed method is robust in handling scale variations and occlusions, making it suitable for day-to-day scenarios. Furthermore, the model can be easily integrated with other computer vision algorithms, such as multi-scale feature extraction and object tracking, for further improvements.
Customised Roles in Scrum Teams for the Development of Secure Solution

This research paper examines the implementation and impact of customised roles in large-scale agile software development teams, specifically within the Scrum framework. This is a single case study based on 15 practitioners' interviews from a Swedish software company to review the dynamics of role customisation. The two customised roles of Product Guardians and Security Masters are crucial for addressing complex secure software development, maintaining product quality and security, and fostering team self-organisation. The study also uncovers challenges associated with role customisation, such as the potential overburdening of individuals and disruption of selforganisation. The findings contribute to the discourse on agile methods' adaptability and provide practical insights for organisations considering similar role customisations. Furthermore, the research opens the door for future exploration of organization-wide roles that promote selforganisation.
Multi-Factor Behavioral Real Time Authentication for Enhanced Security in Digital Environment

This study presents a novel method of authentication in digital environment in which each element of authentication is linked to one another. Having multiple factors to authenticate and deriving co-relations among these increases the safety and security of the device. Types of behavioral and acoustic patterns which are to be considered are GPS, accelerometer, microphone & speaker fingerprint, lip & tongue movement sensing and pinna shape sensing. Pattern data from different sensors is compared and cross checked. Having multiple factors to authenticate and deriving co-relations among these increases the security of device. The main advantage of multi factor behavioral authentication is that the verification is done dynamically and continuously to provide real time security. All authentication activities are carried out in the background without the user being interrupted. Furthermore, because these authentication approaches do not involve the user, the user experience is enhanced along with the security of the device.
Environmental Impacts and Remediation in Soil Analysis Techniques

Soil analysis reveal the soil's physical, chemical, and biological qualities for diverse uses. Soil analysis is crucial in agriculture, environmental management, and sustainable land use. Soil analysis helps farmers determine soil fertility by measuring nutrients, pH, organic matter, and other parameters. This information helps farmers apply fertilizers wisely to maximise crop growth and minimize environmental impact. Soil analysis can help identify nutrient deficits or imbalances to boost crop productivity and output. Environmental management requires soil analysis beyond agriculture. It assesses ecosystem, water, and human health concerns by analyzing soil composition and pollutants. This information is essential for pollution control, soil erosion management, and land use sustainability assessments. Soil analysis helps clean up contaminated places and protect land resources. Soil analysis promotes soil health and biodiversity for sustainable land use. It evaluates beneficial microbes, soil structure, and organic matter to determine ecosystem resilience. Land managers may mitigate and adapt to climate change through studying soil properties, conserving water, and sequestering carbon. Soil analysis is essential for measu ring soil fertility, detecting nutrient shortages, managing environmental concerns, and promoting sustainable land use. It helps farmers, land managers, and environmental specialists make informed decisions that maximise agricultural productivity, safeguard ecosystems, and sustain our planet's irreplaceable soil resources.
Measuring the Level of Readiness in SDI Al-Hasaniah Students for Computer-Based Exams Using Technology Readiness Index Method

The context of this research is that erratic rainfall can disrupt community activities, especially for traders who want to make sales. In addition, information about rainfall is also needed by farmers in determining planting patterns to get maximum yields. The purpose of this research is to be able to help farmers predict rainfall to get maximum crop yields. One method to be able to predict rainfall is Fuzzy Logic. This research will use the Tsukamoto Fuzzy method. In the research conducted this time, the author conducted monthly rainfall forecasting in Sleman Regency. Rainfall data in Sleman Regency from 2015 to 2022 will be used in this research. This research succeeded in getting a MAPE value of 49.31%. The result of this research is the highest monthly rainfall prediction in November, with a rainfall of 713.78 mm. At the same time, the lowest occurred in August, which amounted to 36.47 mm. This research only gets a MAPE value of 49.31%. So, it can be concluded that the Tsukamoto fuzzy method cannot predict rainfall well.
Dynamic Neural Network with Guaranteed Sensitivity to External Influences

