Reviewerly/exHarmony huggingface dataset:
Perbandingan Hasil Recovery Tools Mobile Forensic Di Smartphone Android Menggunakan Metode National Institute Of Justice (NIJ)

Penggunaan smartphone di Indonesia mencapai 192,15 juta pengguna aktif pada tahun 2022, menjadikannya pasar terbesar keempat di dunia. Namun, pertumbuhan ini juga berdampak pada meningkatnya tindak kejahatan siber, tercatat sebanyak 8,831 kasus selama tahun tersebut. Penelitian tentang efektivitas alat pemulihan mobile forensic pada smartphone Android dapat menjadi acuan pemilihan alat uji mobile forensic baik untuk kebutuhan penyelidikan maupun akademis. Pada penelitian ini dilakukan pengujian terhadap dua tools mobile forensic, yaitu Andriller yang merupakan aplikasi open source, dan MOBILedit Forensic Express Pro yang merupakan aplikasi berbayar, untuk mengetahui seberapa tingkat recovery yang mampu dilakukan oleh kedua tools tersebut. Penelitian dengan metode National Institute of Justice (NIJ) menemukan bahwa kedua software yang digunakan dapat digunakan untuk analisis mobile forensic tanpa akses root ke smartphone. Hasil pengujian menunjukkan MOBILedit Forensic Express Pro memiliki tingkat pemulihan data yang lebih tinggi (83,33%) dibandingkan dengan Andriller (50%). Dari hasil ini diharapkan dapat membantu dalam memilih alat atau software mobile forensic dalam proses penyelidikan maupun kebutuhan akademis. Kata kunci: digital forensik, mobile forensic, national institute of justice ------------------------------------------------------------------------- The usage of smartphones in Indonesia reached 192.15 million active users in 2022, making it the fourth largest market in the world. However, this growth has also led to an increase in cybercrime, with 8,831 cases reported during that year. A research on the effectiveness of mobile forensic recovery tools on Android smartphones, can be a reference for selecting mobile forensic testing tools for both investigative and academic needs. In this research, two mobile forensic tools were tested, namely Andriller, which is an open source application, and MOBILedit Forensic Express Pro, which is a paid application, to find out what level of recovery these two tools are capable of. Research using the National Institute of Justice (NIJ) method found that the two software used can be used for mobile forensic analysis without root access to a smartphone. The test results indicated that MOBILedit Forensic Express Pro achieved a higher data recovery rate (83.33%) compared to Andriller (50%). These findings are expected to aid in selecting suitable mobile forensic tools for investigative and academic purposes. Keywords: digital forensik, mobile forensic, national institute of justice
Real-Time Traffic Light Recognition with Lightweight State Recognition and Ratio-Preserving Zero Padding

As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation.
Problematika Kemampuan Literasi Membaca Siswa Sekolah Dasar

Reading is an activity that has the aim of understanding the content of what is written and is also a skill that elementary school students must master because with good reading skills students can learn a lot of knowledge. The purpose of writing this journal is to describe the problematic factors of low reading ability in elementary school students. This type of research is qualitative research using the library study method, with data collection techniques from various reading sources in the form of articles on various types of problems in elementary schools. The research results show that 1) The first problematic cause of low reading ability is students' low interest in reading, books that are less interesting to students, inadequate libraries, short reading time of only 15 minutes. 2) The role of parents in increasing literacy, is providing motivation and encouragement, 3) The role of teachers in increasing literacy, providing media and methods that attract students' interest, teachers providing reading corners in the classroom.
Evaluating the Usefulness of Least Squares Regression in Petrophysics Interpretation

This paper is to update the information provided in the author’s previous papers on evaluating the uncertainty in least squares results. It is prompted by new information that shows that the usefulness of any least squares result cannot be guaranteed by conventional statistics (such as R-squared, F-statistic, or standard error of the regression, sigma). A new method based on the singular value decomposition (SVD) of a matrix, when accompanied by a Relative Error Bound (REB) on the estimated parameters, provides the user with a tool that can better assess the usefulness of any least squares result. Another important aspect of the REB is that it provides the user of the SVD method with a powerful tool for judging which is the best among several candidate solutions. In addition, it provides the user with a numerically stable method of computing the data ordinarily provided by principal component regression by eliminating the need to perform an eigenvector-eigenvalue analysis of an ATA matrix. This is of particular interest because forming the ATA matrix is often accompanied by a loss of data. The new method also provides the user with an improved method for selection of which principal components should be retained for a given problem.
Copy-Move Forgery Detection and Localization Framework for Images Using Stationary Wavelet Transform and Hybrid Dilated Adaptive VGG16 with Optimization Strategy