Mathematical models are used to represent a vast array of complex processes in engineering, physics, biology, social science, and economics. Model parameters with significant impacts on identification outcomes are ascertained through parametric sensitivity analysis. Authors previously considered an approximation models for systems with uncertain dynamics using a dynamic neural network. The results obtained from studying the problem of predicting the response variable, indicated that this model has a structural flaw. This flaw manifests as an insensitivity of the weight coefficients to external influences, leading to inaccurate predictions. This insensitivity is marked by the minimal contribution of weight coefficient components in the identification process. This paper discusses modifying learning laws to enhance the sensitivity of the weight coefficients to external signals. Through Lyapunov stability analysis, stable algorithms for weight component evolution that minimize identification error were derived.
Return of Small-Scale Crowd Counting via Fast and Accurate Semi-Supervised Least Squares Model

Existing crowd counting techniques have achieved significant progress with the emergence of deep learning. During development, emerging crowd counting methods have generally become more and more complex and enormous, enabling them to understand and process more prior knowledge from input data. However, they suffer from two major drawbacks: 1) they generally require a significant amount of labeled training samples, which is labor-intensive, and 2) they require increasing computational hardware resources, making it luxurious and impractical to apply directly in small-scale scenes. To address these issues, we formulate crowd counting as a classification problem and leverage least squares model with a novel semi-supervised strategy. Technically, we construct the least squares model based on only two regularization terms: a regression term and a discriminative relaxation term. Moreover, we propose a semi-supervised soft label correcting strategy incorporated in the model. As a result, a fast and accurate crowd counting method is achieved. Experimental results on five small-scale benchmarks demonstrate the proposed method outperforms the other competitors in terms of both regression metrics and consumed time.
The Narrative Experiences of Mental Readiness Facing Educational Transition among Primary School Students

The transition that occurs in the field of education is common and can be experienced by each individual involved in the learning process. Transition shows that an individual has a good and smooth development process. As part of this adaptation, there are a few standard six students who have concerns about some issues such as bullying and anxiety. Ability to develop new relationship with peers help them to cope this transition. This study aims to explore their narrative of mental readiness to face the transition in education among primary students to secondary school. This study employed a qualitative research design with triangulating interview, discussion, and observation methods. There were four themes in this study, 1) to explore the role of the family on students, 2) to understand students' expectations of the transition process, 3) to recognize the level of students' knowledge about the types of secondary schools and finally, to investigate students' skills in building sentences and making simple sentences. The discussion made can make the respondents more prepared and have the goal of entering a school with high grades.
Breaking the Cycle: Exploring the Advantages of Novel Evolutionary Cycles

There have been many different forms of evolution-ary algorithms (EAs) designed by humans over the past 50 years, with many variants optimized for specific classes of problems. As computational resources grow, the automated design of EAs has become an increasingly viable method for improving performance. However, many components of EAs have been treated as largely immutable, for both human and automated designers, dramatically constraining design space. In particular, the evolutionary cycle (the repeating pattern of reproduction and survival) has little or no differences between most popular forms of EA. In a previous paper, we proposed a technique for automatically designing evolutionary cycles using directed graphs, greatly increasing the explorable design space. In this paper, we showcase an improved representation and evolutionary process, provide preliminary experiments demonstrating that EAs produced by this process can outperform those with a traditional cycle, and explore the phenotype landscape to show that the new space explored by our technique may contain better EAs than traditional cycles allow.
Factors associated with adolescent pregnancy: A literature review

The number of high-risk pregnant women always shows a fairly large number every year Adolescence pregnancy is in the spotlight in various countries because of the harmful effects. The purpose of this study was to determine the factors associated with adolescence pregnancy. This research uses the literature review method. The data were sourced from national scientific journal articles obtained through Google Scholar, and Pubmed using the keywords “Adolescent pregnancy", “ Knowledge’’, ‘’Parents”, “Education’’ and " Economic condition". This study shows that the factors associated with adolescent pregnancy are the role of parents, latest education, sex knowledge and economic conditions.
SMART SAFE USING FACE DETECTION METHOD ESP32 CAM