Due to the availability of low-cost electronic devices and advanced image editing tools, changing the semantic meaning of a particular image has become straightforward by employing various image manipulation techniques like image copy-move, image splicing and removal operations. The tampered images with this sophisticated software are rich in visualization, making the modifications invisible to the naked eye. Detecting these image alterations is laborious, time-consuming, and often yields inappropriate results. The current techniques use conventional square, slide regular, and artifacts procedures to identify image deviations to combat image forgery practices. Still, these techniques exhibit problems related to generalization, training and testing, and model complexity. So, in this paper, a novel image forgery detection and localization framework is implemented using stationary wavelet transform (SWT), and a Hybrid Dilated Adaptive VGG16 model with optimization is introduced to classify forgery images and localize the forgery regions present in an image. Initially, the proposed framework processes the input image with SWT to decompose an image into different subband and further divide it into patches. After that, the hybrid dilated adaptive VGG16 Network (HDA-VGG16Net) is built to extract the deep image features from the patches. Later, the Hybridized Tuna Swarm with Bald Eagle Search Optimization (HTS-BESO) technique is applied to optimize the VGG16 parameters. Finally, feature matching is formed using multi-similarity searching to recognize whether the input image is forged or original by locating forgery regions. The evaluation results are compared with existing forgery detection approaches to ensure the efficiency of the developed model by considering multiple performance measures.
When Handcrafted Features Meet Deep Features: An Empirical Study on Component-Level Image Classification

Scene text detection from natural images has been a prime focus from last few decades. Classification of foreground object components is an essential task in many scene text detection approaches under uncontrollable environment. As it heavily relies upon robust and discriminating features, several features have been engineered for component-level text non-text classification. Competency of such feature descriptors particularly in respect of deep features needs to be examined. In this paper, we present prospective feature descriptors applicable to component-level text non-text classification and examine their performance along with convolutional neural network based deep features. Series of experiments have been carried out on publicly available benchmark dataset(s) of multi-script document-type, scene-type, and combined text vs. non-text components. Interestingly, feature combination is found to put well-demonstrated deep features into tough competition on most datasets under consideration. For instance, on the combined text non-text classification problem, CNN based deep features yield 97.6%, whereas aggregated features produce an accuracy of 98.4%. Similar findings are obtained on other experiments as well. Along with the quantitative figures, results have been analyzed and insightful discussion is made to ascertain the conjectures drawn herein. This study may cater the need of leveraging potentially strong handcrafted feature descriptors.
Non-Gaussianity detection of single-mode rotationally symmetric quantum states via cumulant method

Abstract The non-Gaussianity of quantum states incarnates an important resource for improving the performance of continuous-variable quantum information protocols. We propose a novel criterion of non-Gaussianity for single-mode rotationally symmetric quantum states via the squared Frobenius norm of higher-order cumulant matrix for the quadrature distribution function. As an application, we study the non-Gaussianities of three classes of single-mode symmetric non-Gaussian states: a mixture of vacuum and Fock states, single-photon added thermal states, and even/odd Schrödinger cat states. It is shown that such a criterion is a faithful and effective one for revealing non-Gaussianity. We further extend this criterion to two cases of symmetric multi-mode non-Gaussian states and non-symmetric single-mode non-Gaussian states, respectively.
Field-enriched A* search algorithm for robot motion in path planning