Currently crime cases are undeniable, one of the cases of crime or crime that often occurs in our lives is theft, the role of technology has an important role in the security system, one of which is the security system in the Safe. The author got the idea to design a "Smart Safe With Face Detection Method Use ESP32 CAM" which uses ESP32 CAM which functions to recognize or detect the face of the safe owner, the safe is also equipped with a keypad as an alternative to open the safe and alarm when forcibly disturbed by others. This smart safe technology is expected to help users in safeguarding assets and valuables stored in a safe, so there is no more worry over theft.
Understanding And Implementation Of The Concept Of Natural Disaster Mitigation Materials In Geography Learning At Man 2 Palu City

Natural disasters can occur in a way suddenly or through an ongoing process in a way slowly. Disaster Of course can give rise to impact loss from various aspects, then required understanding to the public from an early age through education in schools/madrasas about types of disasters and their mitigation To minimize the impact caused. Teachers have an important role to provide learning about disasters and mitigating natural disasters. Objective 1. How students understand about draft material mitigation disaster nature in the eyes lesson geography at MAN 2 Palu City, 2. How teachers implement it learning about material mitigation disaster nature in the eyes lesson geography at MAN 2 Palu City. Sample viz student class XI IPS with total 53, one eye teacher lesson geography as informant . Type of research This is a descriptive survey. Data collection techniques through questionnaires , interviews and documentation . Research results showing students' understanding on indicator 1. Types and Characteristics Natural Disaster, obtained percentage an average score of 76% with category good; 2. Understand Cycle Countermeasures Disaster, obtained an average score of 63% with category good ; 3. Analyze Distribution of Areas Prone to Natural Disasters in Indonesia, obtained an average score of 76% with category good ; 4. Identifying Institutions That Play a Role in Countermeasures Natural Disasters, with an average score of 63% is in the category good; 5. Explain Community Participation in Mitigation of Natural Disasters in Indonesia with an average score of 66% is in the good category. Percentage average value _ overall 72.3%, with study results showing students' understanding about draft material mitigation disaster nature in the eyes lesson geography at MAN 2 Palu City is in the category good .
The Use of Podcast to Teach Listening Comprehension to EFL Students: A Library Research

This research provides a review of the current literature on podcast in listening comprehension of EFL students. The type of research is library research study. The aim of this research is to analyze the effect of podcast in improving listening comprehension skills on EFL students. This paper reviewed six articles conducted between 2010-2020 utilizing multiple databases, they are ProQuest, EBSCOhost, and Springer Link through the digital library e- resource Perpustakaan Nasional RI. Furthermore, Google Scholar and Research Gate were used as additional resources. Each relevant publication screened according to some parameters; year of publishing, country, research purpose, research participant, methodology, research finding, type of article, and article in English text. A preliminary synthesis indicates that the majority of studies related to podcasts in improving listening comprehension to EFL students focused on the themes namely: (1) the effect of listening to podcast in improving students’ listening comprehension and (2) how podcast improve students’ positive perception and satisfiction in learning, then finally affected the improvement of students’ listening comprehension skills. A number of suggestions for future research have emerged from the quality and the content of previous studies. They are methodology and quality design of podcast to determine the long-term effects of podcasting on learning outcomes.&#x0D;
Natural Language Processing (NLP) and EFL Learning: A Case Study Based on Deep Learning

The enhancement of artificial intelligence (AI) and related machine learning represents one of many recent technological developments, causing educators to consider the potential of AI for teaching and learning. In this case study, opinions were gathered from six mature students of English as a foreign language (EFL) in a university in Saudi Arabia. The researcher especially explored the use of a voice recognition device (Amazon’s virtual assistant, Alexa) based on natural language processing (NLP) for deep learning. The participants were instructed to interact with Alexa individually for 30 minutes each, over the course of one week, including a game of ‘hide-and-seek’. Structured observations were performed of this activity, and semi-structured interviews were subsequently conducted to gather the participants’ opinions of the device. Main and sub-themes emerged from the results, related to deep learning through NLP. This theoretical perspective was adopted because Alexa was found to develop its voice recognition across different language structures and styles. Moreover, Alexa was very responsive to the participants, sometimes asking them to reword or modify their questions, so that it could find answers. The game also proved helpful for presenting the language, reflecting a real game of hide-and-seek. The participants mentioned that such technology could be useful in language labs, as it was fun, entertaining, convenient, and easy to use. It is therefore recommended to invest in these devices for learning activities in education. Furthermore, statistical studies are recommended to test the impact of voice recognition devices on teaching and learning for generalisable results.