Path planning in unknown or complex environments is a central issue in the fields of automation and robotics. To address this global path planning problem while maintaining the smoothness of robot motion, as well as to ensure safety by keeping away from obstacles, this paper propose an innovative method called Field-Enriched A* Planner (FEAP), which incorporates the repulsive potential field into the heuristic computation of the A* search algorithm. The research modified the heuristic mechanism of the A* algorithm by incorporating both attraction and repulsion factors from the Artificial Potential Field method, thereby enhancing the influence of obstacles in the decision-making process. The repulsive potential field enhances obstacle avoidance, ensuring smoother robot motion during the path planning process. The simulation experiments are conducted on the proposed method and compare it with common path planning algorithms. Results from the experimental trials demonstrate that the new method can effectively enhance the smoothness of robot motion while ensuring a safe path is identified.
Prediction of CBR by Deep Artificial Neural Networks with Hyperparameter Optimization by Simulated Annealing

Abstract The construction of pavements requires the complete identification of the soils in place and of the added materials. This identification consists in determining the class of the soils and in evaluating their bearing capacity through the California bearing ratio (CBR) index. Obtaining the CBR index is very costly in terms of time and financial resources, especially when it is a large-scale project. It thus leaves prospects of obtaining it by simpler processes; hence, it arises the need to find simpler processes compared to classical processes. This study develops models for predicting the CBR index from physical properties that are less complex to obtain, based on deep neural networks. To achieve this, three databases were used. A first database consists of the proportion of fines, the Atterberg limits and the Proctor references of the soils. A second database uses the methylene blue value instead of the Atterberg limits, and a third database uses only the proportion of fines and the Proctor soil reference. On each of the databases, a deep neural network model was developed using dense layers, regularization layers, residual blocks and parallelization in TensorFlow to predict the CBR value. Each model was formed by combining several deep neural networks developed according to specific architectures. To expedite training, the simulated annealing method was employed to optimize hyperparameters and define the optimal configuration for each network. The predictions obtained are correlated with the true values from 83.6 to 96.5%. In terms of performance, the models have a mean deviation ranging from 3.74 to 5.96%, a maximum deviation ranging from 12.43 to 16.2% and a squared deviation ranging from 0.781 to 2.189. The results suggest that the variable VBS has a negative impact on the accuracy of the networks in predicting the CBR index. The developed models respect the confidence threshold (± 10%) and can be used to set up a local or regional geotechnical platform.
Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery

Cross-site request forgery (CSRF) vulnerabilities pose a significant threat to web application security, enabling attackers to execute unauthorized actions on behalf of authenticated users. Conventional CSRF detection methods, such as manual code review and static analysis, are often time-consuming, error-prone, and inefficient. Proposes Mitch, a novel machine learning (ML)-based solution for the black-box detection of CSRF vulnerabilities. Mitch employs supervised learning, trained on a comprehensive dataset of HTTP requests and responses, to effectively identify security-sensitive HTTP requests and uncover CSRF vulnerabilities within them. Rigorous evaluations on a diverse set of real-world web applications demonstrate Mitch's remarkable ability to detect CSRF vulnerabilities with high accuracy, outperforming traditional methods. Mitch's automated nature eliminates the need for manual code review and static analysis, saving time and effort while reducing the risk of human error. Additionally, Mitch's scalability allows seamless integration into continuous integration and continuous delivery (CI/CD) pipelines, enabling continuous security monitoring and vulnerability detection. Mitch's efficacy extends beyond detecting known CSRF vulnerabilities. Its ability to identify patterns and relationships enables it to uncover obscure CSRF vulnerabilities that may have been overlooked by traditional methods, including zero-day vulnerabilities. In conclusion, Mitch emerges as a powerful tool for enhancing web application security, offering a comprehensive and automated solution for detecting CSRF vulnerabilities. Its ability to handle complex web applications, uncover hidden CSRF vulnerabilities, and integrate into CI/CD pipelines makes it an indispensable tool for web security professionals. Mitch's adoption has the potential to significantly reduce the risk of CSRF attacks and safeguard sensitive user data. We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. In this project, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyses, due to their diversity and the widespread adoption of custom programming practices. ML is thus very helpful for web application security it can take advantage of manually labeled data to bring the human understanding of the web application semantics into automated analysis toolsMitch allowed us to identify 35 new CSRFs on 20 major websites and 3 new CSRFs on production software. Keywords: Mitch, CSRF, CI/CD pipelines, Security Token Service (STS), Same-Origin Policy (SOP).
On Time-based Exploration of LMS Data and Prediction of Student Performance

Abstract Learning Management Systems (LMS) gather extensive amounts of data about students' progression through courses. Such data is then made available via APIs for data exploration and utilization. A body of research has investigated using such data to predict student performance based on data collected earlier in a course. The driving question in such efforts has been whether student performance can be accurately predicted early enough to intervene and provide needed help. Two main issues have been pointed out in these attempts: the portability and robustness of these predictions. In this work-in-progress study, we introduce a new approach to exploring LMS data. Such an approach looks at the data as a set of time series each representing the progress of a student within a course. This study explores how students advance through courses over time and the variability of student performance between any two time points. It utilizes the LMS data (Canvas in this case) obtained from multiple Computer Science courses taught by different instructors in different formats (online and face-to-face) over three years in a public four-year university. Ways for summarizing and visualizing such data are discussed, and useful predictor features are extracted and used to build and evaluate predictive models of student performance at any time point. The study explores questions such as: How does the progress of passed and failed students differ in these courses? How early can student performance be accurately predicted? Can data collected from one course be used to predict the performance of students in another course by the same or a different instructor? Are student journeys through courses unique, or are there patterns that transcend students and courses?
Knowledge and Instance Mapping: architecture for premeditated interoperability of disparate data for materials

Abstract Predicting and elucidating the impacts of materials on human health and the environment is an unending task that has taken on special significance in the context of nanomaterials research over the last two decades. The properties of materials in environmental and physiological media are dynamic, reflecting the complex interactions between materials and these media. This dynamic behavior requires special consideration in the design of databases and data curation that allow for subsequent comparability and interrogation of the data from potentially diverse sources. We present two data processing methods that can be integrated into the experimental process to encourage pre-mediated interoperability of disparate material data: Knowledge Mapping and Instance Mapping. Originally developed as a framework for the NanoInformatics Knowledge Commons (NIKC) database, this architecture and associated methods can be used independently of the NIKC and applied across multiple subfields of nanotechnology and material science.
Lab Modules for Developing Practical Skills in a Python Scripting Course

Abstract To implement recommendations from our Industry Advisory Board, we switched from PHP to Python Scripting Language in the course Scripting for Information Technology. Python is easy to learn, easy to use and powerful. It is widely used in software development (Dev) and IT operations (Ops), artificial intelligence (AI), big data and cloud computing. Our students are eager to learn hands-on skills of using Python in real-world scenarios, which are not covered adequately in the textbook. In this project, we developed five supplemental lab modules and an online, virtual environment, which can be used by both face-to-face students and distance education students, to meet their increasing demand for hands-on exercises. The lab modules include 1) Python in Linux Automation (Ansible), 2) Using Python with NoSQL database system MongoDB, 3) Web Programming Environment with Python, 4) Using Python with Podman RESTful API and 5) Simulating an Internet-Connected Weather Sensor Using Python. A Canvas shell was created to host lab manuals, lab quizzes, and supporting material such as demo video and tutorial. The Linux-based virtual environment is portable and can be accessed by students anytime and anywhere to do labs. Some tasks in the labs are automatically graded on Canvas. Other tasks are manually graded. The lab modules were deployed in two sections taught by different instructors in spring 2022. An anonymous survey was conducted at the end of the semester to collect feedback from students. The instructors also provided their evaluations. We believe that the hands-on approach will help students better understand course topics and improve their practical skills of using Python in the real world.
Quantum Computing and Artificial Intelligence: Synergies and Challenges

Due to the explosive rise of quantum computing, there has been intense competition in business and academics in the field of quantum optics in recent decades. The current invention's overall scalability in quantum computing has surpassed many orders of magnitude, whereas ubiquitous quantum computers can support up to hundreds of quantum bits, or thousands of qubits. Strong machines continue to be developed. As a result, ethnicity has served as the inspiration for a huge number of studies and reports. This essay offers an introduction for everyone who would truly like to understand more about the ideas of quant communication and computing from a machine learning standpoint. It starts with such an educational approach and goes on to cover important turning points and the latest advancements in quantum computing. In this research, these fundamental characteristics of such a virtual network are divided into four major challenges, each of which has been thoroughly examined. correspondingly, A, B, C, and D stand for quantum physics, networking, security, and algorithms. The main issues, important areas of research, and most recent advancements are discussed as the article comes to a close.